A Coach’s Responsibility: Learning How to Prepare Athletes for Peak Performance

### Abstract

The coaching profession is ever-changing and coaches at each level of sport competition need to know more than just the Xs and Os in order to be successful. As the primary individuals tasked with developing athletes and helping them achieve their goals, coaches should acquire a working knowledge of all areas affiliated with performance enhancement. Specifically, the disciplines of sports administration, sports medicine, strength and conditioning, and sports psychology can assist coaches while physically and mentally training their athletes. This article illustrates six primary components of these disciplines: risk management, injury prevention, communication, nutrition, goal setting, and athlete development. It is imperative coaches gain a familiarity with these aforementioned components in order to teach athletes about skill development and prepare them to achieve peak performance.
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2018-10-22T15:29:44-05:00February 14th, 2011|Sports Coaching, Sports Exercise Science, Sports Management|Comments Off on A Coach’s Responsibility: Learning How to Prepare Athletes for Peak Performance

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

### 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 Yi, the dependent variable, is a dichotomous indicator for whether the home team won game i, Ri and Pi 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]

**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 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)

### References

Associated Press (2005). Rams Rally to Down Texans in Overtime. <http://www.tsn.ca/nfl/teams/news_story/?ID=144796&hubname=nfl-rams>, accessed August 28, 2006.

Berri, D. (2007). Back to back evaluations on the gridiron. In Statistical Thinking in Sports. Albert, J., and Konig, R.H. eds. CRC Press, Ann Arbor, MI. pp. 235-56.

Berri, D., Schmidt, M., and Brook, S. (2006). The Wages of Wins. Stanford University Press, Stanford, MI.

Boulier, B. L., and Stekler, H.O. (2003). Predicting the outcomes of National Football League games International Journal of Forecasting, 19, 257−70.

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.

2013-11-25T16:35:43-06:00February 2nd, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Is Controlling the Rushing or Passing Game the Key to NFL Victories?

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

### 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
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
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

### References

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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).

2016-04-01T09:17:20-05:00January 12th, 2011|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on An Examination of Idaho High School Football Coaches’ General Understanding of Concussion

Developing a Mental Game Plan: Mental Periodization for Achieving a “Flow” State for the Track and Field Throws Athlete

### Abstract

Athletes participating in all levels of sport experience extraordinarily high levels of stress, expectations, and physical challenges. The throws event athlete in track and field should strive to achieve an optimal state of arousal and concentration during specific competitions. A strong body of research evaluating the qualities of the flow state in athletics and psychological skills training is present in sport psychology. A practical guide for coaches to apply psychological skills training in a periodized training plan is missing. The purpose of this article is to: 1) describe a periodized annual plan for mental skills training and 2) suggest a method to interject those skills into the competition day routine to achieve flow for the track and field thrower.

**Key words:** flow, mental toughness, mental periodization, track and field, throws

### Introduction

Achieving an optimal arousal and focus state is beneficial for successful throwers. The optimal state for a thrower can be referred to as a “flow” state (6). Whereas most throws coaches are quite adept in training the necessary physiological systems, many coaches lack a proper framework for addressing psychological components. What is absent in the literature is a system of psychological preparation that is thorough enough to match the physical preparation and help the athlete to achieve “flow.”

Mental Periodization has emerged as the latest tool to help coaches prepare athletes (11). Mental periodization is a systematic mental conditioning program designed for peak performance for specific competitions. Recognition of the need of a theoretical framework for the periodization of psychological skills is easily accepted intellectually – however, the practicalities of putting this framework together for coaches have not yet been fully realized. Thus, coaches and sport psychology consultants must work together to properly implement mental periodization plans. This paper attempts to bridge the gap in building a mental periodization plan for helping track and field throws athletes achieve “flow.”

#### Flow State in Athletics

The idyllic mindset enables the body to function automatically with little conscious effort. In this optimal state, complex tasks appear to be effortlessly accomplished and time is even perceptually transcended. Coaches and sport psychology consultants often refer to this optimal mindset as a flow state or “the zone.” For some athletes, performance in the zone is achieved only a few times in their careers; however, with systematic training using mental periodization the opportunities for achieving flow state are increased. Where does this concept of a flow state fit into athletics? The body of research exploring the relationship between flow states and sport supports the notion that a flow state also acts as a peak performance state in athletics.

A flow state or “the zone” is an experience athletes get wherein everything they do seems effortless. Within the state of flow is a delicate balance between skill level and challenge (13). If the demands of an activity are greater than one’s skills, then a state of anxiety is a result. If skill level exceeds the situational challenges, boredom will result. A flow state includes the achievement of a positive state void of either of these conditions (6). The participants allow themselves to be athletes and surrender their subconscious minds to “auto pilot”. In this state, athletes produce their best performances. When an athlete is properly physically trained, the body is conditioned, and skills are well-practiced or “programmed” so that when an athlete peaks for a championship, they are in their best physical shape for the best possible performance. Flow is a transient state and it must be viewed as a process rather than an overall state to achieve (23). Trying to anticipate when flow will occur or getting into flow is not very likely because there is no direct route and often thoughts about flow distract from actually achieving a flow state (6). However, the more often athletes can create similar processes especially in practice; the more likely they are to experience this state. For instance, flow can occur by accident, but common themes are associated with optimal experience.

Csikszentmihayi lays out essential steps for producing flow based on the use of physical skills: a) set an overall goal, b) find ways to measure progress, c) concentrate on what one is doing and make distinctions in the challenges, d) develop skills necessary to interact with opportunities available, and e) raise the stakes if the activity becomes boring (6).

![Figure 1. Mental Periodization](/files/volume-13-number-4/6/figure-1.jpg “Figure 1. Mental Periodization”)

Similarly, Jackson (12) researched 16 national champion figure skaters with at least 13 years of skating experience. Results revealed four important dimensions for allowing flow states to occur. 1) Positive mental attitude: inclusion of positive thoughts, feelings of confidence, and motivation to do well; 2) Positive precompetitive & competitive affect: including feelings of being relaxed and having fun; 3) Maintaining appropriate focus: staying in the present moment; 4) Physical readiness: being well-trained.

In order to construct the optimal performance model for the thrower, a training program must be developed that systematically and progressively builds the proper physiological abilities, necessary fundamental skills, and psychological tools that will lead to the achievement of peak performance for targeted competitions (17). Coaches may get impatient with athletes because they cannot perform at full capacity during the championship season and coaches fail to realize the true cause of an athlete’s technical difficulties: the training plan. Training programs often lack carefully planned long-term goals agreed upon by both coach and athlete program through mental periodization. Sound planning is essential to elite sport performance and mental skills must be developed at the same rate as physical skills. Proper sequencing of the training effects function further contributes to sport form and peak performance (3).

#### Mental Periodization Training for Flow

Mental Periodization is intended to maximize effects of psychological and physical training. Similarly to physical periodization concepts, the goal of such programs is to focus on specific competitions, which allow the athlete to “peak” (11). Thus, mental periodization programs are centered on varying specific themes, timing, sequence, and interaction of the training stimuli to allow optimum adaptive response in pursuit of specific competitive goals (11). Each training phase addresses and reinforces specific mental skill components to enhance the opportunity for flow states to occur.

For the track and field throws athlete, mental periodization training is broken up into two macrocycles (long-term training cycles) for the indoor and outdoor season. These are further broken into mesocycles (shorter-term cycles), each lasting three to four weeks (which is the summation of training effects from various stimuli). These phases have specific themes and are designed to blend smoothly, unfolding in an evolutionary process (3).

#### Preparation Phase (General and Specific) Microcycles 1-12:

The goals of the preparation phase are two-fold; first, to develop and nourish one’s motivation; second, to help the athlete merge awareness and action of performance. During this period, the first mental skill addressed is motivation. Developing and enriching one’s motivation requires a balance of merging appropriate challenges and skills. An accurate balance puts one in a desired flow state as opposed to feelings of anxiety or boredom. For instance, if one’s skill level is (or is perceived to be) advanced for the task at hand, then boredom will result. On the other hand, if the challenge is too high, then anxiety will result. If a throw athlete and coach can effectively manage merging the challenge and enhancing the skill level on a consistent basis, then the opportunity arises for flow to occur.

Performers and coaches alike often set goals, but are often solely outcome oriented rather than performance or process based. Examples of outcome goals are frequently indicative of beating an opponent or record. However, outcome goals can often cause anxiety for performers due to the high challenge and relatively uncontrollable nature of competition. Contrarily, performance and process goals are more effective because they are based on aspects within the performers’ control. Process goals allow athletes to accept challenges more easily while not becoming burdened by feelings of anxiety. Alas, the acceptance of a process goal can also engender boredom from not having effective challenges.

An effective process goal could be to execute a certain skill three efforts in a row, or to complete a certain skill by the end of the week. Process goals should encompass every facet of training including physical and mental goals, and practice and weight training goals. Research has labeled this effective merging of challenge and skill “intrinsic motivation.” Intrinsically motivated athletes strive to master the task at hand, seek challenges, enjoy competition, and focus on the fun of the sport (26).

Feedback regarding one’s goals and challenges must be effectively monitored by both coach and athlete. For instance, Czisentmihayi (6) stated that feedback of execution must be present in order for flow to occur. Thus, allowing athletes to experience progress and results (no matter how small) presents the best opportunity for successful skill execution and optimal functioning. During the preparation phase, different types of competition can be a means of evaluating progress. For instance, throwing performance tests provide feedback, yet instead of contesting the competition implement, one might challenge athletes with a different weight implement, such as a heavy shot put. Full technique tests may be performed with overweight implements like the 5k shot put instead of the 4k shot put for women. Testing technical parts of the throw or warm-up drills, such as the standing throw, can be utilized for performance feedback. Throws may also be performed into a net to best provide appropriate “technical” or kinesthetic feedback.

Within the paradigm of flow states, merging awareness of self and action is a key component. In addition, Jackson (12) points out that physical readiness for competition is an important pre-requisite for flow states to occur. Being physically ready for competition requires an awareness of one’s ideal arousal levels. The Individual Zones of Optimal (IZOF) hypothesis of arousal proclaims that for every situation, an athlete has an optimal arousal level (10). Coaches must help athletes discover their best recipe for achieving optimal states of flow. The initial process is recognizing one’s optimal arousal level through identifying past peak experiences and past poor performances. For instance, Orlick (18) designed a competitive reflections assessment that asks the athlete to assess arousal level, cognitions, and awareness during both best and worse competitive experiences. This exercise is intended for athletes (and coaches) to begin to recognize their optimal states before and during competition. Within the twelve weeks of the preparation phase, this worksheet can be utilized every few weeks as a progress check. As the athlete reflects on more recent experiences during the start of training, he/she can begin to identify key components to successful practices.

#### Precompetitive Phase Microcycles 13-17:

The pre-competitive stage is intended to reinforce one’s capacity to concentrate effectively and create a positive effect before and during competition (12). In order to augment these skills, phase I should accomplish setting process goals, evaluating such progress, and merging one’s arousal level during competitive states. Only by thoroughly reflecting on one’s optimal arousal level can he/she recognize such barriers. For instance, perhaps outside distractions, worry about past performances, or future events cause the throw athlete to worry or to be distracted during competition. Thus, being totally absorbed in the task at hand is a requisite skill necessary for the process of flow (6).

The establishment and maintenance of a pre-competitive routine is paramount for flow to occur. Research has shown the establishment of a routine is effective for performance and the most effective means for athletes to focus on the task at hand and control arousal levels (2,10). A few components have been noted for a routine to appropriately focus one’s attention.

First, breathing and being focused on a cleansing breath prior to a throw appears to be the easiest and most effective means of a consistent routine. The acquisition of diaphragmatic breathing consists of slow, controlled breathing patterns that originate in the abdomen as opposed to the chest cavity. These types of breathing patterns are designed to reduce muscle tension, and shift one’s focus to internal stimuli of controlled respirations (20). Within the realm of competition and the pre-throw routine, the onset of the routine is an ideal opportunity to utilize a deep, controlled, diaphragmatic breath. Variations of a cleansing breath are abundant, but a common theme appears to be counting both the inhalation and exhalation to a specific count, such as four seconds each.

A second main component of a routine is to utilize proper self-talk during execution (22). Individuals possess a limited attentional capacity (24) and the attentional demands are even lower for well-learned tasks. Due to our limited attentional capacity, research has suggested that a cue word, either instructional or motivational in nature, might have a positive effective on performance (5). The majority of research has supported that an effective cue word prevents lapses in concentration due to unwarranted or noxious thoughts. Since research varies regarding whether motivational or instructional cue words are best, implementing the use of different types to discover the most individually effective is warranted. Practices can vary in which an athlete can utilize a motivational cue word such as “power,” or “release,” and other practices the athlete can focus on a technical cue such as “turn” or “tight.”

Another contribution to routines includes the practice of allowing athletes to choose pre-competitive music. Research has suggested the use of music in a variety of capacities including within pre-competition routines to help regulate arousal and concentration (14). Music has also been shown to help athletes directly with flow states (19). Recent results from Mesagano et al. (15) revealed that the inclusion of music helped facilitate performance in free-throw shooting by decreasing public self-awareness and distracting thoughts. With the availability of portable music, athletes can chose any type that pleases them, without the worry of distracting others. The implementation of a set “play-list” is an important aspect of building confidence and alleviating anxiety. In a track and field competition music can be utilized during the general warm-up but headsets cannot be brought on to the field of play; because of this rule, it is important that athletes do not become too reliant on headphones at practice.

#### Competitive Phase Microcycles 18-27:

Up to this point in training, specific processes have been implemented that increase one’s chances for flow to occur. It is important to reinforce the previous examples of process goals, awareness of optimal arousal levels, providing situations for feedback, and establishing pre-throw routines. Within the last phase of periodization, additional components as pointed out by Jackson (12), and Reardon and Gordin(23), are to reinforce the positive mental affect through confidence. Additionally, Csiksentmihayi (6) suggests developing skills necessary to interact with available opportunities.

Nearer to specific competitions, it is important to create both a physical and mental taper for the championship competitions. As mentioned previously, confidence and control are interwoven constructs. The main goal is for athletes to have confidence in aspects that he/she can control. Csiksentmihayi (6) suggests that “it is not the sense of being in control, but the sense of exercising control in difficult situations” (p.61). Within the sporting realm, the outcome is out of one’s control; however, nearer to competition the focus naturally becomes more result based, which often raises arousal levels and adds irrelevant thoughts. One avenue is for athletes to accept more responsibility and control over their pre-competitive states. One strategy may be for coaches and athletes to collaborate on practices for the day. For instance, athletes can exercise control over their preparation and coaches can reinforce confidence by helping choose components focused on athletes’ strengths.

Coaches can have a direct impact on an athlete’s confidence during this stage. Bandura (1) suggested four main sources of self-confidence including past performances (I’ve done it before) and vicarious experiences (modeling). It is important to recognize past accomplishments and goals achieved earlier in the training phases. The mere identification of progress and past performances can elevate one’s confidence.

One avenue for exploring both of these sources is to create a highlight film of successful past accomplishments. These videos can serve as compilations of specific skills, competition, and personal bests and can include music of the athletes’ choice. Templin and Vernacchia (25) created highlight films of specific basketball players’ performances and set the videos to inspirational music. Players watched themselves throughout the season and although causal relationships were not established, performance increased for most players involved in the study. The video is played often enough to provide the athlete an avenue to visualize their own success before and during competition when video is not readily available.

Mental periodization training can then involve watching the mentioned highlight performance video and utilizing the images from the video as a template for imagery. Athletes should bring all of the senses into play to recreate the video in their own minds. The effective amalgamation of senses is termed synaesthesis (17). Athletes should rehearse the sequences of their event or sport as if looking through their own eyes, noticing all the shapes, colors, and textures. Competitors should immerse themselves in the smells, sounds, and general feel of their competitive environment. After the athlete has become proficient with imagery, coaches can later implement a series of “what if” scenarios: unplanned competition situations that may include unforeseen obstacles. Athletes should be taught to use imagery to help cope with late starts, poor conditions, tough opponents, and minor mishaps during the pre-competitive phase so they are prepared for the “uncontrollables” during the competition phase. Preparing mentally for any adversity before it happens ensures that athletes will not be impacted in competition when such situations arise.

Lastly, an athlete’s self-talk during this phase is also important for the process of flow. Effectively monitoring self-talk requires a focus on the positive aspects of performance, which in turn reaffirms positive self-talk (22). Developing statements that remain positive and focused on the task at hand is important for reinforcing positive self-talk. As Gill (9) points out, one strategy is for athletes to develop pre-planned statements to help produce positive thoughts and images. Athletes can develop and experiment with various statements in practice such as “I am mentally tough,” “It’s no big deal,” and “stay relaxed.” Self instructions, or instructional self-talk, can likewise be used during practice sessions to build a technical habit or immediately before a performance to serve as a technical cue (8).

#### Preparing for Competition Day: Getting into the Flow

Coaches without the school-hired asset of a sport psychology consultant can take the initiative themselves and implement mental periodization training for flow on the day of competition. But keep in mind that competition day skills need to be introduced early in the training program and developed as the training program unfolds. Utilizing techniques that have not been properly rehearsed may be more detrimental than beneficial to the athlete’s overall performance.

Imagery and instructional self-talk can be utilized on the day of competition. Positive imagery in sports involves imagining oneself doing the needed athletic performance. Imagery can be utilized as practice between throws in a competition (8), or immediately before a competition as a cue and to increase self-efficacy (8,16). Self-instructions (sometimes called instructional self-talk), such as a shot putter saying to himself, “eyes on the spot when you throw,” can likewise be used during practice sessions to build a habit or immediately before a performance to serve as a cue. Again, due to our limited attentional capacity, having a cue word either instructional or motivational in nature may have a positive effect on performance (5). In a sport like track and field, the coach may not be within earshot of the athlete on certain competition days and may have to use hand signals, further emphasizing the need for easy and direct cue words.

The application of psychological skills for competitive situations requires the execution of the pre-competition routine (Table 2), a sound pre-performance routine, and a sound recovery/refocusing routine for use in the heat of competition (7,21). All of these routines need to be developed, utilized, and applied in a practice situation in order to be able to effectively implement them in a competitive situation. Elements of a competition day mental plan include:

– Energy Management Skills
– Checklist For Competition Day
– Mental Plan Chronology

Table 2. Sample Pre-Competition Routine for a Shot Putter

<td >

4 Hours Prior 2 Hours Prior 1 Hour Prior 30 Min. Prior 20 Min. Prior Competition Post-Competition
Video review Arrive at the competition site and set up camp Execute a series of planned walks, jogs, and skips to increase body temperature Execute a specific warm up drills to set up the technique Execute a predetermined number of warm-up throws Counts breaths in between throws to re-focus for next throw Review competitive strategy
Visualize proper technique Walk over and examine throwing venue Begin to achieve physical arousal Feel the desired body positions during the drills Count breaths in between throws to re-focus for the next throw
Review technical cues worked on in the previous week of practice Count breaths if one loses focus until concentration is once again reached Positive self-talk: no negative thoughts about or during throwing warm-up Positive self-talk: no negative thoughts about or during throwing warm-up
Positive self-talk: review all reasons why athlete should do well that meet
Count breaths if one loses focus until concentration is once again reached

A pre-competition routine may include a planned warm up, positive self-talk, a focus on performance goals, a relaxation strategy, controlling the type and amount of interaction with others, an imagery session followed by a nap earlier in the day, and monitoring fluid and food intake. Ultimately, athletes need to experiment with the pre-competition routine in practice with the guidance of the coach keeping the three skill areas of flow in mind to help the athlete evaluate the strategy.

Coaches must include relaxation strategies in two ways: on a regular basis as part of the mental periodization program and as part of a pre-performance routine. When performed regularly, relaxation techniques can reduce the physiological response to stress, prevent the cumulative effect of stress, improve memory and concentration, increase energy levels, and reduce muscle tension (4). Remember, the power of flow is a feeling that makes a difficult task fun, and daunting tasks feel manageable. The power of flow is present when your athletes have the confidence to accept their situations, when they enjoy the process, and when they have the enthusiasm needed to accomplish specific results.

### Conclusions

This manuscript represents only a modest beginning point of mental periodization training for flow (see 11 for a further discourse on the topic). Competing at a high level requires a well-planned program of physical training and technical preparation. Psychological preparation for any athletic endeavor is a complex process that involves acquiring, practicing, and applying many different specific psychological skills. Many athletes and coaches utilize training programs that concentrate too heavily on physical training. Inadequate mental preparation can easily overcome and undermine an excellent physical technical preparation. Flow, or what many experts in the field term “being in the zone”, is the goal of athletes and coaches alike. Introducing a plan to train the psychological skills along with the physical skills will take the guesswork out of performing to the best of an athlete’s ability when it counts in big meet situations. Preparing mentally for any adversity ensures that athletes will not be stifled in competition when unexpected stressful circumstances arise.

### Applications in Sport

This manuscript has several important implications for athletes and coaches. Dedicated and driven coaches seeking success cannot stop their knowledge base at just understanding the physical aspect of training. Sport psychology has emerged as the latest tool for helping coaches prepare athletes to edge out other competitors; however, few coaches take full advantage of psychological skills preparation. Psychological training for any athletic undertaking is a complex process that involves acquiring, practicing, and applying numerous psychological skills. Psychological training must be part of the periodized plan and must be programmed as such.

Although this paper has focused specifically upon mental periodization for the throwing events in track and field, the basic psychological concepts and practices noted have applications in numerous other sports. Other sports can benefit from development of a psychological training plan that is sequenced and unfolds in harmony with the physical training plan. That gap between the science used to develop the training program on paper and the art of maximizing the performance on the playing and practice field separates good coaches from great coaches. All coaches strive for the ability to have their athletes perform in an uninhibited, relaxed, skillful manner. Various personalities, team chemistries, motivations, and attitudes coalesce to create a series of variables to juggle. With the session plan in hand, the coach steps onto the field and begins practice. Implementing and successfully executing the plan may very well be the biggest challenge. It does not matter what is on paper if the coach cannot relate to the athletes. Understanding each individual athlete and knowing what motivates him or her is the crucial step to a great performance. Inadequate mental preparation can easily overcome and undermine an excellent physical technical preparation. Flow, or as many experts in the field term it, “being in the zone,” is the goal of athletes and coaches alike. Introducing a plan to train the psychological skills along with the physical skills throughout the year will take the guesswork out of performing to the best of an athlete’s ability when it counts in big competitions.

### References

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### Corresponding Author
Dr. Larry Judge
Ball State University
School of P.E., Sport, and Exercise Science
HP 213
Muncie, IN 47304, USA
(765) 285-4211
<LWJudge@bsu.edu>

### Author Bios

Lawrence W. Judge, PhD, is an associate professor and coordinator of the graduate coaching education program in the School of Physical Education, Sport, and Exercise Science at Ball State University. Prior to arriving at Ball State, he was an NCAA Division I track and field coach for the 18 years and coached 100 All Americans and 8 Olympians. Dr. Judge is currently the president of the National Council for the Accreditation of Coaching Education (NCACE).

Robert J. Bell, PhD., CC-AASP, is an assistant professor of the Sport & Exercise Psychology program in the School of Physical Education, Sport, and Exercise Science at Ball State University. He consults with high-school, collegiate and professional athletes. He specializes with the sports of baseball and golf, and with players on the PGA/Nationwide Tour.

David Bellar, PhD, is an assistant professor in the department of Kinesiology at the University of Louisiana-Lafayette. He research interests include cognition, fitness and aging as well as human performance. Dr. Bellar also serves as the throws coach for the Cajun track and field team.

Elizabeth Wanless, BA., is a currently a graduate student at Ball State University. Liz was an alternate on the 2008 United States Olympic team in the shot put and participated in the 2005 World Championships in Helsinki, Finland. She finished 6th in the 2008 World Athletics final and finished the 2008 season ranked 20th in the world.

2013-11-25T16:56:10-06:00October 4th, 2010|Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Developing a Mental Game Plan: Mental Periodization for Achieving a “Flow” State for the Track and Field Throws Athlete

Closing Address

Dear participants and friends, with the conclusion of the works of the 10th Joint International Session for Presidents or Directors of National Olympic Academies and Officials of National Olympic Committees, I would like to express my gratitude for your presence in the International Olympic Academy and my conviction regarding our future cooperation for the propagation of the Olympic Education and the management of crisis and challenges in the sports world and the Olympic Movement.

The National Olympic Academies and the National Olympic Committees constitute the two pillars for the cultivation and the dissemination of the Olympic Ideal in cooperation with the International Olympic Academy and the International Olympic Committee. As Henry Tandau aptly mentioned in this room, you are “the key players in the development and spread of Olympic Education,” and we must have a common perception and try to reinforce the communication for the realization of Olympic Educational and Training Programs all around the world.

We all have to realize that, in order to achieve this goal, the broader Olympic Family has to be constantly prepared. The role of the National Olympic Committees is significant for the work of the National Olympic Academies. The differences in their structures and operations should not affect, but, on the contrary, they should strengthen the common goals mentioned before.

Dear friends, I believe that the sacredness of Ancient Olympia where we are and the humanistic ideas of the Olympic Movement are the elements that will reinforce the coherence for the future course of the National Olympic Academies and the National Olympic Committees. In an era dominated by individualism and cruel economic and social competition, one could say that the topics that we discussed in this Session could probably be considered by some as utopian.

However, your presence here, the interest you all showed through your presentations, and the conclusions of the discussion groups prove the opposite. Due to my necessary absence, I didn’t have the opportunity to attend the presentations of the 26 National Olympic Academies. Nevertheless, my colleagues inform me that there is a constant and unceasing effort of continuous activities by the Olympic Academies that prove that there is will, intention, and vision.

The contemporary societies desperately need ideas and people with vision. Let us keep a vivid memory of the beauty of the landscape and of the ideas of Ancient Olympia, and let’s join our forces for the achievement of the common goals. Where there is no track, let’s trace it together as we walk. Because otherwise, “it is not only for what we do that we are held responsible, but also for what we do not do,” according to the famous words of the French dramatist, Moliere.

Dear friends, I would like to thank you all personally, both the exceptional lecturers as well as the participants, for your contributions to this session. I wish you all a safe trip back home, and I reassure you that, as IOA President, I will always unconditionally support your work.


### The Olympic Anthem

Immortal spirit of antiquity, Father of the true, beautiful, and good,
Descend, appear, shed over us they light, upon this ground and under this sky
Which has first witnessed thy unperishable fame.
Give life and animation to those noble games!
Throw wreaths of fadeless flowers to the victors in the race and in the strife!
Create in our breasts, hearts of steel!
In thy light, plains, mountains, and seas, shine in a roseate hue and form a vast temple
To which all nations throng to adore thee, oh, immortal spirit of antiquity!

2013-11-25T17:16:14-06:00September 9th, 2010|Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Closing Address
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