Contemporary Sports Issues – Page 35

Is Controlling the Rushing or Passing Game the Key to NFL Victories?

Jeremy Arkes, Associate Professor of Economics, Graduate School of Business and Public Policy, Naval Postgraduate School

### Abstract

#### Purpose

To evaluate whether controlling the running game or the passing game contributes more to winning in the NFL.

#### Methods

This analysis uses regression analysis to dispel the myth that controlling the rushing game wins NFL games. Final-game rushing and passing statistics are endogenous because teams that are ahead will rush more in order to protect the ball and run the clock down. To address this issue, I use first-half statistics (essentially stripping the endogenous component from the statistics), with the justification that the halftime leader wins 78 percent of the time. The data are the 256 regular season games for 2005. I use logistic models to model the probability of winning a game based on differences in rushing success and passing success in the first half.

#### Results

I find that having a first-half passing-yard advantage increases the probability of winning, but having a first-half rushing-yard advantage has no significant effect.

#### Conclusions and Applications

The results suggest that the common belief that controlling the running game is the key to winning in the NFL may be a misguided belief. Coaches and teams may have greater success by focusing on the passing games, both offensively and defensively.

**Keywords:** Football, NFL, passing, rushing, coaching

### Introduction

A common assessment of the key to winning professional football games is to control the running game. And a very common statistic used to support this claim is that teams are much more likely to win if they have a 100-yard rusher. This is often used in recapping games and when analysts describe the keys to victory. For example, the recap of a 2005 victory for the St. Louis Rams over the Houston Texans indicated: “[Steven] Jackson finished with 25 carries for 110 yards, improving the Rams’ record when having a 100-yard rusher to 38-0 since moving to St. Louis in 1995” (1). This implies that rushing 100 yards was the catalyst for the victory. Likewise, many analysts say that establishing the running game is a key to victory. For example, one analyst argued that a key to winning for the Tampa Bay Buccaneers over the Oakland Raiders in Superbowl XXXVII was to “contain Raiders’ [running back] Charlie Garner,” citing evidence that: “In the past five seasons, the Bucs are 1-12 when opponents have a 100-yard rusher” (11). In a closely tied statistic to rushing dominance, analysts also argue that teams that control the time of possession are more likely to win. These assessments imply that controlling the passing game is of much less importance.

A set of articles on espn.com using data from the NFL’s 2003 and 2004 regular season games supports these contentions by arguing that preventing an opposing runner from gaining 100 yards and winning the time-of-possession battle increased a team’s chances of winning (5, 6). At the same time, these articles imply that the passing game is insignificant, citing as evidence:

1. Teams having a 100-yard rusher win 75 percent of games;
2. Teams winning the time of possession battles win 67 percent of games;
3. Having a 300-yard passer has no advantage, as those teams only win 46 percent of games.

A problem with these simple assessments is that teams that are winning will rush the ball more to run out the clock and reduce the chance of turnovers and will often wait until the clock runs down before starting a play. So, if a team is heading towards victory, they are likely to increase their rushing yards while boosting their time of possession. Likewise, a team that is behind will pass more for potentially higher-gaining plays and to preserve the clock. Thus, in statistical terms, we could say that rushing yards, passing yards, and time of possession are endogenous, or partly a product of the outcome rather than just a contributor to the outcome. This makes it difficult to attribute any advantages in rushing yards or time of possession to the winner as causal impacts. In fact, what happens in the first half or even the first quarter can dictate the outcome of the game, as teams leading after just the first quarter (in 2003 and 2004 games) won 75 percent of the time (5).

This paper presents an empirical test of these issues with econometric analysis. Primarily, this analysis tests whether controlling the rushing or passing game was more likely to contribute to a victory in NFL games in the 2005 season. Rushing and passing advantages represent efficiency both on offense and defense. In addition, the model examines the relative contribution of turnovers, penalties, and sacks allowed to the probability of winning. These models could represent a more accurate picture of the effects of certain factors on winning, as they hold other factors constant.

The twist in this analysis is that the model corrects for endogeneity by using the first-half statistics. This essentially strips a large portion of the endogenous component from these statistics, as teams are not likely to change strategies to “ball preservation” or “speedy catch up” until the second half. Given that 78.5 percent of teams leading at halftime in 2005 games ended up winning the game, having a halftime advantage in many of these statistics should contribute to a higher probability of winning.

Determining the key contributions to winning in the NFL is important as teams, subject to the college draft and salary caps, attempt to obtain the best allocation of talent among various positions. If it does turn out that big passing games are the keys to victory, then investing relatively more on players in passing-related offensive and defensive positions than on players in rushing-related positions may be wiser.

Research on football issues has been very limited in the academic literature. There have been some interesting analyses on optimal 4th-down strategies (8, 9). Some research has attempted to predict the outcome of a game based on betting markets or power scores (4, 10). Other research has examined the success of teams over the course of a season (2, 3, 12). However, to my knowledge, this is the first analysis attempting to predict outcomes of games in a multivariate framework based on in-game statistics.

The most similar prior research examined how certain factors contributed to the number of wins NFL teams had (7). This article examined how first downs, average rushing yards per carry and passing yards per completion, interceptions, fumbles, and other factors affected the number of wins a team had, and then used the results to judge coaching efficiency. The models use full-game statistics so the results are subject to the biases mentioned above.

In this study, I first present a simple breakdown of the descriptive statistics for the first and second half, which clearly demonstrates the likely existence of endogeneity using the full-game statistics, as the eventual winner or the half-time leader clearly changes strategy in run-pass mix in the second half. The stark contrast found between the models using full-game vs. first-half statistics further corroborates how endogeneity affects the models using the full-game statistics. In particular, while the models using full-game statistics show a connection between controlling the rushing game and the probability of winning and no connection for controlling the passing game, the models using first-half statistics show the opposite: that controlling the passing game matters, but controlling the running game does not. Given that the analyses based on the first-half statistics should be free of biases from endogeneity, it appears that controlling the passing game is the key to winning. In addition, both full-game and first-half models show that the time of possession has no effect on the probability of winning, after controlling for other factors.

### Methods

#### Data

The sample includes all 256 regular season games from the 2005 NFL season. Each of the 32 teams has 16 games in the sample. The data come from the “Gamebooks” that are available on the NFL’s website (nfl.com). These data were used with permission granted from the National Football League’s Licensing Office. The advantage of these data is that they provide both final and first-half statistics, while a disadvantage is that the relevant statistics need to be manually extracted from each game report, which is roughly 10 pages long for each game. The descriptive statistics are presented in the Appendix. Table A1 shows the average team-level first-half and full-game statistics for the 512 team-game observations. Table A2 shows the average game-level statistics used in the econometric models for the 256 regular season games, with the key variables being “moderate” and “great” control of the rushing and passing games.

What is useful to show here are the differences that exist between first-half and second-half statistics for the eventual winners versus the losers and for the first-half leaders versus the trailers. These demonstrate how the second-half strategies can be dictated by first-half success, which is the basis for the argument that full-game statistics are endogenous to the outcome. Table 1 shows these results for the 243 games that did not go into overtime, as the second-half statistics cannot be calculated for the 13 games going into overtime because of how the NFL Gamebooks are set up. The first two columns, based on which team wins the game, show that, whereas the winner had an average of a 22-passing-yard advantage in the first half (119 versus 97), it had 26 fewer yards passing than the loser in the second half.

The next set of columns makes the comparison based on which team had the lead at halftime. There were 227 games in which one team led at halftime and the game did not go into overtime. Table 1 shows that there was little difference between the first half and second half in the rushing advantage for the halftime leader. However, that difference for the passing advantage is much greater. The halftime leader had a 34-yard passing advantage (126 vs. 92) in the first half and a 39-yard passing disadvantage (85 vs. 124) in second-half passing yards. Furthermore, the advantage for the leader in terms of fewer sacks allowed increased from 0.43 to 0.76. The differences are even starker in the final two columns for the 141 games that had a team leading by 7 or more at halftime. The 49-yard first-half passing advantage for the leader turned to a 50-yard disadvantage in the second half. And the 0.49 first-half advantage for the leader in fewer sacks allowed turned to a 0.90 second-half advantage. Note that the lack of much difference between the leader and trailer in first-half versus second-half rushing yards does not indicate that strategy does not shift, as the ratio of passing-to-rushing yards does increase for the trailer and decrease for the leader.

These results offer strong statistical evidence that the halftime leader passes less (probably to help protect the ball and run the clock down) and is more careful with the ball (with fewer turnovers). In addition, the results indicate that the halftime trailer passes more. The implication for statistical analysis is that many full-game statistics are likely endogenous to the eventual outcome. This includes rushing yards, passing yards, turnovers, and the number of sacks allowed. Thus, any comparison of full-game or final statistics for the winner versus the loser would be biased indicators of a causal effect.

#### Econometric Models

Given the likely bias that would come from using full-game statistics, the primary model will use first-half statistics, while still basing the outcome on the eventual game winner. As mentioned above, the justification for this is that 78.5 percent of the teams that led at halftime ended up winning the game. In order to provide a comparison so that readers can gauge the level of bias in using full-game statistics, an initial set of models will show the results from models using the full-game statistics.

The econometric model is the following:

![Formula 1](/files/volume-14/5/formula.png “Formula 1”)

where Y_i, the dependent variable, is a dichotomous indicator for whether the home team won game i, R_i and P_i represent measures of the rushing and passing advantages of the home team relative to the visiting team, Xi is a vector of three other statistics for the home team relative to the visiting team, including penalty yards, turnovers, and sacks allowed, and Hi and Ai are vectors of 31 indicator variables for which team is the home team and away team in game i, with one team excluded. Thus, all statistical variables are created in terms of the home-team statistic minus the visiting-team’s statistic or, in a few cases, the advantage of the home team over the away team. For example, the variable for rushing-yards advantage would be the number of rushing yards for the home team minus the number of rushing yards for the visiting team. The results would be the same regardless of whether the model predicts the probability of the home team or the visiting team winning.

For both sets of models with final statistics and first-half statistics, three sets of rushing and passing variables are created. The first set has the raw difference in rushing and passing yards, measured as the advantages the home team has over the visiting team. The second set has a variable indicating whether one of the teams had “moderate” control of the rushing or passing yards, with the threshold being 25 yards for the models with first-half statistics and 50 yards for models with full-game statistics. For the models with first-half statistics, this variable is coded as “1” if the home team had at least 25 more rushing (or passing) yards than the visiting team at halftime, “-1” if the visiting team had at least 25 more yards than the home team, and “0” if the absolute difference in yards between the two teams was less than 25. The third set of variables, constructed similarly to the second set, has variables indicating whether one of the teams had “great” control of the rushing or passing game. The thresholds are 50 yards for the models with first-half statistics and 100 yards for models with full-game statistics. Note that these variables taking on the values of (-1, 0, 1) essentially constrains the absolute values of the following two effects to be the same: (a) the effect of home-team control of the rushing/passing game on the probability of the home team winning and (b) the negative of the effect of visiting-team control of the rushing/passing game on the probability of the home team winning. This helps to give greater power to the model.

The models include three other statistical variables: the difference in penalty yards, the difference in turnovers, and the difference in the number of times the team is sacked. Including the number of penalties had a very small effect, so it was excluded so that the full effect of penalty yards could be estimated.

Finally, the model includes team fixed effects for both being the home team and being the visitor. That is, it includes 31 dummy variables for the home team and 31 dummy variables for the away team, excluding one team as the reference category. They account for differences in team-specific factors, such as the quality of coaching and the strength of home-field advantage (e.g., from fan enthusiasm and weather conditions). In addition, the team fixed effects account for differences in the strength and weakness of the passing vs. rushing games for teams and for opponents.

These team fixed effects are included to help avoid unobserved team heterogeneity affecting the results. For example, one of the better teams in 2005 was the Indianapolis Colts, which had a very strong passing game. Thus, without team fixed effects, the general success of the Colts could contribute to a positive correlation between passing yards and winning that could be due to other unobserved factors. By including team fixed effects, the estimates represent within-team variation across games in winning attributable to within-team variation across games in control of the rushing and passing game. The coefficients on these (not reported) generally reflect differences across teams in both home and away winning percentages, after taking into account the other variables included in the model.

Equation (1) is estimated with logit models. The models have a final sample of 212 games because 44 games were dropped by the model due to perfect prediction of the outcome—e.g., 8 observations were dropped for Seattle home games because they won all those games. In estimation, it turned out that that the marginal effects were highly dependent on the home and visiting teams used for the prediction. Some teams that won (or lost) nearly all their home or away games were too close to a predicted probability of winning of one (or zero), so that the marginal effect of the variables would be close to zero for them. To correct for this, the reported marginal effects are calculated as the averages for all team combinations that played in the 2005 season.

The model presented here is fairly simple. One reason for this is that the home- and away-team fixed effects account for a wide set of team-specific factors (some unobservable and some observable), such as the quality of coaching, having artificial turf, and generally favoring either passing or rushing. The other reason why the model is kept simple is that it is designed to estimate the full effects of having advantages in the rushing game and the passing game. As it turns out, this simple model tells an interesting story.

The model could be made more complex by including such factors as the run-pass mix, time-of-possession, and return yards off of kick-offs and punts. These other factors are excluded because they could themselves be products of running and passing success in the game. For example, having a higher time-of-possession is an indicator of rushing the ball successfully. And, having a rush-pass mix favoring passing may be an indicator of success in the passing game. Controlling for these variables would cause the model to factor out part of why having rushing or passing advantages helps win games, so that the coefficient estimates on the rushing and passing advantages would represent partial effects rather than the full effects the model aims to estimate. Separate analyses below do test whether time-of-possession matters, after controlling for rushing and passing yards, as well as the other factors that are in our primary set of models.

Another factor excluded from the model for similar reasons is the number of return yards from kick-offs and punts. Return-yard success (or more generally, special-teams success) could be representative of other factors. Indeed, one of the ESPN articles notes that teams returning a punt or kick-off for a TD win only 42 percent of the time (6). One confounding factor is that teams have a greater chance of return success on kick-offs than on punts, but having more kick-off returns is an indication that the other team has scored more often. Given these complexities, we exclude return-yardage indications. Given that we use team fixed effects, this should not be a problem to our analysis, as within-team variation in special-teams success relative to the other team (holding constant special-teams’ opportunities) should be mostly uncorrelated with the within-team variation in rushing and passing success relative to the other team.

### Results and Discussion

#### Is controlling the rushing or passing game more important to winning?

Table 2 presents the results of the econometric models that examine the relationship between full-game statistics and the probability of winning. These results are subject to biases created by the endogeneity described above, so they are meant to be compared to the results of the preferred model, in Table 3, which is based on the relationship between first-half statistics and the probability of winning.

The results in Table 2 are consistent with the widely held belief that controlling the rushing game is the key to winning and that great passing success is not important. The coefficient estimate on the rushing-yard difference is positive and significant at the one-percent level. The corresponding marginal effect, in brackets, indicates that each 10-yard advantage in rushing yards is associated with a 2.3-percentage-points higher probability of winning (p < 0.01). The coefficient estimate on passing-yards advantage is small and insignificant. Considering the indicators for “moderate” control of the rushing and passing game, having a 50-yard advantage in rushing yards is associated with an estimated 17.2-percentage-points higher probability of winning (p < 0.01). The estimate on having a 50-yard advantage in passing yards is again insignificant. Having “great” control of the rushing game (100-yard advantage) is associated with an estimated 31.4-percentage-points higher probability of winning (p < 0.01). Having “great” control of the passing game is still statistically insignificant.

As for other results, each turnover is associated with a decrease in the probability of winning of about 16 percentage points (p < 0.01), while each sack is associated with an 11-percentage-points decrease in the probability of winning (p < 0.01). These seemingly large effects could be indicative of the extra chances that teams take when they are behind late in the game. Penalty yards do not appear to make a difference, after controlling for other factors.

The main point from the models using full-game statistics is that total rushing yards or controlling the rushing game is positively correlated with the probability of winning, while passing yards and controlling the passing game has little correlation with the probability of winning.

The results from models using first-half statistics give the opposite conclusion. The estimates indicate that controlling the passing game is the key to winning, not controlling the rushing game. In contrast to the results in Table 2, those in Table 3, for the coefficient estimates on first-half statistics, arguably represent causal effects because most teams probably do not start the strategy of protecting the ball and running out the clock to end the game while still in the first half.

All three of the coefficient estimates on the passing yard advantage are positive and significant (p < 0.01). The estimates on rushing yard advantage are still positive, but smaller than those for the passing-yard advantage and statistically insignificant. The estimated marginal effects indicate that each 10 yards of passing gained increase the probability of winning by 2.6 percentage points, while having a 25- or 50-yard-passing advantage in the first half increases the probability of winning by about 21 percentage points. Thus, these estimates indicate that controlling the passing game in the first half increases a team’s probability of winning the game by about 12 percentage points, while controlling the rushing game in the first half has no significant effect on the probability of winning.

Among the other factors, first-half penalty yards again do not affect the probability of winning. Each turnover is estimated to reduce the chance of winning by about 10 percentage points (p < 0.01), while each sack allowed reduced the probability of winning by about 5 percentage points (p < 0.10). The estimated marginal effects of turnovers and sacks allowed are smaller for the first-half model than for the full-game model. This could indicate that, like rushing and passing yards, the full-game statistics on the number of turnovers and sacks allowed are endogenous and reflective of the outcome of the game, as the teams that are behind will be susceptible to more turnovers and sacks as they pass more and take more chances to try to catch up.

#### Does time of possession matter?

Another commonly-held belief is that having a greater time-of-possession is a major key to winning, as 67 percent of the teams that won the time-of-possession battle in 2003 and 2004 had won their games(5). This suggests that winning the time-of-possession battle increases a team’s chances of winning by about one-third. However, this statistic is also a product of a team’s success (or endogenous) and thus subject to biases. For example, teams that are ahead will let the clock run down further between plays.

Table 4 presents the coefficient estimates on variables representing time of possession from models similar to column (1) in Tables 2 and 3—i.e., models that use the rushing- and passing-yard advantage. It includes estimates using the full-game and first-half statistics. The first row has the estimates on the actual time-of-possession advantage; the second row has the estimates on indicators for whether the team had a higher time-of-possession, and the last two rows have estimates on indicators for having advantages of 7 minutes (for the full game) and 5 minutes (for the first half), which are roughly the average mean absolute differences. For the full-game statistics, none of the time-of-possession variables is statistically significant. For the models based on first-half statistics, all of the coefficient estimates on time of possession are negative, with the first one being statistically significant (p < 0.10). These results suggest that time-of-possession is not important to winning, holding constant other factors.

### Conclusions

This paper is the first analysis to model a production function for winning an NFL game based on in-game statistics. This carefully constructed framework, which models victories based on home-team over away-team statistics, can be used for other models for winning games in the NFL or in other sports leagues.

The results of this analysis cast doubt on the contention that the key to winning games in the NFL is to control the rushing game. The results do indicate that having a rushing advantage for the full game is positively correlated with the probability of winning and having a passing advantage for the full game is not correlated with winning, holding other factors constant. However, these correlations are likely due to endogeneity, in that full-game rushing and passing yards are partly products of a team’s success during the game. In other words, as demonstrated in this paper, the strategy for second-half rushing-passing mix depends on where a team stands at halftime. This means that we cannot label these correlations as causal influences.

The econometric strategy in this analysis is to identify a causal effect of various factors by using first-half statistics. These first-half statistics should be exogenous because strategies to run the clock down and to take extra precautions of preserving the ball (and to play catch-up by passing the ball so that incompletions stop the clock) arguably do not start until sometime in the second half. Of course, there could be cases in which teams build such a huge lead early in the first half that they start such a strategy at some point in the second quarter. But, typically teams that are ahead would want to build on their momentum in the first half before shifting strategy at some point in the second half.

One other key result is that having a time-of-possession advantage does not matter, after controlling for other factors (e.g., rushing and passing yards). However, the major findings from models using first-half statistics are that, on average, controlling the passing game contributes significantly to the probability of winning and controlling the rushing game has little impact. Having some level of control over the passing game in the first half is estimated to increase a team’s chance of winning by 21 percentage points. It is not that rushing success does not matter, as many would argue that having the threat of a potent running attack is key to a successful passing game. In addition, a strong running game may help with ball preservation for holding a second-half lead. But, in contrast to conventional thought, holding other things constant, it appears that a big passing day is more important to victory than a big running game. It is important to keep in mind here that passing advantage and control incorporates both how strong a team’s passing game is and how strong its pass defense is.

### Applications in Sport

The results in this analysis suggest that NFL coaches may be more successful if they were to place more emphasis on the passing game than on the running game. This result may translate to lower levels of football (e.g., high school and college). In this case, for professional football or something lower, obtaining and developing premier players for passing-related offensive and defensive positions may be more important than obtaining and developing premier players in rushing-related positions.

### Tables

#### Table 1
A comparison of first-half and second-half statistics for the eventual winner versus the loser and the halftime leader versus the trailer.

	Based on eventual outcome (N=243)		Based on which team leads at halftime (N=227)		Based on which team had 7+ point lead at halftime (N=141)
	Winner	Loser	Led at halftime	Trailed at halftime	Led by 7+ points at halftime	Trailed by 7+ points at halftime
1st-half rushing yards	64	50	66	47	70	42
2nd-half rushing yards	71	39	67	42	71	40
1st-half passing yards	120	98	126	92	136	87
2nd-half passing yards	92	118	85	124	76	126
1st-half penalty yards	28	32	27	32	27	32
2nd-half penalty yards	27	29	28	28	28	28
1st-half turnovers yards	0.65	0.99	0.56	1.04	0.55	1.20
2nd-half turnovers yards	0.49	1.38	0.68	1.22	0.62	1.26
1st-half sacks allowed	0.88	1.26	0.85	1.28	0.87	1.36
2nd-half sacks allowed	0.72	1.68	0.81	1.57	0.73	1.63

**NOTE:** These statistics exclude the 13 games that go into overtime because second-half
statistics cannot be determined.

#### Table 2
Logistic regression model for the relationship between full-game statistics and the probability of winning (N=212)

	(1) Using rushing and passing yards difference	(2) Using “moderate” control of rushing and passing game	(3) Using “great” control of rushing and passing game
Rushing yards difference	0.0266^*** (0.0078) [0.0023]
Passing yards difference	0.0064 (0.0065) [0.0006]
Had 50-yard rushing advantage		2.081^*** (0.690) [0.172]
Had 50-yard passing advantage		0.485 (0.743) [0.040]
Had 100-yard rushing advantage			3.918^*** (1.290) [0.314]
Had 100-yard passing advantage			1.375 (0.018) [0.110]
Penalty yards difference	-0.023 (0.019) [-0.002]	-0.025 (0.020) [-0.002]	-0.020 (0.018) [-0.002]
Turnover difference	-1.790^*** (0.440) [-0.158]	-1.860^*** (0.440) [-0.153]	-2.045^*** (0.495) [-0.164]
# sacks allowed difference	-1.268^*** (0.375) [-0.112]	-1.313^*** (0.381) [-0.108]	-1.211^*** (0.366) [-0.097]

**NOTE:** *, **, and *** indicate statistical significance at the five- and one-percent level. The models also include dummy variables for each visiting team and home team. Standard errors are in parentheses and marginal effects are in brackets.

#### Table 3
Logistic regression model for the relationship between first-half statistics and the probability of winning (N=212)

	(1) Using rushing and passing yards difference	(2) Using “moderate” control of rushing and passing game	(3) Using “great” control of rushing and passing game
Rushing yards difference	0.0090 (0.0060) [0.0013]
Passing yards difference	0.0177^*** (0.0049) [0.0026]
Had 25-yard rushing advantage		0.155 (0.366) [0.209]
Had 25-yard passing advantage		1.628^*** (0.394) [0.020]
Had 50-yard rushing advantage			0.781 (0.495) [0.103]
Had 50-yard passing advantage			1.648^*** (0.449) [0.216]
Penalty yards difference	-0.010 (0.007) [-0.001]	-0.010 (0.007) [-0.001]	-0.007 (0.007) [-0.001]
Turnover difference	-0.724^*** (0.251) [0.105]	-0.841^*** (0.254) [-0.108]	-0.739^*** (0.252) [0.097]
# sacks allowed difference	-0.352^* (0.183) [-0.051]	-0.357^* (0.184) [-0.046]	-0.416^** (0.183) [-0.055]

#### Table 4
Logistic regression model for coefficient estimates on time-of-possession variables

	Full-game	First-half
Time-of-possession difference	0.052 (0.107) [0.002]	-0.126^* (0.073) [0.017]
Had any advantage in time-of-possession	0.288 (0.670) [0.024]	-0.427 (0.350) [-0.057]
Had 5+ minute advantage in time-of-possession		-0.698 (0.583) [-0.039]
Had 7+ minute advantage in time-of-possession	0.448 (1.132) [0.037]

NOTE: *, **, and *** indicate statistical significance at the ten-, five- and one-percent level. The models also include dummy variables for each visiting team and home team. Each coefficient estimate is based on a separate regression. These regressions include, for either full-game and first-half statistics, the same regressors represented in column (1) of Tables 2 and 3. Standard errors are in parentheses and marginal effects are in brackets.

#### Table A.1.
Average team statistics in key categories for 2005 regular season games (N=512)

	Full half	Final game
Rushing yards	56.8 (30.3)	112.5 (51.1)
Passing yards	108.1 (51.0)	219.9 (73.7)
Penalty yards	30.2 (22.8)	58.2 (26.0)
Number of turnovers	0.81 (0.88)	1.76 (1.45)
Number of sacks allowed	1.08 (1.06)	2.30 (1.73)

NOTE: Standard deviations are in parentheses. The final-game statistics include 13 overtimes (or 26 observations), all of which lasted less than the full 15 minutes allowed. Thus, the differences do not exactly represent second half statistics.

#### Table A.2.
Average game statistics in key categories for 2005 regular season games (N=256)

	Percent of games with one team having indicated advantage in yards	Mean absolute value of difference (with standard deviation in parentheses)
		First-half	Full-game
Moderate Control of rushing and passing game
First-half advantage of 25 rushing yards	54.7%
First-half advantage of 25 passing yards	71.1%
Full-game advantage of 50 rushing yards	53.1%
Full-game advantage of 50 passing yards	62.5%
Great Control of rushing and passing game
First-half advantage of 50 rushing yards	29.3%
First-half advantage of 50 passing yards	49.2%
Full-game advantage of 100 rushing yards	20.3%
Full-game advantage of 100 passing yards	33.6%
Mean (standard deviation) of absolute value of differences
Rushing yards		37.0 (30.3)	65.5 (50.1)
Passing yards		58.8 (46.8)	80.3 (58.5)
Penalty yards		19.8 (24.3)	27.3 (21.4)
Turnovers		0.86 (0.80)	1.59 (1.42)
# sacks allowed		1.20 (1.06)	2.08 (1.70)

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Garber, G. (2005a). Turnovers, early deficits lead to losses. <http://sports.espn.go.com/nfl/news/story?id=2241121>, 2005a, accessed December 2, 2005.

Garber, G. (2005b). Penalties hurt but aren’t indicator of failure. <http://sports.espn.go.com/nfl/news/story?id=2241159>, 2005b, accessed December 2, 2005.

Hadley, L., Poitras, M., Ruggiero, J., and Knowles, S. (2000). Performance Evaluation of National Football League Teams. Managerial and Decision Economics, 2000, 21, 63-70.

Kreider, B. (2006). To Punt or Not to Punt. The UMAP Journal, 17, 353-63.

Romer, D. (2006). Do Firms Maximize? Evidence from Professional Football. The Journal of Political Economy, 114, 340-65.

Song, C, Boulier, B. L., and Stekler, H. O. (2007). The comparative accuracy of judgmental and model forecasts of American football games. International Journal of Forecasting, 23, 405–13.

Stroud, R. (2003). Keys to Victory, St. Petersburg Times, January 26, 2003, <http://www.sptimes.com/2003/01/26/Bucs/Keys_to_victory.2.shtml>, accessed August 25, 2006.

Terry, N. (2007). Investing in NFL Prospects: Factors Influencing Team Winning Percentage. International Advances in Economic Research 13, 117.

### Corresponding Author

**Jeremy Arkes**
Associate Professor of Economics
Graduate School of Business and Public Policy
Naval Postgraduate School
555 Dyer Rd.
Monterey, CA 93943
<arkes@nps.edu>
831-656-2646

### Author Biography

Dr. Jeremy Arkes is an Associate Professor of Economics in the Graduate School of Business and Public Policy at the Naval Postgraduate School.

U.S. Sports Academy2013-11-25T16:35:43-06:00February 2nd, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off

Factors that Influence African-American Millennials to Purchase Athletic Shoes

Dr. Robert Lyons Jr., Johnson C. Smith University; Dr. E. Newton Jackson Jr., University of North Florida; Dr. Annette Singleton-Jackson, Florida A & M University

### Abstract

The purpose of the study was to determine which factors greatly influenced African-American millennials to purchase athletic shoes. A sample of (n=101) African-American millennials participated in the study. The participants rated the following seven purchasing factors in order of importance using a Likert scale from one (“strongly disagree”) to five (“strongly agree”). The seven factors were athlete endorsement, brand name, color of shoe, comfort level, cost, style of shoe, and quality. The results indicated that athletic shoe style, color and cost were determining factors among the participants when purchasing athletic shoes. T-test for unequal sample sizes indicated that there were significant differences as it related to males’ and females’ purchasing preferences. This study supports previous research findings on African-American youth purchasing behavior. Moreover, athletic shoe marketers should use this information as a means to understand the purchasing behavior of African-American millenials and to design marketing strategies to better reach this target audience.

### Introduction

African-American buying power has increased by 187 percent since 1990 (5). African-American buying power rose from $318 billion in 1990 to $590 billion in 2000, to $845 billion in 2007, and it is projected to increase to $1.1 trillion by 2012 (18). The buying power increase has been a result of African-American upward mobility (3). This increased buying power has afforded African-Americans from all generations the opportunity to purchase more goods and services, particularly African-American millennials. In general, millennials are those individuals born from 1980 to 1995, they are technologically savvy, very tolerant when it comes to sexual orientation, religion, and politics to name a few. Moreover, millennials are characterized by their independent nature, optimism, propensity to question the status quo, self-expression, and financial acumen (2,13,19). In contrast, generation x individuals (generally those born between 1964 and 1980) are characterized as pragmatic, self-reliant, less accepting of other viewpoints, and multi-taskers (17). Again, African-American millennial purchasing clout and influence is unparalleled, as witnessed by the following statement in the African-American/Black Market Profile report(8):

> Today’s African-American teen market (12- to 19-year-olds) are consumers and creators of trends, strong influencers of household purchases and a valuable target for advertisers. The same holds true for African-American/Black teens, who have a major impact on today’s mainstream culture—especially in music, sports and fashion. African-American/Black teens spend an average of $96 dollars monthly, 20% more per month than the average U.S. teen. In addition, when compared to all U.S. teens, male and female African-American/Black teens spend more yearly on items such as apparel and technology-related products and athletic shoes. (p. 11)

What’s more, the African-American/Black Market Profile report indicated that African-

American millennials have more brand loyalty to a variety of goods, including personal

products, food and footwear. Specifically, African-American millennial males exert more influence on household athletic shoe purchasing decisions and they are more brand loyal than other racial segments of millennials when it comes to purchasing athletic shoes (10). This trend in purchasing visible goods (such as athletic shoes) will continue as the African-American millennials continue to exert more influence on household purchases and as they continue to enter the workforce and earn wages (4).

In regard to the sport industry, athletic footwear is a thriving and lucrative business. According to the National Sporting Goods Association (2009), athletic shoe sales reached $17.1 billion for 2009 (12). Furthermore, of the 2.3 billion pairs of footwear purchased in the United States in 2007, Americans purchased 334 million pairs of athletic (1). African-Americans spent $391 per consumer unit on athletic footwear in 2006. This was more than any other race that year (5). Thus, the propensity that African-Americans have toward purchasing athletic shoes along with their loyalty to brands makes this population one worth investigating to determine their athletic shoe purchasing preferences.

There have been very few empirical studies dedicated to understanding the athletic shoe purchasing behaviors of youth and there is a dearth of information on the factors that influence African-American millennials to purchase athletic shoes. It is the intent of this study to add to the existing body of knowledge. The purpose of this study was to determine and identify the most important factors that influence African-American millennials to purchase athletic shoes.

### Methods

#### Procedures

The study was carried out in the summer of 2009 at a small historically black university in the southeastern United States. The researchers randomly selected a course time block to disseminate the questionnaire. This practice was initiated to prevent the same student from completing the questionnaire at one course time period and then attempting to complete during another course time period. The 11:30 am course time block was randomly selected. The researchers contacted all of the professors that taught a class during the time block via email to ask permission to disseminate the questionnaire. Professors were also informed that the researchers had received permission from the university’s institutional review board to conduct the study utilizing responses from university students, and that the questionnaire would take their students approximately ten minutes to complete. Thirteen professors offered courses at the 11:30 am time period. Of the thirteen, eight professors agreed to have their students complete the questionnaire.

#### Instrument

The researchers utilized a modified version of the Lyons and Jackson Athletic Shoe Survey. A ten item questionnaire was used to elicit responses from the participants. The questionnaire contained three demographic questions pertaining to the participant’s age, gender and race. In addition, seven questions addressing athletic shoe purchasing factors were included. The participants were asked to rate each factor on a Likert scale from one to five with one being strongly disagree and five being strongly agree.

#### Participants

Participants for this study were African-American millennials (n=101) between the ages of 18 and 24. All of the participants attended a historically black university in the southeastern United States. Of the participants, 52 (46.8%) were male and 59 (53.2%) were female.

#### Statistical Analysis

Descriptive statistics such as percentages, frequencies, and means were utilized to analyze data. Moreover, the researchers employed inferential statistics to further analyze data. The researchers used the t-test for independent unequal sample sizes. Specifically, the t-test for independent unequal sample sizes was employed to determine if there were significant differences between the purchasing factor mean scores of males and females.

### Results

Results from the study produced the following information regarding athletic shoe purchasing factors of African-American millennials. Group mean scores for both males and females revealed that style of shoe, comfort, color and quality were the most influential purchasing factors (Table 1).

For females style (M = 4.31), comfort (M = 4.14) and color (M = 4.03) were the most important factors (Table 2).

Style (M=4.63), quality (M=4.19), color (4.10) and brand (4.08) were the most influential factors for African-American males (Table 3).

T-test results revealed that there were no significant differences between males and females on each of the factors at the .05 level. To this end, there is indication that African-American males and females (in this study) have similar buying behaviors in that they valued each of the study factors somewhat equally (Table 4).

In terms of mean scores, athlete endorsement was the least influential factor for both males and females. Moreover, the cost factor did not rank highly for either group. In addition, the researchers considered the number of strongly agree and agree responses for each factor. Ninety-one percent of the participants indicated that they either strongly agreed or agreed that style was a crucial factor in purchasing athletic shoes. This factor was followed by comfort (76%), color (75%), quality (75%), brand (72%), cost (61%) and athlete endorsement (36%).

### Discussion

It became very apparent that style of shoe was the most dominating factor when deciding whether to purchase athletic shoes. The style factor mean score for males in this study was 4.63 and 4.31 for females. This finding is consistent with the findings from previous athletic shoe purchasing studies (16,20). It confirms to an extent that when African American youth are purchasing athletic shoes they focus primarily on the look of the shoe. Perhaps, as has been suggested, wearing a shoe that looks good makes one feel good. Better yet, the style of shoe may convey a form of status. Lyons and Jackson’s (2001) study on factors that influence African-American gen Xers to purchase athletic shoes also found that style was the most influential factor(7). This finding mirrored the responses of African-American millennials studied in this investigation, suggesting that the style phenomenon may be passed from generation to generation via cultural communication methods within the African-American community. It could also suggest that athletic shoe companies should continue to effectively communicate style as an influential feature among the African-American community.

Even though style was the predominant factor, other factors were influential as well. In regard to females, color and comfort ranked high, with mean scores of 4.14 and 4.03 respectively. For males, quality, color and brand name received mean scores of 4.19, 4.10 and 4.08 respectively, suggesting that African-American millennials are considering a specific set of factors that influence their purchasing decisions, based on their knowledge and experience with the athletic shoe. This knowledge and experience may be derived from the fact that African-American millennials may have purchased athletic shoes before and or they may have received information about the shoe via commercials, friends or other sources.

Athlete endorsement was rated the least influential purchasing factor in this study. Again, this finding is consistent with Lyons and Jackson’s 2001 study on African-American generation Xers(7). Both males and females rated athlete endorsement the least influential purchasing factor. This is surprising when one considers the enormous amount of money that athletic shoe companies spend to have athletes endorse their shoes. Nike spent close to three billion dollars in endorsements and sponsorship deals in 2007 with players like Michael Jordan and Tiger Woods receiving over twenty million dollars each (6). Perhaps athlete endorsement creates awareness for the shoe and even evokes some sort of emotion that causes a person to become loyal, curious and attached to the shoe brand. However, Martin stated that “the image of sport, independent of the athlete, can contribute significantly to the consumer’s response to an endorsement. The image of the sport can enhance, or detract from, the effects of the personality and appearance of the athlete making the endorsement” (9). In light of this statement, perhaps the respondents in this study held negative views of athlete endorsers and or their particular sport. Still, based on findings from this study, when an African-American millennial decides to make a purchase the athlete endorser does not figure prominently into the purchasing equation.

### Sport Marketing Implications

Based on the results of this study, athletic shoe sport marketers should be cognizant in crafting media messages that focus on style, color, and comfort. Moreover, athletic shoe retailers should develop in-store sales techniques that sales people can use to highlight shoe comfort, style and the importance of shoe color scheme when encountering African-American millennial customers. Marketing products and services are extremely important to the survival of many sport companies and franchises (11). Effectively marketing sport products and services can translate in to increased revenue for sport entities if they understand the needs and wants of their target audience (15).

### Recommendations

Based on the findings of this study, the researchers recommend the following:

– that a larger sample size be utilized to solidify and strengthen results;
– that studies comparing the purchasing behaviors of African-American and non-African-Americans should be conducted to determine if there are cultural and racial differences; and
– that athletic shoe studies comparing the purchasing behaviors of African-American generation-Xers and millennials be conducted to determine generational differences.

### Tables

#### Table 1
Factor Group Mean Scores

Factor	Group Means
Athlete Endorsement	2.83
Brand	3.98
Color	4.06
Comfort	4.06
Cost	3.50
Quality	4.01
Style	4.46

#### Table 2
Mean scores for African-American Millennial Females

Factor	Means
Athlete Endorsement	2.92
Brand	3.88
Color	4.03
Comfort	4.14
Cost	3.63
Quality	3.85
Style	4.31
Style	4.31

#### Table 3

Factor	Means
Athlete Endorsement	2.73
Brand	4.08
Color	4.10
Comfort	3.98
Cost	3.41
Quality	4.19
Style	4.63

#### Table 4

Factor	Males	Females	p-values (p > 0.05)
Athlete Endorsement	2.73	2.92	0.167
Brand	4.08	3.88	0.423
Color	4.10	4.03	0.252
Comfort	3.98	4.14	0.102
Cost	3.41	3.63	0.251
Quality	4.19	3.85	0.256
Style	4.63	4.31	0.256

### References

1. American Apparel and Footwear Association, (2008). Shoe stats 2008. Retrieved May 22, 2009 from <http://www.apparelandfootwear.org/UserFiles/File/Statistics/ShoeStats2008_0808.pdf>.
2. Armour, S. (2005, November 6). Generation Y: They’ve arrived at work with a new attitude.USA Today. Retrieved May 20, 2009, from <http://www.usatoday.com/money/workplace 2005-11-06-geny_x.htm>.
3. Buford, H (2005) Getting serious about winning the African American market. The SourceBook of Multicultural experts 2004/2005. Retrieved May 20, 2009, from <http://www.primeaccess.net/downloads/news/Sourcebook_AA_04-05.pdf>.
4. Charles, K. K., E. Hurst, & N. Rousannov (2008, May 14). Conspicuous consumption and race: Who spends more on what. Retrieved May 23, 2009 from <http://knowledge.wharton.upenn.edu/article.cfm?articleid=1963>.
5. Humphreys, J. (2009). The multicultural economy 2009. Georgia business and economic conditions, 69 (3), 1-16. Retreived August 17, 2009 from <http://www.terry.uga.edu/selig/docs/GBEC0903q.pdf>.
6. Kaplan, D. & Lefton, T. (2008, January 28). Nike to keep federer with a 10-year deal. The SportBusiness Journal. Retrieved May 22, 2009 from <http://www.sportbusinessjournal.com/index.cfm?fuseaction=article.main&articleId=57885&requestTimeout=900>.
7. Lyons, R., & Jackson, E. N. (2001). Factors that influence African American Gen-Xers to purchase Nikes. Sport Marketing Quarterly, 10 (2), 96-101.
8. Magazine Publishers of America (2008). African-American/Black market profile: Drawing on diversity for successful marketing. New York, NY.
9. Martin, J. A. (1996). Is the athlete’s sport important when picking an athlete to endorse a nonsport product? Journal of Consumer Marketing, 13 (6), 28 – 43.Mediamark Research & Intelligence (2007). Teenmark New York, NY.
10. Mullin, B., Hardy, S. and Sutton, W. (2008). Sport marketing (4th ed.). Human Kinetics: Champaign, IL.
11. National Sporting Goods Association (2009). Athletic footwear sales by month 2009. Retrieved May 23, 2009 from <http://www.nsga.org/i4a/pages/index.cfm?pageid=3513>.
12. Neuborne, K. (1999, February 15). Generation Y. BusinessWeek. Retrieved May 20, 2009, from <http://www.businessweek.com/1999/99_07/b3616001.htm>.
13. Shani, D. (1997). A framework for implementing relationship marketing in the sport industry. Sport Marketing Quarterly, 6 (2), 9-15.
14. Shank, M. (2008). Sports marketing: A strategic perspective (4th ed.). Prentice Hall: New York.
15. Stevens, J., Lathrop, A., & Bradish, C. (2005). Tracking Generation Y: A contemporary sport consumer profile. Journal of Sport Management, 19 (3), 254-277.
16. Turco, D. M. (1996). The X factor: Marketing sport to Generation X. Sport Marketing Quarterly, 5(1), 21-23, 26.
17. University of Georgia, Selig Center for Economic Growth (2008). The multicultural economy 2008. Retrieved May 22, 2009, from the Terry College of Business Web site: <http://www.terry.uga.edu/selig/docs/buying_power_2008.pdf>.
18. Yan, S. (2006, December 8). Understanding generation Y. The Oberlin Review. Retrieved May 22, 2009 from <http://www.oberlin.edu/stupub/ocreview/2006/12/08/features/>
19. Yoh. T., Mohr, M. S., & Gordon, B. (2006). The effect of gender on Korean teens’ athletic footwear purchasing. The Sport Journal, 9(1), 14-28.
20. Yoh. T., & Pitts, B. (2005). Information sources for college students athletic shoe purchasing. Sport Management and Related Topics, 1(2), 28-34.

U.S. Sports Academy2013-11-25T16:36:38-06:00January 25th, 2011|Contemporary Sports Issues, Sports Facilities, Sports Studies and Sports Psychology|Comments Off

An Examination of Idaho High School Football Coaches’ General Understanding of Concussion

Caroline E. Faure, EdD, ATC; Cynthia Lee A. Pemberton, EdD

### Abstract

While the underreporting of concussions to high school football players has been previously documented through an investigation of the general understanding of football players, no studies to date have looked at high school football coaches’ general understanding of concussion. This study was conducted in 2006 with a dual purpose of examining the Idaho high school football coaches’ general understanding of concussion and determining whether or not those coaches were consistent with experts’ recommendations in concussion management, including the determination of the appropriate time for return to play. Questionnaires were sent to all Idaho high school head football coaches (n=128) of which 60% (n=77) responded. Data showed the consistency, or lack thereof, of concussion management and return to play, relative to published expert guidelines. Upon analysis it was clear that these coaches’ practices were not consistent with expert recommendations regarding identifying and managing concussion. Many coaches were unfamiliar with the signs and symptoms of concussion, and were especially naïve when it came to identifying instances of mild concussion, including “bell ringers” and “dings”. There was also a lack of awareness about objective tools related to return-to-play decision making. Coaches who had access to athletic trainers managed concussion more consistently. Across all levels, but especially in smaller schools, there was a lack of concussion education afforded to coaches.

**Keywords:** concussion, coaches, high school, football, education

### Introduction

An estimated 300,000 sport-related concussions occur annually in the United States, with high school football players suffering more than 64,000 of those injuries (4, 12, 29). These are the known cases. Thousands more are believed to go unreported (5,16, 29). A concussion is defined as, “any transient neurological dysfunction resulting from a biomechanical force that may of may not result in a loss of consciousness” (8, p. 228). Unlike a cut, a scrape, or a broken leg, concussive injuries are rarely visually obvious. What makes concussive injuries even more complicated is the fact that concussion is a functional injury, not a structural one, meaning it will affect neurocognitive performance but not necessarily show up on MRI or CT scans (5,6,31). This could contribute to the lack of concussion diagnosis or to the belief that concussion does not necessitate conservative treatment if structural damage is not found. In 1990, Dr. M. Goldstein (9) referred to concussion as “a silent epidemic” (p. 327). Unfortunately, nearly two decades later, Goldstein’s warning still sends shockwaves, as young athletes die from sport-induced concussions (1,13,25). Leading experts agree that high school athletes have a significantly greater risk of sustaining a concussion, and that those concussions take longer to heal when compared with concussions sustained by college-aged athletes (6,7). There are many potential reasons for this, but most researchers agree that the younger brain is more vulnerable because it is not fully developed (11,17). Furthermore, many concussions sustained by younger athletes go unreported because youth sport coaches, leaders, parents and even athletes themselves do not fully understand what concussion is or that it has occurred (6,16). Experts agree, even so-called “bell ringers” and “dings” require medical attention and should be considered concussive injuries (17,31). When such momentary states of disorientation or dizziness are ignored, an additional threat is posed in the form of Second Impact Syndrome, or SIS (1,13,22). SIS may occur when an athlete sustains a second concussion before the symptoms of the first have healed (1). Though rare, SIS is characterized by rapid swelling of the brain and may be fatal (2). SIS is most often associated with adolescent athletes, perhaps because of the sensitivity of their developing brains, and because the seriousness of the first concussion is often overlooked (1,5,13,22,28).

While the national spotlight illuminates instances of deaths that occur from sport-related concussion, there still remains the need to educate sport leaders on ways to protect the athletes who compete (21). The Centers for Disease Control and Prevention (3) offer a free toolkit, Heads Up: Concussion in High School Sports that is available to coaches at no charge. In addition, the National Athletic Trainers’ Association (NATA) and its Appropriate Medical Care for Secondary School-Aged Athletes Task Force (AMCSSAA) have made several recommendations (11). Among them are that every high school in the United States develop and implement a comprehensive athletic health care administrative system. Athletic trainers and physicians are critical components of that system (11,16).

Recognizing a lack of athletic trainers in Idaho’s secondary school setting and especially in the rural school environment, a study was conducted in 2006 with the dual purpose of examining the Idaho high school football coaches’ general understanding of concussion, and determining whether or not those coaches were consistent with experts’ recommendations when it came to managing concussion and determining the appropriate time for return to play following concussion. The findings make clearer the need for proper concussion management in high schools, including the need for athletic trainers and continuing education for coaches. Understanding the characteristics of concussion and recognizing the unavailability of athletic trainers, the following research questions guided this investigation:

1. Who was the person most often called upon to identify and manage concussive injury in Idaho’s high school football programs?
2. What is the Idaho high school football coaches’ general understanding of current research on concussion characteristics, evaluation and management?
3. Relative to published expert recommendations, how consistently did Idaho high school football coaches determine when it was safe to return concussed athletes to play?
4. What, if any, continuing education opportunities have been made available to Idaho high school football coaches in the area of concussion management?

### Methods

#### Participants

The participants consisted of 128 Idaho high schools fielding a high school football program. All head football coaches were invited to participate in the study (N=128) via postcards and e-mails, with contact information obtained through the directory of the Idaho High School Activities Association (IHSAA).

#### Instrumentation

This study involved the use of two instruments. The primary instrument was a questionnaire entitled *Profiles and Perceptions of Idaho High School Football Coaches*. This instrument was developed by the researchers to address the research questions, and employed a forced choice response format, supplemented by two open-ended questions. Once drafted, the questionnaire was subjected to expert review with two of the nation’s leading experts on concussion research and six athletic trainers from the Idaho Athletic Trainers’ Association.

The secondary instrument was *The Concussion Management and Return to Play Protocol*. This instrument employed a semi-structure interview protocol and focused on research questions two and three. Like the questionnaire, it was subjected to expert review as described above. The interview protocol was engaged in person with a small, purposive sample of high school football coaches (n=10). The interview questions were phrased to solicit responses that explained the coaches’ behaviors when it came to managing concussion and determining when it was safe to return an athlete to play.

#### Procedures

Institutional review board approval was obtained from Idaho State University before the study began. In mid-September of 2005, all Idaho head high school football coaches were invited to participate via a mailed postcard. The postcard summarized the study purpose and alerted the coaches that a survey packet would arrive the following week. At the same time, Idaho high school principals and athletic directors were informed about the study via an e-mail blast. Administrators were asked to encourage their coaches to participate. The following week the survey packets were mailed. The packets included an introductory letter, a copy of the primary instrument, and a postage-paid, self-addressed return envelope. Coaches were instructed to complete the questionnaire within a two-week time period. The following week, an email reminder was sent to both the coaches and athletic directors. Informed consent was implied upon completion and return of the questionnaire.

Interviews were conducted approximately 6 weeks after the return of the questionnaires. This time frame was chosen because it coincided with the state high school football playoffs and there was good accessibility to a purposive sample of coaches. The interviews were audiotaped and lasted between 10 to 45 minutes. Recorded interviews were transcribed verbatim and interviewees were sent the transcripts with a request to check for response accuracy. Because of convenience, electronic mail transmission was the preferred method for these communications. Coaches were encouraged to make necessary corrections and/or add additional comments. To ensure confidentiality, final verbatim transcripts were coded, and referenced in the study by those codes.

#### Data Analysis

For the primary instrument, data were analyzed using basic descriptive statistics. The data were also stratified according to athletic classification level (i.e., school size). Narrative data from the two open-ended questions, “In the space below, please describe any other signs or symptoms that you would expect to be a sign or symptom of concussion that are not listed above” and “Please use the space provided below to make comments/suggestions that could benefit you as a coach in recognizing the signs and symptoms of head injuries in sports” were reviewed and read noting common themes.

As Yin (33) pointed out, it is necessary to go beyond the simple collection of descriptive data and begin the complex procedure of analyzing behavioral characteristics. Therefore, it was deemed important to also consider the behaviors that guided the coaches’ decision-making processes. When reviewing the interview transcripts, processes of open and axial coding were used to help with pattern analysis (27). Open coding was the first step toward distinguishing “properties” and “dimensions” in the data (27, p. 102). Themes and subthemes emerged that helped to explain the coaches’ patterns of behavior. Special attention was directed to repeated words and phrases, and to the chronological behaviors of the coaches. We first identified these themes and subthemes and later their presence in the data was confirmed by a data analysis focus group consisting of athletic trainers from the Idaho Athletic Trainers’ Association. Focus group members were instructed to separate narrative data into their own major themes and subthemes. The focus group’s thematic analyses were then compared to the thematic analysis derived by the researchers. Finally, through discussion between the researchers and focus group members, the agreed-upon thematic constructs were narrowed and confirmed (see Table 1).

### Results

Study findings are reported first regarding respondent/interviewee demographics, then by questionnaire areas of inquiry. Specifically these areas of inquiry include: person(s) responsible for concussion identification and management, coaches’ understanding of concussion identification and management, return to play decision-making, coaches’ continuing education relative to concussion identification and management, and findings reviewed relative to school size.

#### Demographics

Of the 128 coaches invited to participate in the study, 77 responded, resulting in a 60.1% response rate. The responses represented all five Idaho high school athletic classification levels. All participating coaches confirmed they were the head varsity football coach at their school. Descriptive data related to participant demographics appear in Table 2. Of the responding coaches, 93.3% (n=70) stated they had taken a basic or advanced first aid course through the American Red Cross (ARC) or the American Heart Association (AHA), and 94.7% (n=71) stated they had taken a CPR course through one of the same organizations. Nearly 88% of the coaches (n=65) also mentioned they had received formal training in sports injury prevention at some time in their past. While 89% (n=66) of coaches could identify formalized educational training in sport-specific issues (such as tackling), only 42% (n=31) stated they had also received formal training in football equipment fitting (see Table 2).

#### Person(s) Responsible for Concussion Identification and Management

To better understand who identifies and manages concussion in Idaho high school football programs, the questionnaire asked the coaches to clarify the person(s) primarily responsible for evaluating sports related head injuries including concussion. Only 35.9% (n=23) acknowledged having an athletic trainer at their disposal regularly for practices and games. Coaches were asked, “When an athlete on your team sustains a head injury or suspected concussion, what is the title of the person who is most often called upon to evaluate the injury?” Understanding that some teams might have medical personnel on hand for game settings but not for practices, coaches were asked to clarify any differences that might exist between practice and game situations. Figure 1 depicts the summary of the coaches’ responses, and reveals the distribution of responsibility when it comes to evaluation of concussion (see Figure 1).

To better understand return to play practices, coaches were also asked, “When an athlete on your team sustains a head injury or suspected concussion, what is the title of the person who is most often called upon to determine when it is safe to return the athlete to play?” Again, responses were specific to practice and game situations. Figure 2 displays these responses, and shows the distribution of responsibility when it comes to determining return to play (see Figure 2).

#### Coaches’ General Understanding of Concussion Identification and Management

Despite the fact that an overwhelming majority of coaches had previously taken first aid or sports injury management courses, most Idaho high school football coaches felt they were unprepared to manage concussion inherent in football. 76.7% (n=56) of participants stated they did not feel they had been adequately trained in this area. Participants were also asked whether or not the risk of concussion in the sport of football concerned them. Overwhelmingly, 94.2% (n=65) of coaches said the risk of concussion in football did concern them.

Coaches acknowledged their job duties extend beyond schematics. 86.3% (n=63) of coaches felt they had a responsibility to be able to recognize the signs and symptoms of concussion and to know how to tell when it is safe to return an athlete to play. However, when participants were asked to identify what they felt those signs and symptoms of concussion were given a list, there seemed to be some confusion. While common signs and symptoms such as headache and disorientation were widely recognized, the majority of coaches did not understand that less-common symptoms, such as difficulty breathing and insomnia, are indicative of concussion, as well. Only 32% (n=24) of participants felt difficulty breathing could be associated with concussion, and 29% (n=22) understood insomnia to be connected to concussion. Other notable signs and symptoms of concussion were also mistaken, including sensitivity to noise (47%, n=35) and sensitivity to light (69%, n=52). Table 3 displays coaches’ responses when asked to identify whether or not a certain sign or symptom could be indicative of concussion. Experts have agreed that all of these signs and symptoms are consistent with concussion (11,17). It was important to note that 97.3% (n=73) of the participants understood that a concussion is not always accompanied by a loss of consciousness. These data may help to dispel the myth that concussion is only associated with a loss of consciousness (see Table 3).

Interview data were grouped according to observations regarding (a) physical signs and symptoms, (b) mental status, and (c) kinesthetic awareness. When asked, “How do you know when a concussion is sustained? Describe the first thing you look for,” nearly all of the coaches said the athlete’s eyes, specifically, “the pupils of the eye” (C7, C10) were the primary focal point. C2’s methods were more unique. Replying that he had been “trained real good” in a “five-minute training”, C2 described his process:

> The only way I’ve been taught is to look at his eyes… to have him shut his eyes and stay real still and if he opens his eyes and his pupils dilate, then he probably doesn’t have a head injury.

Some coaches did not seem to understand the potential seriousness of those concussions that do not result in a loss of consciousness, especially mild (Grade 1) concussions. “Bell ringers” were often not identified as concussions. Participants were asked to respond to a scenario and decide whether or not they felt a player who was “hit hard, feels dazed and confused for just a few minutes (sometimes referred to as ‘getting his bell rung’), but who is able to walk back to the huddle on his own” had suffered a concussion. 57.6% (n=38) felt that the player had sustained a concussion while 42.4% (n=28) felt that the player had not sustained a concussion. Seven participants either did not answer the question or commented that they were unsure. Concussion researchers agree that getting one’s bell rung is characteristic of mild concussion. However, it is often dismissed (11,17). At least one coach acknowledged his uncertainty:

> In my opinion and experience as a player and a coach, every player experiences at least one of the symptoms … at least once a game and practice. Where to draw the line between a real head injury and getting your bell rung is tough. (C15)

#### Return to Play Decision-making

As stated, many coaches acknowledged a duty to determine when it was safe to allow a concussed athlete to return to activity. An additional set of questions in the questionnaire sought to detect whether or not Idaho high school football coaches felt the seriousness of a concussion, formerly referred to as a grade, played a role in allowing an athlete to continue play. When asked if a player who had sustained a Grade 1, or mild, concussion should be immediately removed from a game or practice, 57.3% (n=43) said yes. 34.7% (n=26) said no, and 8.0% (n=6) stated that they did not know. When asked if a player who had sustained a Grade 2, or moderate, concussion should be immediately removed from the game or practice, 88.0% (n=66) said yes, 6.7% (n=5) said no, and 5.3% (n=4) said they did not know. When asked if a player who had sustained a Grade 3, or severe, concussion should be immediately removed from the game or practice, 94.6% (n=70) of coaches said he should, 4.1% (n=3) said he should not, and 1.4% (n=1) said he did not know. Clearly, these coaches were aware that as concussion grade increased, play/participation should be discontinued.

The coaches’ methods for determining return to play were further explored through the interviews. Responses were grouped according to those that typically make referrals to physicians and/or athletic trainers, and those that do not. Coaches who stated they had athletic trainers at their disposal said they are not involved in the decision-making process. When asked, “How do you decide when it is safe to allow an athlete with a concussion to go back into the game?” C3 abruptly responded, “We don’t decide. That’s decided by the team doctor and the trainer.”

Other coaches said they sometimes do not make referrals. C8 said he was hesitant to allow his athletes to be evaluated by physicians. He did not agree that bell ringers were consistent with concussion, nor did he agree that there was an added risk of playing through such an injury. C8 suggested doctors were too quick to diagnose a concussion and remove an athlete from play, thereby making his coaching job more difficult:

> I just think doctors are sometimes being so leery that if there’s any question in their mind then they say the kid’s got a concussion and shouldn’t play. They just don’t want to risk getting sued. There’s got to be a happy medium there.

Influencers were apparent when it came to return to play decision-making. While the majority of coaches said they would always keep the safety of the athlete as the primary focus, and that they would “err on the side of caution” and “sit players out” (C17), several coaches acknowledged the pressure to win or play, or pressure from parents, school administrators, and the athletes themselves, had, at some point, impacted their decisions. C8 said as a coach, his job was “to get the best players on the field” and that sitting players out for something as simple as a bell ringer “can get to the point where we side on the side of over-caution – to the point where it can get a little ridiculous.” C6 said it was “a little hard” to hold one of his better athletes out, “especially when the community recognizes how vital that player is to the team’s success.” C4 suggested he also might follow different rules for different kids. He told me, “When you’re a senior, you know how that works – you’ve been around athletics… you get a senior and he really wants to play.”

Participating coaches were largely unfamiliar with evidence-based concussion assessment tools. These were identified as symptom scale checklists, the Sideline Assessment of Concussion, and computerized neurocognitive assessments, such as ImPACT, HeadMinder and CogState. 56.8% (n=42) of coaches stated they never use concussion assessment tools. Of those who indicated they were familiar with the tools, 25.7% (n=19) said they were familiar with concussion symptom scale checklists, 9.5% (n=7) said they were familiar with the Sideline Assessment of Concussion, or SAC, and 6.8% (n=5) said they knew about computerized neurocognitive testing programs. No coaches were familiar with the Balance Error Scoring System. When asked how frequently they used these evidence-based assessment tools, only 18.9% (n=14) of those coaches who were familiar with one or more of the tools stated that they use them every time a suspected concussion was sustained, and 40% (n=12) said they learned about them from an athletic trainer. Of the eight coaches interviewed, only one described a research-based procedure for determining whether or not an athlete could return to play. This coach was at a 5A school with two athletic trainers. The athletic trainers at this school utilized the ImPACT concussion assessment tool:

> During the week if it’s not a game we hold the player out until they have taken a post concussion test and we evaluate their scores from when they were healthy to after the concussion has happened. Once they score equivalent to where they were prior to a concussion and they feel good and they’re cleared by the trainer or the doctor then they’re able to return. (C9)

#### Continuing Education

Participants were asked whether or not the school they coached at had provided them with training opportunities aimed at concussion and other sports injury management. 60% (n=45) stated that their school had not offered any additional training, while 40% (n=30) stated their school had. The majority stated they would be eager to learn more about the topic. 97.83% (n=72) said they would be more likely to use an evidence-based concussion assessment tool if it were made available to them at no cost. And, when asked whether or not they would be likely to participate in an educational program to teach them how to be more prepared to handle concussion injuries, 98.6% (n=71) said they would be.

#### Data Stratification by School Size
After initial analysis, the data were stratified to see whether or not trends existed relative to school size. As expected, there was a marked difference in the presence of athletic trainers based on school size. At Idaho’s largest (5A) high schools (more than 1280 students), an athletic trainer worked regularly with all football teams. By comparison, only 7% of Idaho’s smallest (1A) schools (less than 159 students) coaches stated that they had an athletic trainer. Table 4 displays these data and shows the presence of athletic trainers at the various athletic classifications (see Table 4).

The availability of athletic trainers at Idaho’s larger schools relieved coaches of the primary responsibility of concussion identification and management. C15 said, “I would rather my trainer do that and I just coach football.” C20 commented, “Having an athletic trainer has been a big relief on me on making decisions on head injuries.” Without athletic trainers, coaches inherited the responsibility. At the 1A level, 70.6% of coaches (n=12) said they were the ones responsible for identifying concussive injuries when they occur at practice. At the 2A level, 46.7% of coaches (n=7) assumed this responsibility, and 73.7% of 3A coaches (n=14) had the responsibility. By comparison, none of the 5A coaches who participated in this study acknowledged having responsibility for concussion identification and management. During game situations, coaches at the smaller schools acknowledged having more medical assistance to rely on. Physicians, nurses and EMTs were often available during games, even at the smaller schools. Because of their presence, just over 35% of 1A coaches (n=6) said they were the ones responsible for identifying concussive injuries in a game setting. Nearly 27% of 2A coaches (n=4) and 33% of 3A coaches (n=3) had this responsibility. All 4A and 5A coaches suggested the responsibility of managing concussion-related injuries was charged to either athletic trainers and/or team physicians during game situations. Table 5 displays these data and the differences between school classification in terms of concussion identification and management (see Table 5).

In Idaho, it was apparent that the smaller the school, the more likely the coach was the one who made return to play decisions. When asked who the primary person responsible for determining the appropriate time for an athlete who had sustained a concussion to return to play during practice situations was, 64.8% of 1A coaches (n=11) said they were. Again, no coaches at 5A schools had this responsibility. In game settings, the trend continued. Just over 47% of 1A coaches (n=8) reported being the person primarily responsible for determining return to play on game day, while no 5A coaches acknowledged this responsibility. Table 6 displays the disparities among the various school classification levels regarding determination of return to play (see Table 6).

When presented with the bell ringer scenario, only coaches from Idaho’s largest schools (5A) were consistently recognizing it as such. Table 7 reveals these data (see Table 7).
While beneficial when it came to managing concussion, the presence of athletic trainers did little to make coaches feel more prepared to handle the duty themselves. Coaches at the 4A and 5A levels who were also more consistent in their identification and management of concussion and who had athletic trainers at their disposal, admitted to being most uncomfortable with their ability in this area. Table 8 displays these findings (see Table 8).

Across all athletic classification levels, most coaches felt a compelling need for additional educational training when it came to managing concussion in their football programs. Not only did 1A schools not have appropriate or adequate medical supervision onsite at practices and games, it was also apparent that the football coaches at Idaho’s smallest high schools were not being provided with educational programs aimed at concussion and other sports injury management when compared to coaches at Idaho’s largest schools. Only 18% of 1A coaches stated that their school had provided them with training opportunities while 63% of 5A coaches were provided with educational outreach. Table 9 shows the data (see Table 9).

### Discussion

Since this study was limited to Idaho high school football coaches, its results may not be generalized to other states, however, findings may provide a snapshot that could provoke further inquiry into coaches’ qualifications and expertise in the area of concussion identification and management. This is consistent with the findings of McCrea et al., (16) who suggested continuing education of coaches is warranted. When it comes to concussion recognition, there is little room for error. A concussion disrupts the brain’s metabolism and the only thing that appears to help it heal is rest (17,30). This study brought to light the compelling need to do more when it comes to training coaches to adequately prepare for and manage concussive injuries. The findings spotlight the need for better concussion education programs for Idaho’s secondary sport coaches, especially those who coach at small schools with limited access to an athletic trainer or other medical personnel support. The findings also highlight the need for replicable studies in other states to determine educational needs of coaches in those areas.

The findings are discussed relative to: the persons responsible for concussion identification and management—accessibility of athletic trainers, understanding of concussion, return to play decision making and willingness of coaches to refer athletes, and continuing education. Continuing education implications derived from these findings are discussed in detail, specific to evaluation of concussion signs and symptoms, cognitive stability testing, bell ringer recognition and the ongoing need for additional first aid and concussion training.

#### Persons Responsible—Accessibility of Athletic Trainers

Consistently, coaches were charged with the responsibility of initial concussion identification and management. Some coaches also acknowledged having the sole responsibility of deciding when to allow a concussed athlete to return to play. National recommendations point to the need for athletic trainers to do this job (11,16,17). Despite these recommendations, athletic trainers were accessible to coaches at only 36% of Idaho’s high schools. This was below the 2008 national average of 42% (20). The scarcity of athletic trainers in Idaho’s smallest schools was expected. The best-case scenario would be for sport administrators to require onsite athletic trainers at sport practices and games that have significant catastrophic risks such as football. This study indicated concussion was managed more consistently and effectively at schools with athletic trainers. All 5A (large schools) coaches (n=7) who responded to this survey indicated that they had an athletic trainer who worked regularly with their football teams; and all of these coaches correctly identified a scenario involving a bell ringer as concussion and said their standard practice would be to withhold that athlete from play.

#### Understanding of Concussion, Return to Play Decision-making and Willingness of Coaches to Refer Athletes

Coaches should be informed that in cases where concussion is suspected, their primary role is to ensure medical referral for the athlete (11,16). The coaches in this study were inconsistent with regard to making referrals. While most stated they would always refer athletes with a recognized concussion to an athletic trainer or physician, some said they would rather manage the injury themselves. C8 and others seemed to lack an appreciation of the catastrophic risks associated with concussive injuries. In the past, coaches have been held liable for failing to provide adequate assistance to injured athlete. In numerous court cases, including Mogabgah v. Orleans Parish School Board (19), Stineman v. Fontbonne College (26), and Searles v. Trustees of St. Joseph’s College (23), coaches have been held accountable for their failure to recognize the potential severity of a sports-related injury.

#### Continuing Education and the Evaluation of Concussion Signs and Symptoms

Although the majority of the coaches had received basic first aid and CPR training or had identified taking a formal course in sports injury prevention, this training did not imply an understanding of concussion identification and management. Many of the coaches recognized the most common signs and symptoms of concussion, but they failed to recognize many of the more subtle signs and symptoms. While loss of consciousness, headache, disorientation, and memory loss were clearly connected with concussion, more subtle effects, like sensitivity to noise, and insomnia, were not. Concussion is an “individualized, complex injury, and … no particular symptom can provide definitive guidance for every patient and clinical situation” (11, p. 6). Therefore, even though athletes may demonstrate different signs and symptoms, it is important to consider all of the options (11). Even then, symptom scores should not be considered solely reliable. As expected, the coaches in this study relied on subjective measures of concussion assessment. However, responses to such questions like, ‘Do you have a headache’ and ‘Are you dizzy’ are not consistent or reliable indices of concussive injury. This is largely because athletes may be reluctant to report their symptoms for fear of not being allowed to play or because they do not think their injury is serious enough to warrant removal from play (16). A quick clearance and return to play based on subjective responses can increase athlete susceptibility for additional injury, including SIS (1,11,28). Conservative management of even mild instances of concussion is important in athletes under the age of 18, because almost all reported cases of SIS are in young athletes (1,11).

#### Cognitive and Stability Testing

While assessing symptoms is always warranted, baseline cognitive and postural-stability testing should also be considered for athletes playing sports with a high risk of concussion. Use of such functional tests can help to identify deficits caused by concussion and help protect players from potential risks involved with returning to play too quickly (11,17). This study’s findings reflect a lack of such assessment. Evaluation of symptoms should be supplemented with detailed questioning and functional tests, both of the brain and body (10,17). Guskiewicz, Ross and Marshall (10) concluded that simple processes, including concentration, working memory, immediate memory recall, and rapid visual processing have been shown to be mildly affected by concussion. Establishing baseline measurements before the season is recommended for comparison purposes (11,17). No coaches in this study said they conducted functional testing. In fact, none were even aware of the Sideline Assessment of Concussion or the Balance Error Scoring System. Both of these functional tools can be administered at little or no cost. Furthermore, only one coach who participated in the study was aware of neurocognitive testing programs such as ImPACT, another functional concussion assessment. He said he was aware of the test because he had heard about it being used with professional players.

#### Recognition of ‘Bell Ringers’ as Concussion

Study findings revealed coaches’ misconceptions that bell ringers or dings are not concussive injuries, and as such do not necessitate removal from play. The findings also demonstrated coaches’ beliefs that the terms bell ringer and ding carry a connotation that diminishes the potential seriousness of the injury (11,16,17). Nearly half of the coaches indicated they would allow the athlete who had his bell rung to continue physical activity. This lack of initial recognition and diagnoses supports the findings of McCrea, et al., (16), and the likelihood of athletes being allowed to continue to play while being symptomatic. Not only is SIS a factor when returning to play too soon, concussions can accumulate and lead to other long-term impairments. According to King (14), lasting verbal and visuospatial impairments have been directly linked to concussion, and athletes with a history of concussion can suffer for a lifetime from emotional changes including a difficulty to control their own anger. King (14) also contended that athletes with a history of concussion can also suffer permanent decreases in libido, sleep impairments, and can have difficulty adapting to social changes. Severe depression can also linger (12).

#### Need for Additional Training

While most state high school athletic associations require first aid and CPR training, those classes typically fall short of relaying information concerning sports-related concussion. Few states require the medical training of coaches to be supplemented to include concussion management. To date only Texas, Washington, Oregon, and Connecticut have made comprehensive training on the subject a mandate. In Texas, S.B. 82, or “Will’s Bill”, was signed into law and took effect in September of 2007. Washington’s “Zackery Lystedt Law” and Oregon’s “Max’s Law” were both passed in 2009. All three laws require youth and high school sport coaches to be trained in concussion management and cognizant of SIS. Washington’s law goes one step further. It requires a licensed health care provider to oversee each concussive injury and determine the appropriate time for the athlete to return to play (34). McCrea et al., (16) demonstrated the value of concussion education. Their study examined the reasons for the purported underreporting of concussions to high school football players. McCrea et al. concluded that players, like the coaches in this study, were not fully aware of what a concussion was. However, when provided with a definition of concussion and a description of injury signs and symptoms, the players more readily recognized the injury and were more likely to admit to sustaining concussion over the course of a football season.

No coaches in this study recalled a systematic, stepwise approach for returning athletes to play. Experts contend concussed athletes should not be allowed to return to play until all of the following conditions are met: (a) there was no loss of consciousness, (b) the athlete suffers from no amnesia, (c) the athlete is asymptomatic at rest, (d) the athlete is asymptomatic following exertion, and (e) the athlete passes all functional tests (11,17,24). The coaches in this study admitted there were other influences that convinced them to return concussed athletes to play prior to the resolution of symptoms. Some, perhaps refusing to accept responsibility or more concerned with winning, de-emphasized the importance of concussion management. Micheli, Glassman, and Klein (18) suggested coaches might feel the management of injury is not their responsibility. This was clearly the case among the Idaho football coaches in this study. In fact, one coach, C29, reiterated that “trainers are here to make the decisions and deal with the injuries, NOT THE HEAD COACHES [sic].” Because of this, coaches may have felt they needed to be less prepared to identify and manage concussion.

The lack of educational opportunities related to concussion identification and management could be the reason why these coaches are unfamiliar with the topic of concussion management. The lack of educational opportunities was most evident in Idaho’s more rural (smallest school) areas. The overwhelming willingness of coaches in this study to attend professional development workshops could be one solution. Coaches who participated in this study clearly stated they would be much more comfortable managing concussion injury if they were adequately trained to do so. When professional development occurs, it is important that knowledgeable and trained professionals teach them. With new information about concussion being discovered every year, educational workshops would be warranted annually. Such educational efforts can and should be extended beyond administrators and coaches. Parents, and even the athletes themselves, can and would benefit from learning about concussion’s subtle signs and symptoms, and the consequences involved with returning to play too soon. Perhaps then, the outside influencers and pressures coaches noted would diminish.

### Conclusions

This study revealed a lack of understanding among Idaho high school football coaches relative to concussion identification and management. Coaches were especially dismissive of instances consistent with mild concussion, or bell ringers, and their catastrophic potential. Coaches purported to address concussion management with subjective approaches that relied on athletes to self-report their symptoms. They were unaware of functional assessments that objectively measured both the brain and body. Coaches acknowledged that outside pressures contribute to their decisions on when to allow concussed athletes to resume physical activity. Their lack of understanding may be attributed, in part, to the fact that there are few athletic trainers in Idaho’s secondary schools, and there are few or no educational workshops provided to coaches on concussion management.

### Applications in Sport

While this study was limited to Idaho high school football coaches, its findings may be generalized to other coaching populations. All contact sport athletes are susceptible to concussive injury. In the absence of athletic trainers or other health care professionals on the sport sideline, it is imperative that coaches be able to recognize concussive injuries and manage them according to current published guidelines.

### Figures and Tables

#### Figure 1
Identifying Concussion Incidence: Idaho High School Football
![Identifying Concussion Incidence: Idaho High School Football](/files/volume-14/2/figure-1.jpg “Identifying Concussion Incidence: Idaho High School Football”)

#### Figure 2
Determining Return to Play: Idaho High School Football
![Identifying Concussion Incidence: Idaho High School Football](/files/volume-14/2/figure-2.jpg “Identifying Concussion Incidence: Idaho High School Football”)

#### Table 1. Thematic Constructs
Examples of Raw Data Themes and Subsequent Subthemes and Major Themes

Raw Data Theme	Subtheme	Theme
Glassy eyes. Dilated pupils.	Physical Signs & Symptoms	Recognition
Whether he’s not all together there. How cognizant they are of where they’re at.	Mental Status
Whether he’s wobbly.	Kinesthetic Awareness
It depends on the kid! Every player experiences at least one of the symptoms. I look at the severity of the hit.	Mechanism of Injury & other variables
I get him to a trainer. We have doctors on our sideline.	Referrals	Evaluation
I asked them questions, look in the eyes. We observe him for awhile. We just keep him out. We watch them very carefully.	Watch and Wait	Evaluation
We don’t decide. That’s decided by the team doctor and the trainer. They have to have a doctor’s release. It’s gotta be a parent.		We let him sit for awhile. Usually you go about a week and a half. We sit them out a week.	Time Away
I think we can go too overboard on it. We can get to the point where we side on the side of over-caution – to the point where it can get a little ridiculous.	It’s No Big Deal
We want to keep our best players in the game. A kid that wanted to play in the playoffs. If the parents say it’s okay, then that at least releases the coach of that (responsibility). He’s a young kid; He’s not a senior.	Pressure to Win (Play)	Influencers
I would put the safety above putting him in the game. It’s too dangerous. The kid’s health is more important than any game that we play.	Safety Comes First	Influencers
We need an athletic trainer. We probably could have more – at least EMT types around for practice.	Resources	Needs
I would love the opportunity to learn more. You have to know what’s happening with your players, especially when concussion is involved.	Education
Helmet issues are going to be real paramount. The teaching of how to tackle is very important.	Equipment & Instruction

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### Corresponding Author

Caroline E. Faure, EdD
Assistant Professor of Sport Science and PE
Idaho State University
STOP 8105
Pocatello, ID 83209
<faurcaro@isu.edu>
208 282-4085

### Author Biographies

#### Caroline Faure, Ed.D., ATC

Caroline Faure, Ed.D., ATC is an Assistant Professor of Sport Science and Physical Education at Idaho State University, where she teaches undergraduate and graduate courses in sports medicine and sports law. Dr. Faure earned the prestigious Kole-McGuffey Award at Idaho State University for her research on concussion management in secondary schools.

#### Cynthia Lee A. Pemberton, Ed.D

Cynthia Lee A. Pemberton, Ed.D. serves as the Associate Dean of the Graduate School and Professor of Education/Graduate Faculty at Idaho State University. Dr. Pemberton has published and presented locally, regionally, nationally and internationally on Title IX and gender equity in school sport. Her book, More Than a Game: One Woman’s Fight for Gender Equity in Sport, addresses Title IX from both personal and professional perspectives, through a lived experience pursuing gender equity in sport at a small liberal arts college in Oregon. The book received the Phi Kappa Phi Bookshelf Award in October 2002, and has been positively reviewed in a number of publications (Journal of Legal Aspects of Sport, Women in Sport and Physical Activity Journal, Booklist and Choice).

U.S. Sports Academy2016-04-01T09:17:20-05:00January 12th, 2011|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Women’s Perspectives of Personal Trainers: A Qualitative Study

Deana Melton, Ed.D., CSCS, HFS; Teresa K. Dail, Ph.D.; Jeffrey A. Katula, Ph.D.; Karen M. Mustian, Ph.D.

### Abstract

Personal trainers play an integral role in the day-to-day operation of the facilities in which they work. Research has identified a number of qualities and competencies necessary to be an effective exercise leader, but there is little scholarly work addressing clients’ attitudes related to the performance of personal trainers. Utilizing focus group methodology, female clients of personal trainers were recruited to provide viewpoints related to the desirable qualities of personal trainers, as well as opinions regarding trainer certification and academic preparation. Responses of the participants were transcribed, coded, and analyzed for themes. Four global themes emerged: Selection Rationale, Personal Trainer Rationale, Loyalty Rationale and Negative Characteristics. Selection Rationale consisted of qualities that influence a client’s decision to hire a particular trainer (e.g., physique, results observed in other clients, social skills). Personal Trainer Rationale referred to the clients’ reasons (e.g., frustration with current fitness level) for hiring a specific trainer. Loyalty Rationale referred to the credentials of a personal trainer that solidify the client/trainer relationship and Negative Characteristics referred to qualities considered unethical or unprofessional. The results suggest that undergraduate exercise science programs should devote additional time toward the development of future fitness trainers’ affective qualities and that clients would benefit from information about the credentials of personal trainers.

**Key words:** qualifications, certifications, credentials, licensure, attitudes, dispositions

### Introduction

Low levels of physical activity, like many other lifestyles activities (e.g., smoking), are strongly correlated with coronary heart disease, the leading cause of death in the United States (4). Lack of physical activity is also associated with asthma, type 2 diabetes, some cancers, impaired psychological status, bone and muscle problems, and decreased life expectancy (5). Despite this well-documented relationship, 37.1 % of adults have insufficient physical activity (6). Of those who do adopt an exercise program, it is estimated that 50% will discontinue it within the first six months (10), making exercise adherence a critical issue. Factors affecting adherence are complex, but an important one is a client’s perception of support from their personal trainer (28).

The significance of personal trainers has been demonstrated in several studies. Ratamass et al. (23) compared individuals trained by personal trainers to individuals working out on their own. Results showed that both1 Repetition Maximum and Ratings of Perceived Exertion scores were significantly higher in individuals who worked under the supervision of a personal trainer. Similar results were noted in studies by Maloof et al. (17) and Mazetti et al. (18). Quinn (23) suggests that part of the advantage of working with trainers relates to motivation, and that, “certified personal trainers can provide structure and accountability, and [can] help … develop a lifestyle that encourages health.”

Personal trainers, as well as club managers, believe that clients are more likely to stay with a program if the trainers exhibit the attributes of empathy, listening skills, and motivation skills (21). In addition, McGuire, Anderson, and Trail (19) report that important components of clients’ satisfaction with their fitness clubs relate to the leaders’ social support skills and instructional competency. Despite these findings, little is known about how a trainer’s qualities, including training and certification, are viewed from the client’s perspective. Several theoretical models explain the adoption and maintenance of exercise behavior (14), but little research has examined these factors in an applied exercise setting.

Finally, women are a growing majority of all health club members, accounting for 57 percent of the grand total in 2005 (13). Within the commercial club category, women constitute 60% of the national membership. In addition, studies have shown that the majority of those clients who hire trainers are female (25). Because these statistics indicate that women are primary consumers of health club memberships and training sessions, this study focused on female clients. The purpose of this study therefore, is to use an applied setting in which to systematically investigate attitudes of female clients toward the dispositions, certification, and education of personal trainers. To the authors’ knowledge, this study is the first scholarly examination of the current state of personal training from this perspective.

### Methods

#### Experimental Approach to the Problem

The data collection was qualitative and interpretive in nature. The study used the three key assumptions of the qualitative research paradigm: 1) there are no “wrong” answers; only diverse opinions, 2) there is a potential influence of the inquirer (see Limitations section of this paper) and respondent relationship, and 3) the goal is to describe findings within a particular situation (29). This interpretive perspective used grounded theory, or theory that emerges from the data (9). Therefore, this type of inquiry is not a critical or empirical comparison to existing theory.

The investigation used a focus group to examine the overarching question, “What qualities are important to be a successful personal trainer?” The focus group interview offered compatibility with the qualitative research paradigm, opportunity for direct contact with subjects, and the advantages of group format (29). This research was conducted with clients of personal trainers. Global themes, major themes, and sub-themes were selected from the transcriptions. Evidence of credibility, reliability, and trustworthiness was provided in several ways. First, three different readers were used, bringing their varying perspectives to the group. Second, the data presented represents consensus reached via thorough discussions among individuals (readers) with expertise in personal training, exercise physiology, health behavior, and qualitative research methods. Finally, the investigators sent a one-page summary (a member check) to the participants and asked for feedback and clarifications and/or additions they would like to make. The study design was identical to that used in two previous studies which examined the current state of personal training from a personal trainer point of view (20) as well as from a manager point of view (21).

#### Role of the Investigators

The primary investigator was a personal trainer for 10 years before devoting her time to teaching exercise science classes at the university level. She is a certified Health Fitness Specialist with the American College of Sports Medicine, and a Certified Strength and Conditioning Specialist with the National Strength and Conditioning Association. She is also a certified group exercise instructor with the Aerobics and Fitness Association of America (AFAA), as well as a certification examiner for their organization. She has developed and maintained close relationships with both clients and personal trainers and is very familiar with the issues surrounding this profession.

#### Subjects

Subjects included 5 female clients of personal trainers (M age= 36.2 years, with a range of 24-50 years). Detailed demographic information for the subjects is represented in Table 1.

#### Procedures

##### Surveys

Volunteers were randomly solicited from four health clubs in a small southeast community. This selection process involved recruitment through posted flyers as well as by word-of-mouth contacts. Subjects were either personally provided with or mailed a packet including: 1) a demographic/survey sheet, including name, address, age, occupation and education; 2) questions related to certification of trainers; 3) an informed consent form approved by the university Internal Review Board committee, explaining that the participants would be video- and audio-taped during the focus groups; and 4) a list of the questions that would be probed so that the participant could reflect on these prior to the meeting. Finally, in addition to the focus group interview and audiotapes, the surveys were used as a third method for triangulation of the data. After collecting all the demographic/survey sheets, participants were contacted via telephone and asked to participate in the focus groups.

##### Focus Groups

Subjects who agreed to participate were given a list of the questions that would be discussed prior to the focus group meeting. These questions were:

1. Why did you decide to hire a personal trainer?
2. What attracted you to a particular trainer?
3. What characteristics kept you coming back to the same trainer?
4. Do you know the qualifications of your trainer?

a. If you do not know, how do you know that you are getting what you paid for?
b. Does it matter if they have certifications?
c. Do you know which certifications are the most respected?
d. If you knew that not all trainers had a nationally recognized certification, how would you feel about that?
5. Have you experienced any unethical behavior with a trainer?

a. If yes, what was the nature of this behavior?
b. Even if you have not experienced it, what do you consider to be unethical?

The focus group comments were recorded using a Marantz audio-recording system and videography (60 Hz). In addition to the informed consent, participants also signed a confidentiality agreement within the group. The confidentiality statement included the investigator’s agreement not to disclose names, as well as the participants’ agreement not to disclose or discuss what was said in the interviews with other participants or individuals outside the designated focus group time. Furthermore, anonymity was assured by removing participants’ names on the final transcripts, and by replacing real names with pseudonyms (see Table 1). A moderator’s guide, (29) was used in each of the focus groups. The focus groups lasted approximately 2 hours with an emphasis on each participant getting equal amounts of speaking time (29).

#### Statistical Analyses

The focus group audio tapes were transcribed verbatim. The three investigators read and re-read each of the three transcripts and searched for key phrases emerging from the data. Key phrases were defined as those that occurred at least five times within the transcript, as the three investigators concurred that this arbitrary number was sufficient to denote a key phrase. The investigators converted the key phrases into codes and then examined the transcripts line by line, inserting the codes where appropriate. After consensus was reached concerning the coding of each line of transcript, the codes were entered into Ethnograph©, a computer program used for qualitative data analysis. In order to determine credibility and reliability, three different readers were used, bringing their varying perspectives to the group. All three read the transcripts, as well as reviewed the audio- and videotapes. This lessened the risk of allowing the primary investigator’s biases to strongly affect emergent themes. A bracketing interview was also completed to lessen this risk. In a bracketing interview, the primary investigator was asked the same questions her participants would be asked, and she answered them from her own perspective and in as much detail as possible. This was in order for her biases as a former personal trainer to be made clear to her and to the other investigators. Throughout data collection and analysis, the interview was referred to, so that her biases would not override the actual perceptions of the participants. Additionally, a member check was employed; the investigator sent a one-page summary to the participants and asked for feedback and any clarifications and/or additions they would like to make. Trustworthiness of data was established through two methods of triangulation: three data collection methods, and three different perspectives concerning the research question. The data collection methods were the focus group interview, the videotape, and the survey.

### Results

The results are reported by themes that emerged from each research question. Figure 1 depicts the hierarchical organization of the clients’ responses into global, major, and sub, and mini-themes. The global themes and their sub-factors are described therein.

#### Personal Trainer Rationale

The first global theme that emerged from the client focus group was Personal Trainer (PT) Rationale which refers to the clients’ reasons or motivations for hiring a personal trainer. Participants in the focus group provided a rich and detailed account of their motives for hiring a personal trainer. The discussion of PT Rationale produced two major themes, including Frustration and Motivation. The clients expressed frustration over their inability to achieve fitness and/or physical appearance goals, such as weight loss, muscular strength, or just the ability to fit into certain clothes. Lorraine stated,

> I just got sick of the way I looked in the mirror naked. I didn’t like the way clothes fit; I didn’t like becoming a plus-size girl at 21 years of age. And, once at the gym, I asked to use the body fat percentage machine. [As the trainer] gave it to me, I was voicing my frustration and he said something about, ‘Oh, you need to lift’ and I [said], ‘Great, I’ll be here in the morning’. And that’s how I got started.

The clients also reported a desire to work with someone who could help them sustain motivation. Clients felt they could not generate the motivation necessary to adhere to regular exercise, and wanted a trainer to motivate them to work harder during a workout session. To illustrate, Carla said that her biggest problem was just getting herself to the gym: “Motivation for me, and for probably most of the population that’s overweight, [is] what they need”.

In summary, it appears that the clients’ incentives for seeking a personal trainer originated from the negative effect or frustration associated with their failure to achieve fitness/physical appearance goals. Additionally, they sought personal trainers to maintain their motivation once in an exercise program. These major themes led to a sub-theme, Body. Clients were frustrated with their physical appearance, and they expressed the need to hire a personal trainer who would help to create the motivation required to change their bodies and to achieve results (e.g., lose weight, gain muscle tone). Once the decision to hire a personal trainer was made, the clients used certain criteria to evaluate potential trainers in order to select someone who most suited them. These criteria are considered next.

#### Selection Rationale

A second global theme for the clients of personal trainers was labeled Selection Rationale (see Figure 1). While PT Rationale examines the reasons clients sought a personal trainer, Selection Rationale refers to the attributes the client considered when evaluating a particular personal trainer. This theme includes first impressions and characteristics that clients would be able to readily observe prior to hiring the trainer. The major themes associated with Selection Rationale are Gender, Empathy, Physique Appearance, and Results of Others. Interestingly, four of the women preferred a female trainer because they felt a woman would be better suited to understand their struggles and comfort levels. Specifically, these women chose a female trainer because they felt that they would not be as self-conscious about their bodies as they might be while working with a male trainer. They also indicated that a female trainer would be prepared to understand their gender-role concerns (e.g., balancing a toned body with a feminine image). Cassie believed that a female trainer would not make her feel self-conscious in the beginning, while she was still at a body size that was undesirable to her. Alicia associated high volume weight lifting with male trainers and that this would “make her own body get too big”. [Both clients later hired male trainers and found that this was not the case]. Lorraine preferred a male trainer because she felt that she would feel the need to compete with a female trainer, though this individual did not elaborate on the meaning of “compete.” In light of the importance of physical appearance relative to reasons for hiring a trainer, it is plausible to suggest that Lorraine felt like she would compete with the trainer in terms of physical appearance. In summary, it appears that gender may play a major role when clients select a particular trainer. Female clients expressed a preference for female trainers because they believed female trainers would empathize with them more than a male trainer could. In fact, the clients discussed empathy to such an extent that it was designated as a major theme.

Empathy refers to the trainer’s understanding of the client’s experience and her skill in effectively listening to their difficulties. Several clients preferred trainers who have personally experienced the challenges associated with weight loss and adhering to an exercise program. Alicia commented, “I knew I wanted someone who had lost the weight, who knew what it felt like to struggle…I wanted someone who felt that [way] to train me”. Whitney commented,

> “I chose the person that I was with because of her [the trainer’s] own personal body change. I was watching her modify her diet and … all the training that she did and just seeing the difference in her own body… I just felt like she could achieve that with anyone who wanted to.”

These clients believed that if a trainer could feel what the client was going through (emotionally and physically), it would not only make the client feel more comfortable during the training session, but would also give the client confidence that they could achieve their own goals.

In addition to empathy and gender, the clients evaluated potential trainers based on the trainer’s physique. Physique Appearance, a third major theme, was discussed at great length and in much detail among all of the clients. The clients believed that a trainer who has a “good body” gave them confidence that the trainer “knew their stuff”. Furthermore, the clients believed that a trainer with an attractive physique must be motivated to be healthy, so they must possess the skill to motivate others. Whitney commented, “… how they look is important to me because I have to be able to put my faith in them and know that they know what they’re doing. . .”

The clients equated having a sculpted physique with competence. At the same time, several clients did recognize that mere physical appearance was not sufficient to indicate knowledge of personal training. Interestingly, the clients clearly identified empathy as a critical factor in selecting a trainer (i.e., the trainer feels or has felt the frustration associated with maintaining an exercise program), yet they also identified the appearance of the trainer’s physique as an important factor. After probing this issue, the clients concluded that for a first impression, the appearance of the trainer’s physique is important, but other factors may overcome this first impression. Clients felt that as long as they saw results with their own bodies, their trainer’s physique would become much less of a factor. Alicia commented,

> “I think that in the beginning, I would be apprehensive [with an overweight trainer]. But I wait and see what kind of change I get after working out with that person for, say, 3 months. In the long run, it’s the changes that I make and the goals that I reach …that’s going to keep me coming back- not their credentials, not what they wear, not what they look like”.

The interviews also revealed that the results that other clients achieved with a personal trainer were more important than the trainer’s physique. The major theme, Results of Others, refers to the results (e.g., successfully achieving changes in physical appearance or fitness) that other clients have achieved while working with a particular trainer. Three of the clients explained that this was one of the major reasons they chose their trainers. Carla commented, “I think that seeing the results that they’ve accomplished with someone else is as important to me as their credentials.”

#### Loyalty Rationale

Another global theme that emerged during the client focus group was Loyalty Rationale (see Figure 1), which refers to the credentials of a personal trainer that solidify the client/trainer relationship. These qualifications were not necessarily known before the client started working with her particular trainer, but they were the reasons the client stayed with the trainer. This global theme included the following major themes: Social Skills; Individuality; Education; Passion; and Results. Social Skills refers to the interpersonal and communication skills of the trainer, as well as the friendships that sometimes result with the one-on-one training. Effective interpersonal skills (e.g., charisma, sincerity) can lead to deeper, satisfying relationships (e.g., friendship) in one-on-one training. The clients noted that they like a trainer who could give them a good workout, yet who made it fun. They enjoy the camaraderie they have with their trainer, and it gives them the motivation to come every session. Carla commented,

> “…I think they should be enthusiastic, I think they should be fun. I mean, that hour is torture sometimes. And I think they have to encourage you…talking to him [trainer] and hanging out while we’re working out, is just as important probably- actually more- important than working out!”

Individuality was another major theme that emerged. It consists of two sub-themes: Full Attention and Documentation. Full Attention refers to the clients’ desire for the trainer’s complete focus and attention during their training session. Cassie commented, “I just think it’s very important to not only [oversee] training [for] the individual, but to make them feel special, make them feel that you want to be there”. Although the clients realize that their trainer has other people that she or he trains, during their hour they want to feel that they are the only client the personal trainer has.

The clients also preferred trainers who could listen closely to their concerns and make notes (e.g., programmatic changes) of what was accomplished during the session. Documentation was a sub-theme of Individuality. The clients felt very strongly that the trainers should keep formal records of what happened during each training session in order to keep track of the workouts so that they can differentiate among all of their clients. This theme also included effective listening skills, since it was believed that this would help avoid injuries. Avoiding Injury is a mini-theme that emerged from Documentation and Full Attention. These clients believed that it is important for trainers to listen to the client and document any injuries that occur so that the trainer remembers not to do that exercise again with that particular client. In addition, clients expected trainers to ask them for an update of the injured area at a later session. Some of the clients had encountered trainers who did not seem to listen when a particular exercise resulted in pain or injury, and some suggested that this was because the trainer had taken on too many other clients.

As one might expect, the clients valued the trainer’s knowledge of anatomy, physiology, and exercise program design, which was reflected in the major theme labeled Education. This theme was discussed in terms of college and certifications. College refers to any formal training at the collegiate level that clients felt should be required of trainers. Most of the clients believed that a trainer with a college degree has a broader understanding of the body than someone without a degree. Cassie, the client who had worked with six different trainers commented,

> “I find that if I have trained with people who had a B.S., the title [in] sports medicine or a related field, [instead of] a weekend course…they have a broader, general understanding of the body besides just, ‘this is the exercise, this is how you do this’. They can give you much more advice about your nutritional needs, you know, some lifestyle changes…”

It was clear that most of the clients were more comfortable with a trainer who earned a college degree, and that most assumed that their trainers had a degree since they were seeing results.

In regard to certifications (the other sub-theme associated with Education), the clients were asked whether they knew the names of any of the certifying organizations. No clients answered affirmatively. In fact, four had not known any of the qualifications of their trainers before they hired them. The exceptions to this were cases in which the trainer had won a bodybuilding or fitness show. Lorraine commented, “In the beginning, I didn’t know [what the qualifications of my trainer were]. I just assumed that everyone was certified”. When clients were informed about the fact that trainers at some locations are not required to possess a degree or have any formal training before they take many of the certification exams, they were surprised. Alicia remarked, “I didn’t ask for their qualifications. It was through our interactions that I found out what the qualifications were. I’m sitting here thinking…when I go to a doctor, I certainly want to see their qualifications.”

Another client had also been disappointed when she discovered how “easy” it can be to acquire some of the certifications. Carla noted, “I think that a lot of these groups that certify people, it’s become more of a money game than making certain people know what they’re doing. To me, it trivializes it somewhat.” Several of the clients also recognized that some trainers elected a quick certification and were training simply to make extra money. Whitney commented, “I think somebody…who’s spent the better part of her adult life working on this kind of stuff is preferable to someone who just got certified in a weekend class.”

The discussion regarding education prompted a wide variety of comments. In the absence of any probe directly concerning college, the clients noted that a degree must be an important quality for a trainer. Although the clients were disturbed by the notion of a trainer without a degree or certification, the clients seemed to quickly dismiss this opinion in situations where the trainer is clearly dedicated to the field and loves what she or he does, regardless of degree or certification. The clients called this passion.

Passion is a major theme that refers to the trainer having a love for what he or she does, including a dedication to the profession. In fact, some of the clients decided that since having a passion for your job will probably motivate a person to become better, the passion of a trainer may be more important to the clients than their education. Carla commented, “If you have a passion for it, you’re going to have a desire to learn more, read more, and to enrich your client’s life with that.”

Although social skills, individuality, education, and passion were clearly important to these clients, detectable changes in their bodies (e.g., weight loss, improved muscle tone), or results, appeared to be the most powerful factor influencing continued work with that trainer. Results refer to the changes that the clients saw in their bodies, which is consistent with their rationale for hiring a trainer in the first place (i.e., clients hired trainers in part because of the frustration that resulted from inability to achieve significant body change). According to these clients, the results that they get from working with a particular trainer may be more important than any other qualification or characteristic a trainer may possess. Alicia reported that her trainer never told her what his credentials were and that it bothered her at first, but since she was seeing results, it seemed to matter less over time.

Negative Characteristics
Finally, the clients discussed and identified a number of negative characteristics or behaviors that might impair the personal training experience. The last global theme that emerged from the client focus group was Negative Characteristics, which consisted of the sub-themes, Unethical and Unprofessional. Negative Characteristics are characteristics that clients felt were inappropriate for trainers. These characteristics might cause a client to terminate her relationship with a trainer. In this study, Unethical refers to behavior that is sexual in nature, such as flirting and sexual comments directed at the client or any other members in the gym.
Unprofessional behavior includes canceling appointments frequently, not calling to cancel appointments, cursing, and telling clients about problems with management. In addition, these clients considered inappropriate attire worn by the trainer as unprofessional. The clients expressed discomfort with female trainers who wear sport tops and bike shorts, since it seemed to make them feel self-conscious about their own bodies. Interestingly, the clients did not discuss male trainer’s dress at length, and when it was mentioned, clients suggested that the male attire should be “tasteful” and “clean”. Cassie felt that female trainers are more likely to wear inappropriate clothing. The female clients seemed to take it as a personal affront when their female trainers dressed in revealing clothing because it made the clients feel self-conscious about their own bodies. In other words, they want their trainer to have a great body, but they also want it covered. Additionally, the clients do not want to hear sexual comments made by their trainers, specifically male trainers. As Table 2 demonstrates, these clients were very clear regarding gender roles in the workplace; females should not show off their bodies, and males should not make sexual innuendos.

### Discussion

The purpose of the present study was to examine clients’ perceptions regarding the qualities of successful personal trainers. Using focus group methodology, four global themes emerged: Personal Trainer Rationale, Selection Rationale, Loyalty Rationale, and Negative Characteristics. Table 2 summarizes these results.

The clients in the present study identified several factors they considered when selecting a personal trainer. The clients preferred a trainer who could empathize with their struggles to adhere to an exercise program, help them lose weight, and improve their bodies. In addition, the trainer’s physique was important when selecting a particular trainer. These findings are in line with self-presentation theory (16), a process by which one monitors and controls how one is perceived by others. Research examining self-presentational processes in physical activity has typically focused on social physique anxiety, a perception that others are negatively evaluating one’s physique (12). The findings of the present study seem to indicate that self-presentational processes may influence the selection of a personal trainer. That is, clients’ perceptions of their own physical appearance in relation to that of a potential personal trainer may influence the selection of that trainer.

The finding that physical appearance was a major factor regarding the selection and hiring of personal trainers, as well as why people decide to exercise in the first place, mirrors contemporary society’s emphasis on the “body beautiful”. People want their bodies to emulate those seen on magazine covers and on television, and therefore seek out trainers who also have these sculpted bodies to train them. Additionally, because attractiveness is more central to women’s identity (11), women are more dissatisfied with their bodies than are men (26).

Although a trainer’s physique was an important factor in the selection of a personal trainer, the clients agreed that other factors may become more relevant (e.g., detectable changes in fitness level and physique) as they progress with their exercise program. The participants indicated that perhaps the most powerful factor when selecting a trainer is that of observing the results a trainer has accomplished with other clients. This is a factor that may lead to or be associated with false assumptions. First, it is possible that a trainer with a lean, athletic, muscular, and sculpted body has never had to worry about his/her weight. In light of the importance of genetics in determining body type, the trainer with the most attractive body may have always had a fit body, and never had to work to maintain or improve it. Thus, this type of trainer may not be necessarily empathetic to a client’s struggles with appearance. Moreover, a trainer may know how to train herself, but there is no guarantee that she can transform another person’s body. This may lead to unrealistic expectations for clients which may result in discontinuation of an exercise program.

Also, while people may see results from exercising (e.g., losing weight, toning muscles), there are incorrect ways to achieve these results. It is possible, for example, to severely dehydrate oneself in order to see more muscular definition, as some bodybuilders do prior to competition. Therefore, clients may obtain results, but they may not be using safe training methods. Finally, while factors such as noticeable results were important in the initial phases of evaluating potential trainers, they were not the factors that ultimately affected whether or not the client stayed with the trainer.

In regard to trainer characteristics, clients suggested that trainers should: 1) be educated; 2) recognize the individuality of each client; and 3) be able to help clients accomplish detectable body changes. In addition, they should have a passion for personal training, and make the workout enjoyable through the use of effective social skills. The importance of “fun” during a workout session corroborates Wankel’s findings (30) that the activity itself and the characteristics of the leader are significant factors that affect enjoyment and adherence to a program. One client mentioned that exercising is difficult, and that it is important for the trainer have the social skills to communicate as a friend and make the session as enjoyable as possible. Exercise is inherently a physically challenging activity. Therefore a trainer’s ability to use his or her social skills to make the training session comfortable is an important one. Clients are more inclined to continue with that particular trainer.

Thus, the clients want to work out in a socially friendly environment in order to sustain motivation. The importance of fitness professionals’ dispositions is critical. Studies examining the influence of disposition in service work (e.g., hospitality, retail) show that personality and social skills often outweigh a person’s technical ability (14,22). Collishaw et al. (7) also reported that an instructor’s genuine enthusiasm for teaching group fitness classes was perceived and appreciated by clients. Finally, clients report more positive affect and loyalty to a trainer as a result of positive body language. “Trainers should listen to [clients] and learn about who they are, what their lifestyle is like and what motivates them. This process will become easier with time and the personal trainers will develop a polished bedside manner.” (2). Clients also want to feel special during workouts and believe that the trainer has her full attention on the client, listening to them and documenting what worked and what did not in order to avoid potential injuries. This expectation for being treated as an individual (Individuality) is an example of the customer service that Americans demand from all businesses.

A trainer’s knowledge was important to the client. It did not necessarily have to be from a college degree or certification, however. As long as the trainer shows a passion for her occupation, and the client sees results with her own body, the need for other credentials may be minimized. If the clients recognize that a trainer is genuinely enthusiastic and shares continued education (e.g., reading) with her client, this may preclude the need for higher education. However, since the majority of the clients did not truly know what the qualifications of their trainers were, or any of the certification programs available, it is plausible to suggest that they also would not be certain that the information their trainer is seeking and distributing is from reputable sources.

While credentials are critical in the selection of a trainer and/or a facility, a trainer’s credentials (e.g., certification, college degree) may mean less to a client than the belief that the trainer can help the client achieve the desired results (8). Of course, this perception is based only upon what they observe (the body change of another). Clients may not recognize that people’s bodies change at different rates and in different ways due to genetic differences, time available for training, diet, and internal motivation.

The clients identified characteristics of personal trainers that they considered unprofessional and unethical. These negative characteristics may influence clients’ decisions to stay with a trainer. In some instances, this unprofessional behavior may result in a discontinuation of exercise altogether. As was previously noted, exercise adherence is quite low in the United States; unprofessional or unethical personal trainers only exacerbate this situation. While personal trainers who have sound knowledge and strong motivational skills inspire clients, those who do not possess these skills may be the reason why a person stops exercising. That is, if the client was frustrated before working with a trainer because she could not obtain desired results, or could not motivate herself to exercise, working with a trainer who displays negative characteristics may cause her to abandon exercise altogether.

Incompetent personal trainers may also hurt those trainers who are qualified and knowledgeable. Personal trainers who are not dedicated to the personal training industry or concerned with improving their skills severely damage the reputations of the qualified trainers who do an excellent job of caring for their clients and who make personal training a respected profession.

#### Limitations

Several limitations should be acknowledged. First, qualitative methods were used and therefore, the results cannot be generalized to other populations. Second, this study used only females and attitudes toward trainers may be gender specific. Third, focus group participants volunteered to be a part of the sessions, and this might have created a potential bias since these individuals may not necessarily represent all clients of personal trainers. Finally, all qualitative research is dependent on the biases of the authors that analyze the data. Although measures were taken to eliminate bias (the lead author completed a bracketing interview and three authors analyzed the data through consensus agreement), it is possible that preconceived beliefs may have influenced the analysis. Despite these limitations however, the authors believe that the results of the present study contribute to scholarly inquiry and offer some important practical applications for the fitness industry.

### Application in Sport

The findings of the present study have several implications relative to the personal training industry, including a discussion of the skills and/or qualifications necessary for successful personal training. First, if personal trainers are to meet the priorities of their clients, they must learn communication skills, motivation techniques, how to treat the client as an individual, and how to design various weight training programs according to the goals of the client. They must also recognize the importance of their clients’ perceptions of training results. Also, while students who do not necessarily have an ‘ideal’ physique should not be discouraged from pursuing this career, they should be cognizant that a trainer’s physique may be a deciding factor in the hiring process.

Second, the public needs to be better informed about exercise and nutrition. Clients would also benefit from information regarding the certifications associated with personal trainers. The majority of the clients in this study had not known the qualifications of their trainer when they hired them, assuming all were degreed and certified by reputable organizations. If fitness professionals can find effective ways to inform the public regarding the selection of a qualified personal trainer, clients may be less likely to have unrealistic expectations when hiring a trainer. In addition, they may be more wary of the trainers who proclaim to be able to change their entire appearance by in a short time.

Third, the authors believe that undergraduate and certification programs should include training in the development of interpersonal skills such as active listening, empathetic communication, and strategies to enhance motivation. The findings of the present study are consistent with research showing that these techniques will positively influence exercise adherence (3). Clients in the current study sought and stayed with trainers who exhibit these skills. The authors therefore, support formal incorporation of best practices into undergraduate programs. Research has shown that using such techniques will positively influence exercise adherence (3,27,28). Additionally, the findings of the present study suggest that personal trainers need to take a more client-focused approach, treating their clients as individuals and not simply as dollar signs.

A final suggestion to strengthen the current state of personal training is to move toward state licensure. The participants in the present study were largely unaware of certification procedures and the multiple licensing agencies. Currently, there are at least 19 different personal trainer certification organizations (1), and approximately 90 organizations offering fitness certifications (31). With so many organizations having their own criteria for membership and certification as a personal trainer, there has been little regulation or assurance that personal trainers working in the field are qualified. It is critical that present and future club members improve their knowledge of how professional personal trainers are educated and certified. Given the poor exercise adherence and high level of dropout rates in the United States, qualified personal trainers are in a position to help change these rates.

### References

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4. Centers for Disease Control [Internet]. Physical activity and health [cited 2010 Sept 15]. Available from: <http://www.cdc.gov/nccdphp/sgr/pdf/execsumm.pdf>.
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7. Collishaw, M.A., Dyer, L., & Boies, K. (2008). The authenticity of positive emotional displays: Client responses to leisure service employees. J Leis Res, 40(1): 23-46.
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10. Dishman, R.K. (1990). Determinants of participation in physical activity. In: Exercise, fitness and hhealth: A consensus of current knowledge. Bouchard, C., Shepard, R.J., Stephens, T., Sutton, J., & MMcPherson, B., eds. Human Kinetics, Champaign, IL. 75-101.
11. Gupta, M.A. & Schork, N.J. (1993). Aging-related concerns and body image: Possible future implications for eating disorder. Int J Eating Disorders, 14: 481-486,
12. Hart, E.A., Leary, M.R., & Rejeski, J.W. (1989). The measurement of social physique anxiety, J Sport and Ex Psychol, 11: 94–104.
13. International Health Racquet & Sport Association Web Site [Internet]. Boston (MA): [cited 2010 Sept 2]. Available from: <http://cms.ihrsa.org/index.cfm?fuseaction=Page.viewPage&pageId=18859&nodeID=15>
14. King, A.C., Stokols, D., Talen, E., Brassington, G.S., & Killingsworth, R. (2002). Theoretical approaches to the promotion of physical activity: Forging a transdisciplinary paradigm. Am J Prev Med, 23(Suppl 2): 15-25.
15. Korczynski, M. (2002). Human resource management in service work. Palgrave, Basingstoke.
16. Leary, M.R. (1992). Self-presentational processes in exercise and sport. J Sport Ex Psychol, 14: 339-351.
17. Maloof, R.M., Zabik, R.M., & Dawson, M.L. (2001). The effect of use of a personal trainer on improvement of health related fitness for adults. Med Sci Sports Exerc, 33(5): 74.
18. Mazetti, S.A., Kraemer, W.J., Volek, J.S., Duncan, N.D., Ratamess, N.A., Gomez, A.L., Newton, R.U., Hakkinen, K., & Fleck, S.J. (2000). The influence of direct supervision of resistance training on strength performance. Med Sci Sports Exerc, 32(6): 1175-1184.
19. McGuire, A.M., Anderson, D.F., & Trail, G. (2009). Examination of consumer differences on the importance and satisfaction with fitness service attributes. Int J Sport Mgmt, 10(1): 102-119.
20. Melton, D., Katula, J.A., & Mustian, K.M. (2008). The current state of personal training: an industry perspective of personal trainers in a southeast community. J Strength Cond Res, 22 (3): 883-889.
21. Melton, D., Dail, T.K., Katula, J.A., & Mustian, K.M. [in press]. The current state of personal training: Managers’ perspectives. J Strength Cond Res.
22. Nickson, D., Warhurst, C, & Dutton, E. (2004). Aesthetic labour and the policy-making agenda: Time for a reappraisal of skills. SKOPE Research Paper 48, Oxford and Warwick Universities.
23. Quinn, E. Do you need a personal trainer? 10 reasons a trainer may be right for you. About.com Guide. Available from: <http://sportsmedicine.about.com/cs/strengthening/a/012004.htm>. Accessed February 16, 2010.
24. Ratamass, N.A., Faigenbaum, A.D., Hoffman, J.R., & Kang, J. (2008). Self-selected resistance training intensity in healthy women: The influence of a personal trainer. J Strength Cond Res, 22(1): 103-111.
25. Redding, J.L. (1994). A descriptive study of personal trainers. Columbia University Teachers College. Diss Abst Intl 55(07A): 1871.
26. Rozin, P., & Fallon, A. (1988). Body image, attitudes to weight, and misperceptions of figure preferences of the opposite sex: A comparison of men and women in two generations. J Abnormal Psych, 97: 342-345.
27. Sallis, J.F., Hovell, M.F., Hofstetter C.R., Faucher, P., Spry, V.M., Barrington, E., & Hackney, M. (1990). Lifetime history of relapse from exercise. Addictive Behav, 15: 573-579.
28. Turner, R.D., Polly, S., & Sherman, A.R. (1976). A behavioral approach to individualized exercise programming. In: Counseling Methods. Krumboltz, J.D. and Thoresen, C.E., eds. Holt, Reinhart, and Winston, New York.
29. Vaughn, S., Schumm, J.S., & Sinagub, J. (1996). Focus group interviews in education and psychology. Sage Publications, Thousand Oaks, CA.
30. Wankel, L.M. (1985). Personal and situational factors affecting exercise involvement: The importance of enjoyment. Res Q Ex Sport, 56(3): 275-282.
31. Williams, A. (2009). Personal trainer certification. IDEA Fit J, 6: 2.

### Corresponding Author

**Deana I. Melton, Ed.D., CSCS, HFS**
Human Performance and Leisure Studies Department
North Carolina A&T State University
203 Corbett Center
Greensboro, NC 27411
Phone: (336) 334-7712
Fax: 336) 334-7258
<dimelton@ncat.edu>

U.S. Sports Academy2016-04-01T09:36:46-05:00January 4th, 2011|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology, Women and Sports|Comments Off

Experimental and Numerical Study of the Flow Past the Olympic Class K-1 Flat Water Racing Kayak at Steady Speed

Georgios D. Tzabiras, Stylianos P. Polyzos, Konstantina Sfakianaki, Vasilios Diafas, Athanasios D. Villiotis, Konstantinos Chrisikopoulos and Sokrates Kaloupsis

### Abstract

The present work is concerned with the study of the hydrodynamic performance of an Olympic class “K-1” flat water racing Kayak. The evaluation of the hydrodynamic resistance of the vessel is of major importance since it is directly related to the human power required to sustain a specific speed. In this respect, experiments in calm water and regular waves were conducted at various speeds past the particular boat at the towing tank of the Laboratory for Ship and Marine Hydrodynamics (LSMH) of the National Technical University of Athens (NTUA). The calm water tests were performed in the range of speeds from 0.25 to 5m/s and useful conclusions were drawn concerning the influence of the wave formation on the non-dimensional resistance coefficients. Experiments in regular waves were carried out for two characteristic speeds and showed an increase of the hydrodynamic resistance of about 11%. Furthermore, systematic numerical tests using advanced computer codes developed at LSMHE have been performed in order to investigate whether Computational Fluid Dynamics (C_FD) tools can be applied with confidence for predicting the calm water resistance of similar vessels. The scope of this part of the investigation is related to a rapid and cost-effective optimization of the shape of the boat. The computed results for the total resistance were in satisfactory agreement with the measurements, thus forming a basis for further investigation and deeper understanding of the athlete-boat interaction, especially for high performance and competition boats.

Under this study, every coach may form the way his athlete paddles, taking into consideration the hydrodynamic resistance during a canoe – kayak race with or without head waves. Additionally, this investigation is important for the canoe – kayak boat manufacturers since they can improve the boat shapes using existing C_FD tools and taking into account the resistance increase due to waves.

**Key words:** racing-kayak, resistance, experiments, potential, RANS

### Introduction

The scope of the present work is to investigate the hydrodynamic behavior of an Olympic class K-1 Flat Water Racing Kayak boat at steady forward speed. In a first approximation, the complicated roll and yaw motion of the boat caused by the rower is simplified by regarding only the forward component including free heave and trim. The athlete is in any case replaced by a constant weight about his/her mean centre of gravity. The study includes both experimental and numerical tests. Basically, the aim of the experimental program was to measure the total resistance of the Kayak, covering a speed range of 0.25 to 5.15 m/s, at the towing tank of the Laboratory for Ship and Marine Hydrodynamics (LSMH) of the National Technical University of Athens (NTUA). The tests took place during the last week of January 2009. First, experiments were carried out in calm water at various speeds. Similar tests have also been performed by towing tanks past other types of vessels, e.g. (3). Next, the particular boat was tested at two characteristic speeds in low regular waves which were produced by the wave generator of the tank. These tests were made in order to assess the increase of the hydrodynamic resistance and the corresponding power which is required to sustain the particular speeds.

On the other hand, the dramatic development of Computational Fluid Dynamics (C_FD) provides a valuable alternative for evaluating the hydrodynamic behavior of floating bodies. Many research groups have developed advanced computer codes which numerically solve the flow field around complicated geometries. So far, most of the applications are concerned with flows about ships and try to overcome the problem of extrapolating the towing tank measurements to full scale. However, this is not the case in the particular study because the real vessel is tested in the towing tank and, therefore, the experiments predict accurately its hydrodynamic behavior. The main reason for performing C_FD tests is to evaluate the codes that have been developed at the LSMH in order to use them in a future optimization procedure regarding the shape of the boat. The application of reliable C_FD tools requires substantially less cost than constructing various models and testing them in a towing tank, since the most favorable shapes can be detected numerically and then a limited number of experiments has to be carried out. In the present investigation two methods have been examined to calculate the boat resistance at steady forward speed; a non-linear potential flow solver as well as a Reynold’s Averaged Navier-Stokes (RANS) solver. Both of them are applied for the first time past the Kayak boat and useful conclusions are drawn.

### Methods

#### Experimental Procedures

All the experiments were performed in the towing tank of the LMSH. The dimensions of the towing tank are 91 m (effective length), 4.56 m (width), and 3.00 m (depth). The towing tank is equipped with a running carriage that can achieve a maximum speed of 5.2 m/s. The tank is also equipped with a wave generating paddle (wave maker), located at one end of the flume. At the opposite end there is a properly shaped inclined shore, for the absorption of the waves. The wave making facilities can produce both harmonic and pseudorandom waves, in the frequency range from 0.3 to 1.4 Hz. The corresponding significant wave height can reach the level of 25 cm.

The hull provided by Pan-Hellenic Kayak and Canoe Trainers Association (PA.SY.P.K-C) was an Olympic class flat water racing Kayak, K-1 category, which refers to a single-seat boat, having the athlete paddling in a seated position. The weight category of the boat is M (medium), corresponding to an athlete’s weight in the range of 70 to 80 kg.

Minor alterations on the internal structure of the model were applied prior to the measurements, in order to accommodate the measuring equipment. This work was supervised by the personnel of PA.SY.P.K-C.

Both experimental and numerical tests were carried out with the boat having a displacement of Δ=86.8 kg (condition A). This is the sum of the bare hull weight with the added fixtures (11.8 kg) and the mean athlete’s weight, the last taken as 75 kg for the present study. The longitudinal position of the center of gravity (LCG) was chosen at the middle of the athlete’s seat. For the experiments, the rod of the resistance dynamometer was mounted on the hull at this location. The mounting was done using a heave rod – pitch bearing assembly, which allows for the vertical motions and trim angles (heave and pitch responses) of the boat.

The resistance measurements were performed for speeds in the range from 0.25 to 5.15 m/s, for the case of calm water and for two speeds (2.5 and 5.0 m/s) for the case of harmonic waves, (5). All the tests were performed in fresh water, at a temperature of 15 oC.

The boat resistance, the rise of the center of gravity (c.g.), the dynamic trim and the towing speed of the model were recorded during the runs on calm water. In this investigation, trim is defined as the signed rotation about the transverse axis passing through the c.g. and is considered positive when the bow of the kayak sinks. In addition, for the case of harmonic waves, the wave elevation was measured using wave probes.

#### Data Analysis

In order to investigate whether C_FD tools can be applied with confidence to predict the calm water resistance of similar vessels under the scope of hull optimization, systematic numerical tests were carried out by applying the non-linear potential flow solver (7,8), as well as the RANS solver (6,8), both developed at LSMH.

The potential method is based on constant source quadrilateral panels that cover the wetted surface of the boat and the real free-surface (Figure 1). The latter is found by an iterative procedure which, after convergence, leads to the satisfaction of both the well known free surface conditions: the kinematic and the dynamic. The potential flow predicts the wave making component C_W, whereas the total resistance coefficient C_T is calculated by adding the corresponding 1957 International Towing Tank Conference (ITTC’57) value for the skin friction coefficient C_F.

![Quadrilateral panels on the hull and water surface for the potential calculations.](/files/volume-13-number-4/1/figure-1.jpg “Quadrilateral panels on the hull and water surface for the potential calculations.”)
**Figure 1** Quadrilateral panels on the hull and water surface for the potential calculations.

Naturally, this procedure suffers from the potential flow drawbacks, i.e. the predicted wave pattern near and after the stern does not include any viscous effects. Besides, the so called form-resistance component including the skin friction alteration due to the shape of the hull and the viscous pressure component cannot be taken into account. These shortcomings disappear when the RANS equations are solved numerically. The latter, however, requires substantially higher computing power and time since a three-dimensional grid discretisation is required, Figure 2.

The employed method uses an H-O type numerical grid which is adjusted to the free-surface as the solution proceeds (6). To account for turbulence effects, the well known k-ε model with wall functions (2) is adopted.

![Numerical H-O type grid for the RANS calculations.](/files/volume-13-number-4/1/figure-2.jpg “Numerical H-O type grid for the RANS calculations.”)
**Figure 2** Numerical H-O type grid for the RANS calculations.

### Results

#### Calm water experiments

Calm water resistance tests were done for the speed range of 0.25 to 5.15 m/s. The experimental results concerning the calm water resistance, the CG rise, the dynamic trim and the towing speed of the kayak are presented in Table 1. The corresponding graphs for the resistance, dynamic trim and CG rise are presented in Figs. 3 to 5, respectively.

As observed in Fig. 4, the dynamic trim is negligible in the range of speeds 0-2.5 m/s while it increases rapidly after it, resulting in an increase of the draft at the stern and a raise of the bow. The CG –rise, Fig. 5, is always negative resulting in an increase of the mean vessel’s draft which presents a peak about the speed of 3.5 m/s. This behavior could be associated with the dynamic trim change and shows that the behavior of the boat is very sensitive with respect to the speed.

![Total Resistance](/files/volume-13-number-4/1/figure-3.gif “Total Resistance”)
**Figure 3** Total Resistance

![Dynamic Trim](/files/volume-13-number-4/1/figure-4.gif “Dynamic Trim”)
**Figure 4** Dynamic Trim

![C.G. Rise](/files/volume-13-number-4/1/figure-5.gif “C.G. Rise”)
**Figure 5** C.G. Rise

In order to study the usual Froude decomposition of the total resistance coefficient versus speed, the relation between the total resistance coefficient (CT) and the Froude number (Fn) is, firstly, depicted in Figure 6. These parameters are defined by the following relations:

![Formula 1](/files/volume-13-number-4/1/formula-1.gif)

![Formula 2](/files/volume-13-number-4/1/formula-2.gif)

where V_S stands for the speed, g is the gravitational acceleration, L the waterline length, R_T the total resistance, ρ the water density and WS the wetted surface.

In the calculation of the total resistance coefficient, the wetted surface used was the one calculated by means of the potential method. The variation total resistance coefficient vs. Fn, presented in Fig.6, shows that it is influenced strongly by the wave formation. The main hump is located in the region of Fn 0.4÷0.45, i.e. it is moved to the left with respect to the predicted one by the linear wave theory (about 0.5) (4). However, the prismatic hump is missing while a “hollow” appears about Fn=0.3 which is moved to the right with respect to the predicted one by the linear wave theory (about 0.24), while the higher values at the low Fn show a dominant effect of skin friction.

![Total resistance coefficient.](/files/volume-13-number-4/1/figure-6.gif “Total resistance coefficient.”)
**Figure 6** Total resistance coefficient.

According to the standard Froude approach, the total resistance coefficient can be decomposed into the friction (CF) and the residual (CR) components as:

![Formula 3](/files/volume-13-number-4/1/formula-3.gif)

The friction coefficient (CF) can be calculated by the ITTC’57 formula as:

![Formula 4](/files/volume-13-number-4/1/formula-4.gif)

where

![Formula 5](/files/volume-13-number-4/1/formula-5.gif)

represents the corresponding Reynolds number, L is the immersed waterline length and ν the kinematic viscosity.

Furthermore, the residual resistance may be regarded as equal to the so-called wave-making resistance CW, i.e. CR ≈ CW. The three coefficients with respect to the Froude number are presented in Table 2. The negative or very low values of CR at the lower Froude numbers show that the skin friction formula rather over-predicts CF and, therefore, an extended laminar region may cover the front part of the vessel. It should be noted here that no turbulence stimulators were applied since the real hull was tested. The slender form of this hull should result in a thin boundary layer region over the major part of the wetted surface, thus permitting the existence of a laminar zone especially at low speeds, which in any case is favorable because it leads to a reduction of the total resistance.

The residual resistance coefficient, plotted vs. Fn in Fig. 7, shows similar trends with Fig. 6 and influences accordingly the total coefficient. CR is comparable to CF after Fn=0.3, but in any case is lower than that, implying that skin friction plays an important role for the total resistance. This trend is due to the very slender form of the particular boat which was designed to produce low waves, as far as possible.

![Wave, Pressure and Residual resistance coefficients.](/files/volume-13-number-4/1/figure-7.gif “Wave, Pressure and Residual resistance coefficients.”)
**Figure 7** Wave, Pressure and Residual resistance coefficients.

#### Potential results

In order to validate the use of the non-linear potential solver (7) for the examined type of vessel, systematic numerical tests were conducted for the same speed range as the experiments.

The solver has been developed at the LSMH and solves the wave problem by covering the hull and the free-surface with quadrilateral panels. The hull geometry is represented by the conformal mapping approach which exhibits the advantage of a fast and effective reconstruction of panels as the free-surface changes. A special feature of the code is the calculation of the free-surface by combining an integral with a differential method. The total number of panels used was 12,000 while the trim angle as well as the dynamic rise of the c.g., were calculated numerically. The potential results of the examined cases are shown in Table 3. Essentially the method predicts only the wave resistance component CW, while CF is derived under the ITTC’57 skin friction approximation. The predicted CW is compared to the measured one in Fig. 7. Evidently it exhibits the same variations, but it is lower than the experimental in the whole range of Fn. This is an expected behavior according to the aforementioned shortcomings. The potential theory predicts higher waves at the stern region, resulting in increased pressures underneath the stern that in turn lead to a reduction of the total wave resistance. However, the total resistance coefficient appears closer to the experimental in Fig. 6 where the skin friction was added. This is reflected also to the calculation of the total resistance (which is the meaningful quantity) in Fig. 3, where the calculated results are in satisfactory agreement with the measurements up to the speed of 3.5 m/s (~7%) while deviations increase at higher speeds.

#### RANS results

In order to explore the possibility of obtaining better results at high speeds with RANS computations, three test cases were examined, corresponding to the speeds of 3, 4 and 5m/s. The relevant code has also been developed at the LSMH and, unlike other methods, uses the concept of orthogonal curvilinear co-ordinates to solve the viscous flow equations. This feature is beneficial for obtaining effectively converged solutions. The free-surface is calculated iteratively by applying a surface-tracking method that has been developed for the first time in (6).

In any case the grid size had 2.65 million grid points. To reduce the computation cost as well as the uncertainties related with the longitudinal position of the center of gravity, the trim angle of the vessel was taken from the experiments while it was assumed free to heave. The results acquired via the RANS solver are shown in Table 4. First, it is important to notice that the calculated skin friction coefficient CF is in very good agreement with the empirical ITTC’57 formula in Table 2, which justifies the relevant assumption when the potential method is adopted. The calculated values of the total resistance coefficients are presented in Table 4. Evidently, the total resistance is predicted with satisfactory agreement with respect to the experimental values for the examined speeds. The larger deviation at the highest speed may be a result of the extended wave breaking which was observed during the experiments in this case, which cannot be simulated numerically. The deviations percent of the calculated vs. the experimental total resistance is depicted in Table 5 for both methods, where the superiority of the RANS approach is obvious at high speeds.

The calculated wave patterns about the boat by the RANS computations are plotted in Figs. 8 to 10 for the speeds of 3.0 m/s, 4.0 m/s and 5.0 m/s, respectively. The full lines represent wave crests while the dashed lines correspond to wave troughs. These plots show a regular formation which is similar to the real one observed during the experiments.

![Water surface elevation contour, RANS solver, VS =2.995 m/s.](/files/volume-13-number-4/1/figure-8.jpg “Water surface elevation contour, RANS solver, VS =2.995 m/s.”)
**Figure 8** Water surface elevation contour, RANS solver, VS =2.995 m/s.

(Full lines: wave crests, dashed lines: wave troughs)

![Water surface elevation contour, RANS solver, VS =3.989 m/s.](/files/volume-13-number-4/1/figure-9.jpg “Water surface elevation contour, RANS solver, VS =3.989 m/s.”)
**Figure 9** Water surface elevation contour, RANS solver, VS =3.989 m/s.

(Full lines: wave crests, dashed lines: wave troughs)

![Water surface elevation contour, RANS solver, VS=5.153 m/s.](/files/volume-13-number-4/1/figure-10.jpg “Water surface elevation contour, RANS solver, VS=5.153 m/s.”)
**Figure 10** Water surface elevation contour, RANS solver, VS=5.153 m/s.

(Full lines: wave crests, dashed lines: wave troughs)

#### Experimental tests in regular waves

The tests in regular waves were done at the speed of 2.5 m/s for wave frequencies of 0.3 Hz, 0.5 Hz, 0.7 Hz, and 0.9 Hz and at the speed of 5.0 m/s for wave frequencies of 0.3 Hz and 0.5 Hz (5).

During the tests, the following responses were measured:

– C.G. rise
– Pitch
– Added resistance
– Wave Height

The experimental results for these tests are presented in Table 6. Based on the recorded time histories of the boat responses, the Response Amplitude Operators (RAOs) in heave (at the CG position) and in pitch motion were calculated and presented also in this Table, together with the measured values of wave amplitude and mean added resistance.

The non-dimensional RAO values were calculated using the following formulae:

– RAOHEAVE = ξ0 / ζ0
– RAOPITCH = θ / (k ξ0)

Where:

– ξ0 : heave response amplitude
– ζ0 : wave amplitude
– θ : pitch amplitude [rad]
– k : wave number (k=2π/λ)
– λ : wave length

The most important result is the resistance increase presented in the last column of Table 6. It can be concluded that the added resistance is negligible for wave lengths much larger than the boat length (low frequency range, examined frequency 0.3 Hz) and can reach values from 7 to 12% for faster waves (examined frequencies 0.5, 0.7, and 0.9 Hz) and for both wave heights. This resistance increase reflects directly on the power required by the athlete.

### Discussion

The measured total resistance coefficient shows a minimum about the vessel speed of 1.5m/s and a maximum at 3.0 m/s. These values appear as a result of the interactions of the generated wave systems about the boat. In addition, the Froude decomposition of the total resistance coefficient demonstrates that skin friction is higher than the residuary component at all speeds, while at low speeds the appearance of laminar flow regions about the bow is rather possible. Wave breaking was also observed at speeds above 3.5 m/s.

The performance of the boat subjected to low amplitude heading harmonic waves was also investigated. The main conclusion is that short waves (high frequencies) may increase the boat resistance and, therefore, the required human power by almost 10%.

The applications of the employed CFD approaches have shown that the computation of the total resistance by applying a non-linear potential flow code in conjunction with the ITTC’57 skin friction formula is in good agreement with the measured one for speeds up to 3.5 m/s. Above this level, viscous effects are dominant and RANS methods have to be employed to obtain accurate results. However, in the usual range of speeds of the particular vessel, the potential approach may produce reliable results and, therefore, can be involved in optimization procedures concerning the hull geometry.

The current investigation has been based on the fruitful collaboration of three research groups, i.e. the Laboratory for Ship and Marine hydrodynamics of NTUA, the Pan-Hellenic Canoe – Kayak Trainers Association, and the Department of Physical Education and Sport Science of the University of Athens. The groups combined their efforts for the first time, and the data acquired can form a basis for further investigation and deeper understanding of the athlete-boat interaction, especially for high performance and high competitive boats, like the case at hand. The research will be continued toward the hull optimization of the boat as well as the experimental study of the effect of the yaw and roll motions by designing the proper experimental apparatus. The numerical tools will be further developed to simulate these motions as well as to take into account the unsteady influence of waves.

### Conclusions

The systematic numerical experiments have shown that both potential and RANS methods can be applied in order to calculate the calm water resistance of a flat water racing kayak. The potential solver provided results in good qualitative agreement with the experiments and, therefore, can be involved in optimization procedures concerning the hull geometry. The RANS solver gave very accurate predictions for the total resistance and therefore can be used with confidence for predicting the resistance of vessels of similar geometry.

### Applications in Sport

In the last several years we have seen a tremendous rise in new technologies (construction materials, e.g. carbon fiber) (1) which in their way affect the increasing improvement of results in canoe – kayak. The main factor for the accomplishment of better times in canoeing is the hydrodynamic resistance of the boat’s hull. With this study, every coach may develop the way his athlete paddles, taking into consideration the hydrodynamic resistance which is observed depending on the waves appearing during a canoe – kayak race.

Additionally, this study is very important for the canoe – kayak boat manufacturers, since they can achieve the making of more improved boat hulls, taking into account the hydrodynamic resistance appearing under different types of waves.

### Acknowledgments

The authors wish to thank the personnel of LSMH and particularly Mr. I. Trachanas who has carried out the measurements in the Towing Tank as well as Mr. D. Triperinas, Ms. D. Damala and Mr. G Katsaounis for designing the experiments and interpreting the results.

The authors would also like to thank Lloyd’s Register Educational Trust (LRET), since Mr. Polyzos’ Phd studies are supported by LRET.

The Lloyd’s Register Educational Trust (LRET) is an independent charity working to achieve advances in transportation, science, engineering and technology education, training and research worldwide for the benefit of all.

### Tables

#### Table 1
Experimental results for the calm water resistance tests, condition: Δ=86.8 Κp.

Speed	Froude Number	Total Resistance (R_r)	Dynamic Trim (+) by bow, (-) by stern	C.G. Rise
m/s		Kp	deg	em
0.244	0.035	0.011	-0.029	-0.063
0.499	0.071	0.078	-0.025	-0.163
1.003	0.142	0.311	-0.007	-0.027
1.502	0.213	0.669	0.007	-0.122
2.005	0.284	1.179	0.002	-0.317
2.500	0.354	1.896	-0.043	-0.629
2.995	0.425	2.854	-0.361	-1.163
3.493	0.495	3.963	-0.628	-1.362
3.989	0.565	5.085	-0.799	-1.195
4.494	0.637	6.318	-0.866	-0.846
5.153	0.730	7.902	-0.947	-0.602

#### Table 2
Experimental results for the calm water resistance tests.

Speed	Froude Number	Total Resistance (R_r)	Total Resistance Coefficient (C_F)	Frictional Resistance Coefficient (C_T)	Residual Resistance Coefficient
m/s		Nt		(ITTC’57)	(C_R)
0.244	0.035	0.105	2.226E-03	4.606>-03	-2.380E-03
0.499	0.071	0.761	3.889E-03	3.971E-03	-8.194E-05
1.003	0.142	3.054	3.827E-03	3.470E-03	3.568E-04
1.502	0.213	6.556	3.644E-03	3.222E-03	4.216E-04
2.005	0.284	11.558	3.561E-03	3.061E-03	4.997E-04
2.500	0.354	18.588	3.651E-03	2.946E-03	7.050E-04
2.995	0.425	27.988	3.776E-03	2.856E-03	9.200E-04
3.493	0.495	38.862	3.872E-03	2.783E-03	1.089E-03
3.989	0.565	49.867	3.815E-03	2.722E-03	1.093E-03
4.494	0.637	61.952	3.710E-03	2.670E-03	1.040E-03
5.153	0.730	77.487	3.488E-03	2.611E-03	8.770E-04

#### Table 3
Numerical results for the calm water resistance tests, potential method.

Speed	Froude Number	Dynamic Trim (+) by bow, (-) by stern	C.G. Rise	Wave Resistance Coefficient (C_W)	Frictional Resistance Coefficient (C_F) (ITTC’57)	Total Resistance Coefficient (C_T)	Total Resistance (R_T)
m/s		deg	cm				Nt
0.244	0.035	-0.001	0.036	3.743E-04	4.606E-03	4.980E-03	0.235
0.499	0.071	0.001	0.022	1.305E-04	3.971E-03	4.102E-03	0.802
1.003	0.142	0.008	-0.008	6.468E-05	3.470E-03	3.535E-03	2.921
1.502	0.213	0.014	-0.112	1.079E-04	3.222E-03	3.330E-03	5.991
2.005	0.284	-0.032	-0.285	4.473E-04	3.061E-03	3.508E-03	11.388
2.500	0.354	-0.072	-0.462	4.288E-04	2.946E-03	3.375E-03	17.182
2.995	0.425	-0.352	-0.808	8.456E-04	2.856E-03	3.702E-03	27.437
3.493	0.495	-0.528	-0.761	8.367E-04	2.783E-03	3.620E-03	36.330
3.989	0.565	-0.665	-0.739	7.948E-04	2.722E-03	3.517E-03	45.974
4.494	0.637	-0.709	-0.626	6.733E-04	2.670E-03	3.343E-03	55.825
5.153	0.730	-0.828	-0.597	5.797E-04	2.611E-03	3.190E-03	70.881

#### Table 4
Numerical results for the calm water resistance tests, RANS method.

Speed	Froude Number	Pressure Resistance Coefficient (C_P)	Frictional Resistance Roefficient (C_F)	Total Resistance Coefficient (C_T)	Total Resistance (R_T)
m/s					Nt
2.995	0.425	9.001E-04	2.852E-03	3.752E-03	28.118
3.989	0.565	1.076E-03	2.717E-03	3.792E-03	50.266
5.153	0.730	7.825E-04	2.594E-03	3.376E-03	75.084

#### Table 5
Experimental results for the calm water resistance tests.

Speed	Froude Number	Deviation in Total Resistance δR_T (%)
m/s		Potential	RANS
0.244	0.035	-123.76
0.499	0.071	-5.46
1.003	0.142	7.63
1.502	0.213	8.61
2.005	0.284	1.47
2.500	0.354	7.56
2.995	0.425	1.97	-0.46
3.493	0.495	6.52
3.989	0.565	7.81	-0.80
4.494	0.637	9.89
5.153	0.730	8.52	3.10

#### Table 6
Experimental results for the tests in regular waves.

Speed	Wave Frequency	Wave Amplitude	RAO Heave	RAO Pitch	Added Resistance	Resistance Increase
m/s	Hz	cm			Kp	%
2.5	0.3	5.9	0.936	1.111	0.016	0.8
2.5	0.5	5.3	0.565	0.598	0.157	8.3
2.5	0.7	5.3	0.139	0.053	0.132	7.0
2.5	0.9	4.8	0.042	0.018	0.221	11.7
5.0	0.3	5.8	1.045	1.164	0.139	1.9
5.0	0.5	5.2	1.000	0.780	0.873	11.6

### References

Diafas, V. (2007). The sport of Canoe-Kayak and its Olympic categories: vol.1Flatwater Canoe-Kayak, University of Athens

Launder, B. E., Spalding, D. B. (1974). The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering, 3, 269-289.

Lazauskas, L., Winters, J., Tuck, E. O. (1997) Hydrodynamic Drag of Small Sea Kayaks. Retrieved from <http://www.cyberiad.net/library/kayaks/skmag/skmag.htm>

Newman, J. N. (1997). Marine Hydrodynamics. Cambridge, Massachusetts and London England: The MIT press, ISBN 0-262-14026-8.

Triperinas, D. V., Damala, D., Katsaounis, G. (2009) Report No. NAL 303 F 2009, Laboratory for Ship and Marine Hydrodynamics, NTUA.

Tzabiras, G. D. (2004). Resistance and Self-propulsion simulations for a Series-60, CB=0.6 hull at model and full scale. Ship Technology Research, 51, 21-34.

Tzabiras, G. D. (2008). A method for predicting the influence of an additive bulb on ship resistance. Proceedings of the 8th International Conference on Hydrodynamics, 53-60.

Tzabiras, G. D., Kontogiannis, K. (2010). An integrated method for predicting the hydrodynamic resistance of low-Cb ships. Computer-Aided Design Journal, Accepted for publication.

### Corresponding Author
Mr. Stylianos Polyzos
Laboratory for Ship and Marine Hydrodynamics
9 Heroon Polytechniou str. NTUA Campus, Zografos 15773, Greece
<spolyzos@mail.ntua.gr>
0030-2107721104

### Author Bios

George Tzabiras is a Professor and Head of the Laboratory for Ship and Marine Hydrodynamics at the National Technical University of Athens (NTUA).

Stylianos Polyzos and Konstantina Sfakianaki are Phd Candidates at the Laboratory for Ship and Marine Hydrodynamics.

Athanasios D. Villiotis and Konstantinos Chrisikopoulos are members of the Pan-Hellenic Canoe – Kayak Trainers Association

Vassilios Diafas and Sokratis Kaloupsis are Professors at the University of Athens, Department of Physical Education and Sport Science, Faculty of water sports

U.S. Sports Academy2015-11-08T07:40:30-06:00October 4th, 2010|Contemporary Sports Issues, Sports Management|Comments Off