A New Method for Ranking Total Driving Performance on the PGA Tour

Abstract

The Professional Golf Association Tour (PGA Tour) currently ranks its players according to their overall Total Driving performance by adding together individual ranks for their average driving distance and for their driving accuracy percentage. However, this widely used and reported measure is inappropriate because it is based upon the addition of two ordinal-scaled measures in which the underlying differences between successive ranks are not equal. In this paper, we propose a new method for ranking golfers in terms of their overall driving performance. The method eliminates the drawbacks of previously reported measures, including the one used by the PGA Tour. Using the new methodology, we re-rank all PGA Tour golfers for the 2005 season and compare these ranks to the “official” ranks reported by the PGA Tour. In some cases, large differences in players’ rankings existed. The reasons for these differences are then discussed.

Introduction

In recent years, numerous statistical analyses have been conducted in an attempt to assess the relative importance of various shot-making skills on overall performance on the PGA Tour and among amateur golfers (Shmanske, 2000; Dorsel and Rotunda, 2001; Engelhardt, 1997 and 2002; Callan and Thomas, 2004 and 2006; and Wiseman and Chatterjee, 2006). While most of the measures that have been used in these analyses have been well-defined and widely accepted, there is one performance statistic, “Total Driving,” that has not been well-defined. This particular statistic, which combines a golfer’s (i) average driving distance and his/her (ii) driving accuracy percentage, has been operationally defined in numerous ways, but no methodologically sound measure has emerged to date. This includes the measure now being used by the PGA Tour.

In this paper, the authors propose a new statistical measure based upon standardized z-scores for ranking golfers according to Total Driving performance. This new measure eliminates the methodological drawbacks of previously developed measures by re-ranking PGA Tour golfers on their Total Driving performance during the 2005 season and comparing these rankings to the “official” PGA Tour rankings for that season.

The evolving nature of the relationship that has existed between driving distance and driving accuracy on the PGA Tour over the last sixteen years (1990-2005) was examined. Then, alternative ranking methods that have been proposed and the necessity of and the rationale for a new composite measure of Total Driving performance were discussed. Following this, the new measure can be applied to the 2005 PGA Tour season. These new rankings dramatically alter the previous ranking of many golfers on the tour. The reasons for the differences in rankings will be explored.

Distance and Accuracy on the PGA Tour: 1990-2005

The average driving distances and the driving accuracy percentages have changed significantly since 1990, with the largest changes taking place in recent years. This is shown in Table 1. The average driving distances have increased every year since 1993 and these increases have been relatively steady on a year-by-year basis, except in 2001 and 2003, when the increases were significantly higher. We surmise that technological improvements in golf balls and equipment are likely to have played a part in these two years.

A similar trend did not exist for the driving accuracy percentage. Here, the accuracy percentage steadily increased from 1990 to 1995, and then remained relatively stable over the next six-year period, only to decline dramatically in the last few years. This dramatic decline occurred at the same time that the average driving distance substantially increased. In fact, during the 2005 PGA Tour season, the average driving distance was at its sixteen year high of 288.6 yards and the driving accuracy percentage was at its sixteen year low at 62.8%.

The negative relationship between a golfer’s average driving distance and driving accuracy percentage increased in strength over this sixteen year period. As indicated in Table 1, the strength of the relationship has grown in recent years and it reached its highest level in 2005, when the correlation between the two measures was -.679.

Current Measures of Total Driving Performance

Ranking golfers on each of the two driving measures presents no problems. Driving distance is simply defined as the average number of yards per measured drive. For each golfer, these drives are measured on two holes per round. Driving accuracy is the percentage of all drives that come to rest in the fairway. However, the PGA Tour and others (for example, Engelhardt, 1997) have indicated the need for a single measure that takes into account both the driving accuracy percentage and the average driving distance. Numerous researchers have attempted to obtain such a measure; unfortunately all of the measures that have been proposed have had methodological flaws associated with them.

The most widely used measure is the one used by the PGA Tour. It is obtained by adding together the individual ranks of a golfer on each of the two measures and then obtaining a final overall ranking based upon the total score. That is, for example, a golfer who was ranked 32nd in driving distance and 42nd in driving accuracy percentage would have a total score of 32+42=74. The PGA Tour would rank such a golfer higher than another golfer who ranked, for example, 25th in average driving distance and 60th in driving accuracy percentage, since the former summated score of 74 is lower than the latter summated score of 85.

Such an approach is flawed despite its widespread use and acceptance. The major flaw is that the level of measurement of each of these two rankings (driving distance and driving accuracy) is at the ordinal level and, as such, it does not take into account the underlying differences in distances or in driving accuracy percentages. Stated differently, while the differences in successive ranks remain the same, the corresponding differences in distance and accuracy are not equal. Thus, it is not possible to add the distance and accuracy ranks directly, without loss or distortion of the underlying information.

Davidson and Templin (1986) suggested a somewhat different approach. They proposed a measure which first divided all PGA Tour players into three groups based upon their average driving distance. They then made a similar classification based upon the driving accuracy percentage. The three groups were coded as 1 (top one-third), 2 (middle one-third), and 3 (bottom one-third). To arrive at a measure of Total Driving performance, the researchers multiplied the individual coded scores of each golfer. The larger the score, which ranged from 1 to 9, the better the performance. The authors used this new measure in a multiple regression analysis in an attempt to isolate the effects of driving on overall scoring performance.

This measure was questioned by Belkin et al. (1994) because no evidence was provided to support the construct validity of the measure and because of the multiplication of the individual codes at the ordinal level of measurement.

More recently, Wiseman and Chatterjee (2006) proposed a multiplicative measure of Total Driving which ranked golfers according to the product of their average driving distances and their driving accuracy percentages. Essentially, this measure reduced golfers’ average driving distances by the proportion of times their drives did not land on the fairway. Thus, a golfer who had an average driving distance of 300 yards and an accuracy percentage of 60% would be ranked lower than another golfer who had an average driving distance of 280 yards and an accuracy percentage of 70%, since 300(.60) =180 < 280(.70)=196. This measure was found to be highly correlated with the PGA Tour measure, but subsequent analyses revealed that it was also flawed because it gave far greater weight to driving accuracy than it did to driving distance. However, unlike the two previously discussed measures, it was operationally sound in that it was appropriate to multiply the two quantities together.

In summary, different measures for Total Driving performance that have been used are all flawed, and it is difficult to justify any of them as an appropriate measure. In the next section of this paper, a new method for ranking golfers that have none of the drawbacks of the previously discussed measures will be explained.

A New Measure for Ranking Total Driving Performance

Both average driving distance and the driving accuracy percentage are ratio-scaled data. To combine these two measures into a single overall measure of Total Driving performance, the measure we propose is based upon two statistically independent standardized z-scores, one for driving distance, and the other for driving accuracy given driving distance.

In proposing such a measure, if the distance and accuracy measures are statistically independent and they are viewed as being of equal importance in driving performance, then it would be reasonable to compute the standardized z-score of each measure, and then to add these z-scores to arrive at an overall score. However, this approach does not seem reasonable in the present situation because (i) there is a strong negative correlation between driving distance and driving accuracy, and (ii) driving distance is the primary factor in determining accuracy, rather than the other way around (driving distance is primarily a function of a player’s physical strength and athletic ability). With this reasoning, we propose the following as a composite score of Total Driving:

Zsum = ZDD + ZDA|DD

where:

ZDD = Standardized z-score of
driving distance, and

ZDA|DD = Standardized z-score
of driving accuracy given driving distance.

To compute ZDD for a player, we subtract the average driving distance for all players, µDD, from the given player’s average driving distance, DD, and divide the result by the standard deviation of average driving distances, σDD. This is expressed as:

ZDD= DD-µDD
σDA|DD

Computation of ZDA|DD is a somewhat more involved procedure. We need to determine the mean or expected accuracy percentage of all golfers who drove the ball a specified average distance, DD, as well as the standard deviation of the driving accuracy percentages given the specified average distance, DD. The formulas for these are:

µDA|DD = ρσDA((DD-µDD)/σDD),
and σDA|DD = √((1-ρ2DA2

where ρ is the correlation coefficient between distance and accuracy.
The conditional standardized z-score of driving accuracy given driving
distance is then computed using the following formula:

ZDA|DD = (DA – µDA|DD) / √((1-ρ2DA2.

Statistical theory about bivariate normal distributions tells us that z-scores for distance and accuracy, ZDD and ZDA|DD, both have a mean of 0.0 and a standard deviation of 1.0. Further, the conditional z-score for accuracy given distance, ZDA|DD, is statistically independent of the z-score for driving distance, ZDD.

Because the two standardized z-score measures are statistically independent, and because ZDA|DD is an indicator of accuracy after taking distance into account, they can be added together to obtain an overall summated z-score for overall driving performance. The higher the overall value of Zsum = ZDD + ZDA|DD, the better the overall performance.

The authors will discuss in greater detail the application of this approach for ranking golfers based upon their Total Driving performance in the 2005 PGA Tour season.

Application to the 2005 PGA Tour Season

In 2005, there were 202 golfers on the PGA Tour. Detailed statistical data for these players can be found on the PGA Tour’s website (www.pgatour.com). Anderson Darling’s (AD) test was used to determine if driving distance has a normal distribution. With this test, we reject the null hypothesis that the data came from a normal distribution if the AD statistic is very large, or equivalently, if the p-value is smaller than a chosen level of significance (usually 0.05 or 5% level of significance). Our data show that the AD statistic was 0.367, which is small, and the p-value is 0.429, which is larger than the 5% level of significance. Therefore, we do not reject the hypothesis that the data came from a normal distribution.

Similarly, we used the AD statistic to test whether the driving accuracy percentage variable was Normally distributed. The AD test produced a test statistic of 0.350 with a p-value of 0.471. As a result, we do not reject the hypothesis that the driving accuracy percentages are Normally distributed. Given these results, we concluded that the joint distribution of driving accuracy and driving distance can be represented by a bivariate Normal distribution, with a correlation coefficient of ρ = -.679 between the two variables.

Next, the authors computed the values of Zsum as the Total Driving scores, and ranked these values in descending order. The scores for the top forty players in the resulting ordering, together with the corresponding PGA Tour ranks, are shown in Table 2.

As it is seen in Table 2, Tiger Woods was the number one ranked golfer in terms of Total Driving under the proposed method, which stands in sharp contrast to his rank of 83rd in the PGA Tour rankings. In terms of average driving distance, Woods was ranked 2nd in 2005 among 202 Tour players with an average driving distance of DD = 316.1 yards. The top ranked player was Scott Hend, who had an average driving distance of 318.9 yards. Woods’ average driving accuracy percentage of DA = 54.6% gave him a PGA Tour ranking of 188th on this measure. The top ranked player was Jeff Hart with a driving accuracy percentage of 76.0%. Woods’ two ranks of 2nd and 188th led to his overall ranking of 83rd for Total Driving based upon the PGA Tour method.

To illustrate the computation of ZDD , ZDA|DD , and Zsum for Tiger Woods, in 2005, the average driving distance among all players was 288.6 yards with a standard deviation of 9.32 yards. The average for the driving accuracy percentage was 62.8% with a standard deviation of 5.32%. As noted previously, the correlation between driving accuracy and driving distance was -.679. Then, the standardized driving distance z-score for Tiger Woods is:

ZDD = (316.1 – 288.6) / 9.32 = 2.95.

The conditional mean driving accuracy percentage given the average driving distance of 316.1 yards is:

µDA|DD = 62.8% + (-.679)(5.32%)(2.95)
= 52.1%.

That is, Tiger Woods or any golfer who has an average driving distance of 316.1 yards would be expected to have a driving accuracy percentage of 52.1%. Since Woods’ actual driving accuracy percentage for 2005 was 54.6%, his conditional z-score would be equal to:

ZDA|DD = (54.6 – 52.1) / √((1-(-.679)2)(5.32)2)
= .63

By adding the two z-scores for Tiger Woods, an overall Zsum score of 3.58 is obtained, which is the highest of any of the PGA Tour
players in 2005.

The rationale for Woods’ jump in the rankings can be seen by a closer examination of the z-scores. His average driving distance of 316.2 yards far outdistanced all other golfers (except one). His z-score value of 2.95 reflects this large differentiation, whereas previously his ranking of 2nd did not because it assumed that the distances between ranks were equal when they were not. Further, his conditional z-score for driving accuracy is now positive where before it was negative. The reason for this is because his relatively low driving accuracy percentage of 54.6% did not reflect at all how far Woods drove the ball. Actually, for those who drive the ball this far, a driving accuracy percentage approximately two percentage points lower could be expected. These two factors taken together accounted for his top ranking.

The Spearman rank correlation between the PGA Tour rankings and the new rankings was computed to be rs = .90 (p < .001). This shows that there was a large degree of similarity between the two rankings. On the other hand, and as illustrated by the case of Tiger Woods, there were also dramatic differences in some cases. To get a better feel for the differences, consider the scatterplot of the rankings under the two methods, which is shown in Figure 1. It is seen that the rankings under the two methods are generally similar, particularly in the middle range of rankings, but discernibly less so near the top or the bottom ranges. Divergence of the rankings at the extremes in this way emphasizes the effect of the ranking method on the results, which in turn brings the virtues and flaws of the ranking methods into focus.

Golfers whose rank improved included V. J. Singh, from 38th to 13th, Davis Love III, from 59th to 11th, and Brett Wetterich, from 73rd to 4th. Those going in the opposite direction included Marc Calcavecchia, from 21st to 45th, Jonathan Kaye, from 23rd to 44th, and Justin Rose, from 13th to 33rd. Typically, the reason for a golfer improving rank is because one of the measures was quite good and the standardized z-scores now reflect this, while the previous ranking system did not. For those golfers falling in rank, their old ranks tended to be clustered around many other golfers and their actual differences in rank did not reflect this closeness. For example, Justin Rose had a driving accuracy percentage of 63.7%, which gave him a ranking of 81st among all golfers on this measure. However, fellow competitor Marc Hensby had a driving accuracy percentage of 62.7%, just one percentage point less, yet Hensby’s rank of 102nd was 21 ranks below that of the rank given to Justin Rose.

Summary

The proposed method for ranking golfers according to their Total Driving skill takes into account the magnitude of the differences that exist between players on each of the two driving dimensions. The current PGA Tour method does not. The proposed method also takes into account the strong negative relationship that exists between driving accuracy and driving distance. This negative relationship is reflected in the new conditional standardized z-score. As a result, this new method gives a better overall reflection of the true Total Driving performance of PGA Tour golfers than does the current ranking system. Computationally, the proposed method is slightly more involved than other existing methods, but this is not a significant factor today.

It should be noted that this methodology can be applied in other areas in which an overall ranking is desired based on two correlated factors, which have different units of measurement and thus need to be combined in some way to provide an overall ranking.

References

Belkin, D.S., Gansneder, B., Pickens, M., Rotella, R. J., & Striegel, D. (1994) “Predictability and stability of Professional Golf Association tour statistics.” Perceptual and Motor Skills, 78, 1275-1280.

Callan, S. J. & Thomas, J. M. (2004) “Determinants of success among amateur golfers: An examination of NCAA Division I male golfers.” The Sports Journal 7, 3 at http://www.thesportjournal.org/2004Journal/Vol7-No3/CallanThomas.asp.

Callan, S. J. & Thomas, J. M. (2006) “Gender, skill and performance in amateur golf: An examination of NCAA Division I golfers.” The Sports Journal 8, 2 at http://www.thesportjournal.org/2006Journal/Vol9-No3/Callan.asp.

Dorsel, T.N., & Rotunda, R. J. (2001) “Low scores, Top 10 finishes and big money: An analysis of Professional Golf Association Tour statistics and how these relate to overall performance.” Perceptual and Motor Skills, 92, 575-585.

Engelhardt, G. M. (1997) “Differences in shot-making skills among high and low money winners on the PGA Tour.” Perceptual and Motor Skills, 84, 1314.

Engelhardt, G. M. (2002) “Driving distance and driving accuracy equals total driving:Reply to Dorsel and Rotunda.” Perceptual and Motor Skills 95, 423-424.

Shmanske, S. (2000) “Gender, skill and earnings in Professional Golf.” Journal of Sports Economics 1(4), 385-400.

Wiseman, F. and Chatterjee, S. (2006) “A comprehensive analysis of golf performance on the PGA Tour: 1990-2004.” Perceptual and Motor Skills, 102, 109-117.

 

TABLE 1
Driving Distance and Driving Accuracy: 1990-2005

Year Average Driving
distance (yds.)
Driving accuracy
percentage
Correlation between
distance and accuracy
1990 262.7 65.3% -.359
1991 261.4 67.1% -.306
1992 260.4 68.6% -.416
1993 260.2 68.8% -.417
1994 261.9 69.2% -.346
1995 263.4 69.5% -.457
1996 266.4 68.3% -.469
1997 267.6 68.6% -.448
1998 270.5 69.5% -.469
1999 272.5 68.4% -.471
2000 273.2 68.3% -.379
2001 279.4 68.4% -.346
2002 279.8 67.7% -.474
2003 286.6 66.1% -.612
2004 287.2 64.1% -.606
2005 288.6 62.8% -.679

 

Table 2
Revised 2005 PGA Tour Rankings for Total Driving (Top 40 players)

Player Driving
Distance
(yards)
Driving
Accuracy
(%)
Expected
Driving
Accuracy (%)
ZDD ZDA|DD ZSUM Rank PGA
Rank
Woods 316.1 54.6 52.1 2.95 0.63 3.58 1 83
Perry 304.7 63.4 56.6 1.73 1.75 3.48 2 2
Gutschewski 310.5 57.9 54.3 2.35 0.92 3.27 3 54
Wetterich 311.7 56.6 53.8 2.48 0.70 3.18 4 73
Hearn 295.2 68.5 60.2 0.71 2.11 2.82 5 1
Gronberg 301.4 63.2 57.8 1.37 1.37 2.74 6 6
Frazar 301.0 63.5 58.0 1.33 1.41 2.74 7 3
Warren 299.2 64.2 58.7 1.14 1.41 2.55 8 3
Glover 302.2 60.7 57.5 1.46 0.81 2.27 9 26
MacKenzie 300.2 62.1 58.3 1.24 0.97 2.22 10 11
Love III 305.4 57.9 56.3 1.80 0.41 2.21 11 59
Garcia 303.5 59.4 57.0 1.60 0.61 2.21 12 38
Durant 289.2 70.9 62.6 0.06 2.13 2.20 13 5
O’Hair 300.1 61.4 58.3 1.23 0.78 2.02 14 26
Singh 301.1 60.2 58.0 1.34 0.57 1.92 15 38
Long 298.3 62.4 59.0 1.04 0.86 1.90 16 13
Smith 300.8 60.2 58.1 1.31 0.54 1.85 17 44
Hend 318.9 45.4 51.1 3.25 -1.45 1.890 18 107
Hughes 291.3 67.5 61.8 0.29 1.47 1.76 19 7
Stadler 300.1 60.4 58.3 1.23 0.53 1.76 20 50
Allenby 297.7 62.3 59.3 0.98 0.77 1.75 21 20
Mayfair 288.2 69.8 63.0 -0.04 1.75 1.71 22 9
Appleby 300.6 59.3 58.1 1.29 0.29 1.58 23 61
Snyder III 291.8 66.3 61.6 0.34 1.21 1.56 24 8
Purdy 295.2 63.4 60.2 0.71 0.81 1.52 25 15
Brigman 295.5 63.1 60.1 0.74 0.76 1.50 26 18
Bryant 283.2 73.0 64.9 -0.58 2.07 1.49 27 30
Rollins 294.4 63.7 60.6 0.62 0.81 1.43 28 10
Jobe 302.3 57.3 57.5 1.47 -0.05 1.42 29 82
Brehaut 286.6 69.9 63.6 -0.21 1.62 1.40 30 17
Ogilvy 298.0 60.7 59.2 1.01 0.39 1.40 31 54
Henry 297.6 61.0 59.3 0.97 0.43 1.40 32 44
Rose 294.1 63.7 60.7 0.59 0.78 1.37 33 13
Westwood 296.8 61.5 59.6 0.88 0.48 1.36 34 43
Johnson 290.0 66.9 62.3 0.15 1.19 1.34 35 15
Senden 291.0 66.0 61.9 0.26 1.06 1.31 36 11
Mickelson 300.0 58.7 58.4 1.22 0.08 1.30 37 77
Watney 298.9 59.4 58.8 1.11 0.15 1.26 38 68
Trahan 295.8 61.8 60.0 0.77 0.46 1.23 39 33
Pappas 309.4 50.6 54.7 2.23 -1.06 1.17 40 109

Figure 1
Figure 1.
Scatterplot of Revised Rankings Versus PGA Tour Rankings

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Abstract

This paper investigates whether or not the DNF’s (those who ‘did not finish the race’) due to early life critical part failures are higher than would be expected in NASCAR vehicles. The hypothesis is that
early life critical part failures are, in fact, higher than would be expected in NASCAR vehicles. This hypothesis is based on the fact that NASCAR teams
have sizeable budgets and use only highly specialized components. In addition,
the extensive mileage typically associated with commercial vehicles is
not required of these parts. This paper develops a reliability model to
test whether the average time of failure for these critical components
is higher than what would be expected of high performance critical components.

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The Effect of Modern Marketing on Martial Arts and Traditional Martial Arts Culture

Abstract

This paper examines the effect of modern marketing strategies upon martial arts activity in the United States. The concentration of the inquiry is twofold. How has marketing effected the economic activity of the martial arts business industry? How has marketing effect the martial arts culture? This paper begins with a historical analysis of the evolution of martial arts as a business practice involving the use of marketing to gain customers. Martial arts marketing practices have proven most effective when they are personal due to the geographic location of specific schools or the instructor-client relationship. Internet marketing is a synthesis of personal and mass marketing, providing readily available information in a client’s home while offering to the martial school the potential audience of a large mass marketing campaign. Marketing has generated sufficient commercial interest in the field, transforming martial arts into a thriving business.

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Use of the Athletic Coping Skills Inventory for Prediction of Performance in Collegiate Baseball

Abstract

The Athletic Coping Skill Inventory (ACSI-28) was completed by twenty-six
collegiate baseball players. Performance statistics were collected, including
batting average (BA), number of errors committed (ERR), and earned run
average (ERA) for pitchers. Regression analysis was carried out using
the seven areas of the ACSI-28 (peaking under pressure, freedom from worry,
coping with adversity, concentration, goal setting and mental preparation,
confidence and achievement motivation, and ‘coachability’)
as the independent variables, and the current season’s performance
statistics as the dependent variables. Correlation coefficients revealed
significance between concentration, confidence, and ERA, while there were
no significant relationships with BA or ERR and any of the psychological
variables. Many of the psychological variables were highly related. While
sequential linear regression did not reveal statistically significant
relationships between the performance statistics and the psychological
variables, large effect sizes indicated a strong degree of practical significance.
Specifically, peaking under pressure and ‘coachability’ appeared
to be strong predictor variables for ERA, concentration for ERR, and ‘coachability’
for BA.

Introduction

Athletes and theorists in human performance agree on the influence of
psychological factors in the performance of motor skills, particularly
at a high level of competition. As a result, an abundance of research
has been dedicated to finding out not only how to prepare athletes mentally
for high-pressure situations, but also what psychological factors are
specifically determinants of performance. The link between research and
application is of great importance because the business of sports is at
an all-time peak and athletes from early childhood to advanced age are
seeking ways to improve their game not only physically but mentally.

The use of self-reporting instruments that indicate specific psychological
skills is widespread, especially in collegiate and professional athletics.
Because of the comparable levels of physical abilities among top-tier
athletes, coaches seek to understand which psychological factors separate
the elite from the non-elite. In sports where “choking” may
cost a player or team a championship ring or millions of dollars, it is
understandable that non-invasive, simple indicators of psychological skill
measures have become popular.

The baseball skills of pitching, hitting, and fielding are arguably as
mental as they are physical. Pressure can affect a pitcher at any point
in the game; managers and pitching coaches make it their business to “know”
which pitchers will crumble under pressure and which will rise to the
occasion. Certainly, if a method for predicting correctly the mental toughness
(coping, if you will) of an athlete was shown to be valid and reliable,
it would be of great benefit to coaches, managers, and athletes alike.

The Athletic Coping Skills Inventory (ACSI-28), created in 1988, contains
seven sport specific subscales: coping with adversity (COPE), peaking
under pressure (PEAK), goal setting/mental preparation (GOAL), concentration
(CONC), freedom from worry (FREE), confidence and achievement motivation
(CONF), and ‘coachability’ (COACH) (Smith, Schutz, Smoll,
& Ptacek, 1995). Smith and Christensen (1995) defined the subscales
as follows as they apply to the sport of baseball:

Peaking under Pressure: is challenged rather than threatened by pressure
situations and performs well under pressure; a clutch performer

Freedom from Worry: does not put pressure on self by worrying about
performing poorly or making mistakes; does not worry about what others
will think if he/she performs poorly

Coping with Adversity: remains positive and enthusiastic even when
things are going badly; remains calm and controlled; can quickly bounce
back from mistakes and setbacks

Concentration: not easily distracted; able to focus on the task at
hand in both practice and game situations, even when adverse or unexpected
situations occur

Goal Setting and Mental Preparation: sets and works toward specific
performance goals; plans and mentally prepares self for games and clearly
has a “game plan” for pitching, hitting, playing hitters,
base running, and so on

Confidence and Achievement Motivation: is confident and positively
motivated; consistently gives 100% during practice and games and works
hard to improve skills

‘Coachability’: open to and learns from instruction; accepts
constructive criticism without taking it personally or becoming upset
(p. 402).

Smith and Christensen (1995) studied the usefulness of the ACSI as a
performance prediction tool in an elite athlete population, namely professional
baseball players. The participants were 104 minor league baseball players
(forty-seven pitchers and fifty-seven position players) of the Houston
Astros organization. Participants completed the ACSI during spring training;
batting averages (BA) for the position players and earned run averages
(ERA) for the pitchers were used as performance indicators. For position
players, only CONF was a significant predictor of BA, while ERA for pitchers
correlated significantly with CONF and PEAK scores. High CONF and PEAK
scores were related to lower ERA’s. Interestingly, ACSI results
were predictive of survival in professional baseball two and three years
after the testing was conducted and ACSI predicted ERA better than coaches’
ratings of physical skill.

Guarnieri, Bourgeois, and LeUnes (1998) used the ACSI with aspiring baseball
umpires at three professional umpire training schools in Florida. They
found that the more experienced umpires used athletic coping skills more
effectively than did those in training. Little research has been done
with the ACSI recently, other than the development of a Greek version
in 1998 (Goudas, Theodorakis, and Karamousalidis), and its usefulness
as a predictive tool for success in sport may remain to be seen.

The purpose of the current study was to examine the usefulness of the
ACSI in predicting BA, ERA, and errors (ERR) for collegiate baseball players.
The seven skills identified by the ACSI at surface level appear to be
related not only to each other, but also to success in discrete motor
skills in baseball that are always performed in the context of pressure:
batting, pitching, and fielding.

Method

Participants

Participants were twenty-six collegiate baseball players from the same
team that were active players during the 2005 season (twelve pitchers,
thirteen position players, and one pitcher/position player). The players
signed a consent form that assured them that their responses would only
be used for research purposes and would not be seen by any member of the
organization or any other individual other than the investigators. None
of the athletes had played baseball professionally.

Procedure

The ACSI (see Appendix) was distributed to the players at a regular meeting
of the team and instructions were read by the investigator. After the
participants signed and returned an informed consent form, they completed
the ACSI-28. Participants were instructed to consider each item and answer
without consulting any other individuals. The procedure took about ten
minutes, and all participants completed the instrument as instructed.
Each participant also indicated on the instrument his/her position, year
of eligibility, and scholarship status (full, partial, or none). Statistics
from the 2005 baseball season were collected; batting average (BA), number
of errors committed (ERR), and earned run average (ERA) for pitchers were
computed.

Statistical Analysis

The statistical analyses were carried out in three stages using SPSS
version 13.0 for windows (SPSS, 2004). First, data screening and descriptive
statistics were completed to examine participant characteristics. Regression
analysis was carried out using the seven areas of the ACSI (COPE, PEAK,
GOAL, CONC, FREE, CONF, and COACH), as the independent variables, and
the current season’s earned run average (ERA05), and batting average
(BA05) as the dependent variables. The primary outcome measures were analyzed
using three separate regression analyses. Differences (p values)
of less than .05 were considered statistically significant.

Results

After data collection, all variables were entered for analysis and screened
to determine if statistical assumptions were met. This screening included
examinations for distribution linearity and outliers. All statistical
assumptions were met for the variables.

In the current study, baseball players were broken down by position, scholarship,
and class level. Of this group, 54% were pitchers (n = 14), 23% were infielders
(n = 6), and 23% were outfielders (n = 6). Only one athlete did not receive
a scholarship; 85% percent of the athletes were on partial scholarships
(n = 22), and 11% were on full scholarships (n=3). Lastly, 27% were freshman
(n = 7), 19% were sophomores (n = 5), 19% were juniors (n = 5), and 35%
were seniors (n = 9). When examining the relationships between variables,
Pearson Product moment correlation coefficients revealed significance
between CONC, CONF, and ERA05, while there were no significant relationships
with BA05, ERR05, and any of the independent variables (Table 1). For
the psychological skills variables, COPE was significantly related to
PEAK, GOAL, and CONC. PEAK was significantly related to CONC and FREE.
Lastly, CONF, COACH, GOAL, and CONC were significantly related. These
correlations were moderately correlated, and ranged from r = 0.444 – 0.541
(see Table 1).

Table 1. Descriptive statistics and correlation coefficients between
ACSI variables and performance statistics.

Variable M SD AVG04 AVE05 ERA05 ERR05 COPE PEAK GOAL CONC FREE CONF COACH
BA05 0.30 0.13 0.50 —-
ERA05 6.98 2.70 0.32 NA —-
ERR05 4.00 3.99 NA 0.34 NA —-
COPE 2.04 0.48 -0.34 -0.13 -0.16 0.03 —-
PEAK 2.41 0.57 -0.34 -0.19 -0.23 -0.03 .521* —-
GOAL 1.74 0.71 -0.19 -0.30 0.11 -0.17 .541* 0.32 —-
CONC 2.41 0.41 -0.19 -0.17 -0.08 -0.41 .444* .606* .485* —-
FREE 1.74 0.73 0.08 -0.01 -0.12 -0.10 0.22 .447* 0.02 0.33 —-
CONF 2.63 0.39 -0.24 -0.02 0.22 0.14 -0.07 0.31 0.01 0.13 .408* —-
COACH 2.52 0.48 0.25 0.31 0.37 0.23 -0.13 0.17 -0.10 0.05 0.31 .408* —-

*p<.05

Sequential linear regression was used to determine significant psychological
predictors of ERA05 , ERR05, and BA05. There was not a statistically significant
relationship among the predictors and ERA05, F(7,6) = .507, p
= .802. A large effect size was evident, R2 = .37, indicative
of a strong degree of practical significance. Peaking and coaching appear
to be stronger predictor variables, each uniquely accounting for 5% of
the variance in the model (see Table 2).

Table 2
Results of Multiple Regression Analysis

Variable B SE B ß sr2
Regression for ERA
coping with adversity 0.53 3.06 0.13 0.00
peaking under pressure -2.24 3.04 -0.54 0.05
goal setting/motivation 0.39 2.28 0.10 0.00
concentration -0.26 2.50 -0.06 0.00
freedom from worry -0.41 1.80 -0.12 0.01
confidence 1.86 3.67 0.45 0.03
‘coachability’ 2.02 2.84 0.47 0.05
Regression for Errors
coping with adversity 4.77 4.22 0.74 0.07
peaking under pressure 3.25 3.08 0.67 0.06
goal setting/motivation -0.98 2.44 -0.18 0.01
concentration -11.45 3.95 -1.87 0.49
freedom from worry -0.25 2.58 -0.05 0.00
confidence 0.82 2.76 0.16 0.01
‘coachability’ 3.77 2.58 0.72 0.12
Regression for Batting Average
coping with adversity 0.19 0.18 0.84 0.10
peaking under pressure -0.09 0.13 -0.51 0.04
goal setting/motivation -0.08 0.10 -0.39 0.05
concentration -0.01 0.17 -0.03 0.00
freedom from worry 0.03 0.11 0.16 0.01
confidence -0.09 0.12 -0.48 0.05
‘coachability’ 0.14 0.11 0.79 0.15

There was not a statistically significant relationship among the predictors
and ERR05, F(7,7) = 1.46, p = .315. A large effect size
was evident, R2= .59, indicative of a strong degree of practical significance.
CONC was the strongest predictor, uniquely accounting for 49% of the variance
to the model. COACH was also a strong predictor, uniquely accounting for
12% of the variance to the model. COPE uniquely accounted for 7% of the
variance to the model. PEAK uniquely accounted for 6% of the variance
to the model.

There was not a statistically significant relationship among the predictors
and BA05, F(7,7) = .60, p = .745. A large effect size
was evident, R2 = .37, indicative of a strong degree of practical
significance. COACH was the strongest predictor, uniquely accounting for
approximately 15% of the variance to the model. COPE uniquely accounted
for 9% of the variance to the model. GOAL and CONF each uniquely accounted
for 5% of the variance to the model.

Discussion

The results of this exploratory study indicate that the usefulness of
the ACSI in predicting performance outcomes in collegiate baseball may
be of benefit. Due to the small sample size of this study, coupled with
the large number of predictor variables, no statistical significance was
found in any of the relationships. However, the large effect sizes for
all three criterion variables were indicative of a strong degree of practical
significance. Specifically, concentration appears to be strongly related
to errors, and ‘coachability’ to batting average. To even
a casual observer of baseball, this observation may seem to be simply
common sense. The usefulness of the ACSI-28 may be designed for managers
of relatively young teams where batting order, starting positions, and
pitching strategies have not yet been determined. If a coach knows (with
some certainty) which players are can be coached and which can maintain
high levels of concentration, the coach’s decisions can be based
more on fact than feeling. Please note that the use of the ACSI does not
guarantee success of the athletes who complete it or coaches who make
decisions based on it. However, I strongly suggest that managers take
advantage of these findings and add the ACSI-28 to their arsenal for strategic
decision-making.

Future research in this area should focus on obtaining larger sample
sizes. An increase in statistical power would likely identify statistically
significant relationships, given the meaningfulness of the predictor variables
in this study.

References

Goudas, M., Theodorakis, Y., and Karamousalidis, G. (1998). Psychological
skills in
basketball: Preliminary study for development of a Greek form of the Athletic
Coping Skills Inventory-28. Perceptual and Motor Skills, 86(1),
59-65.

Guarnieri, A., Bourgeois, T., and LeUnes, A. (1998). A psychometric
comparison of
inexperienced and minor league umpires
. Paper presented at the meeting
of the Association for the Advancement of Applied Sport Psychology, Hyannis,
MA.

Smith, R. E., and Christensen, D. S. (1995). Psychological skills as
predictors of
performance and survival in professional baseball. Journal of Sport
and Exercise Psychology
, 17, 399-415.

Smith, R. E., Schutz, R. W., Smoll, F. L, and Ptacek, J. T. (1995). Development
and
validation of a multidimensional measure of sport-specific psychological
skills: the Athletic Coping Skills Inventory-28. Journal of Sport
and Exercise Psychology
, 17, 379-398.

SPSS Version 13.0 [Computer Software]. (2004). Chicago, IL: SPSS.

Appendix

ACSI SURVEY
NAME:
POSITION: OF INF P C
YR: F SO JR SR
SCHOLARSHIP: NONE PARTIAL FULL

0 = ALMOST NEVER, 1 = SOMETIMES, 2 = OFTEN, 3 = ALMOST ALWAYS

  1. On a daily or weekly basis, I set very specific goals for myself that
    guide what I do. 0 1 2 3
  2. I get the most out of my talent and skills. 0 1 2 3
  3. When a coach or manager tells me how to correct a mistake I’ve
    made, I tend to take it personally and feel upset. 0 1 2 3
  4. When I am playing sports, I can focus my attention and block out distractions.
    0 1 2 3
  5. I remain positive and enthusiastic during competition, no matter how
    badly things are going. 0 1 2 3
  6. I tend to play better under pressure because I think more clearly.
    0 1 2 3
  7. I worry quite a bit about what others think about my performance. 0
    1 2 3
  8. I tend to do lots of planning about how to reach my goals. 0 1 2 3
  9. I feel confident that I will play well. 0 1 2 3
  10. When a coach or manager criticizes me, I become upset rather than
    helped. 0 1 2 3
  11. It is easy for me to keep distracting thoughts from interfering with
    something I am watching or listening to. 0 1 2 3
  12. I put a lot of pressure on myself by worrying how I will perform.
    0 1 2 3
  13. I set my own performance goals for each practice. 0 1 2 3
  14. I don’t have to be pushed to practice or play hard; I give 100%.
    0 1 2 3
  15. If a coach criticizes or yells at me, I correct the mistake without
    getting upset about it. 0 1 2 3
  16. I handle unexpected situations in my sport very well. 0 1 2 3
  17. When things are going badly, I tell myself to keep calm, and this
    works for me. 0 1 2 3
  18. The more pressure there is during a game, the more I enjoy it. 0 1
    2 3
  19. While competing, I worry about making mistakes or failing to come
    through. 0 1 2 3
  20. I have my own game plan worked out in my head long before the game
    begins. 0 1 2 3
  21. When I feel myself getting too tense, I can quickly relax my body
    and calm myself. 0 1 2 3
  22. To me, pressure situations are challenges that I welcome. 0 1 2 3
  23. I think about and imagine what will happen if I fail or screw up.
    0 1 2 3
  24. I maintain emotional control no matter how things are going for me.
    0 1 2 3
  25. It is easy for me to direct my attention and focus on a single object
    or person. 0 1 2 3
  26. When I fail to reach my goals, it makes me try even harder. 0 1 2
    3
  27. I improve my skills by listening carefully to advice and instruction
    from coaches and managers. 0 1 2 3
  28. I make fewer mistakes when the pressure’s on because I concentrate
    better. 0 1 2 3

‘Sponsorship in the Trenches’: Case Study Evidence of Its Legitimate Place in the Promotional Mix

Abstract

An important theme in sponsorship literature involves its definition and its place in marketing theory. With regards to the latter, differing opinion exists as to whether sponsorship is a subset of advertising or whether it is a distinct member of the promotional mix. This research adopts a case study approach to argue that sponsorship should be viewed – both in marketing theory and in business practice – as a distinct and legitimate member of the promotional mix. The subject of the case is KMAC, a sports marketing agency specializing in sponsorship. Results support sponsorship’s inclusion in the promotional mix and outline future research.

Introduction

Sponsorship is a promotional practice that has evolved from its roots as a tool for corporate donations (Wilkinson, 1993) to a highly-developed course of action by which both the sponsor (investor) and the sponsee (property) benefit in a marketing relationship (Polonsky & Speed, 2001). Its rapid adoption into practice by organizations is reflected by the huge growth of worldwide sponsorship investments, which went from US $500,000 in 1984 to what was expected to reach US $28 billion in 2004 (IEG, 2003). This impressive growth in practice, however, has not been matched by theory development. Although a difficult concept to define, the majority of the definitions in related literature are relatively similar (Olkkonen, 2001) but sponsorship’s role in relation to other resource and promotional generating strategies (i.e. philanthropy, advertising, cause-related marketing) remains unclear (e.g. Polansky & Speed, 2001). In particular, sponsorship’s position in marketing’s traditional promotional mix ranges from no inclusion at all (e.g. the vast majority of marketing and marketing communications texts) to recognition that it is an integral part of the communications mix – alongside publicity, public relations, sales promotions, personal selling, and advertising (Crimmins & Horn, 1996; Meenaghan, 2001; Tripodi, 2001; Crompton, 2004). This is supported by a number of studies suggesting that sponsorship plays an important role in supporting an organization’s attainment of its communications objectives (e.g. awareness, reach target markets).

The purpose of this research is to provide evidence to previous academic work (e.g. Meenaghan, 1991; Tripodi, 2001) arguing for sponsorship’s inclusion as a legitimate member of the promotional mix by presenting the case of a successful Canadian sport marketing firm.

Sponsorship and the Promotional Mix

Sponsorship growth is credited, in part, to the increased amount of clutter found in traditional media towards the end of 1970’s (Otker, 1988; Meenaghan, 1991; Sandler & Shani, 1993; Wilkinson, 1993). The increase in the number of television and radio networks available created added clutter in the marketplace and the competition between advertisers to attract consumers’ attention was fierce. As a result, the effectiveness of the ability of advertising to reach consumers was questioned (Howard & Crompton, 1993). For many, sponsorship became an effective and less costly alternative to break through the clutter in order to reach specific targets (Mullin, Hardy & Sutton, 2000). In this regard, a number of studies supporting sponsorship’s distinction from advertising have emerged in the literature emphasizing that it (i) functions differently, (ii) is perceived by audiences differently, and (iii) includes the ‘association’ element that advertising does not (see Javalgi, Traylor, Gross, & Lampman, 1994; Hoek, Gendall, Jeffcoat & Orsman, 1997; Bloxom, 1998).

Nevertheless, as companies’ investments in sport sponsorship increased, so did the need to justify its “Return-On-Investment” (ROI). Thus, the establishment of clear and measurable objectives was identified as important to sponsorship programmes. Irwin and Asimakopoulos (1992) suggest that corporations attempt to achieve objectives that are corporate in nature or specific product/brand related. Similarly, Sandler and Shani (1993) identify three groups of sponsorship objectives: broad corporate objectives (image related), marketing objectives (brand promotions, sales promotions), and media objectives (reach target markets, cost efficiency). Overall, much of the research on sponsorship objectives tends to be dominated by corporate image and/or public goodwill categories (Otker, 1988; Witcher, Craigen, Culligan, & Harvey, 1991; Kuzma, Shanklin, & McCally, 1993).

Berrett (1993) suggests that sponsorship is often used by corporations to achieve multiple objectives. While others, such as Irwin and Sutton (1994) and Crompton (2004), agree, Crompton (2004) proposes that enhancing profitability by generating additional sales remains the ultimate goal of a total communications strategy. Thus, it could be argued that sponsorship plays a strategic role in communicating an organization’s objectives.

The remarkable increase in the number of sport properties available and the number of sponsors investing in sport properties suggest that sponsorship is able to assist a company to achieve its corporate and marketing objectives. This, in turn, would lend support to Tripodi’s argument (2001) that sponsorship (as a promotional tool) has become one of the top promotional considerations for marketers. A well-planned and coordinated approach to communications appears essential if sponsorship is to be effectively integrated into other marketing activities. Keller (2003) suggests that event sponsorship provides an interesting communication option for a company, as the brand becomes engaged during a “special and relevant moment in consumers’ lives” (p.315). In this way, how is sponsorship different from advertising?

As noted by Brooks (1994), a key challenge for sponsorship is to provide evidence that it is more effective than advertising or sales promotion. According to Meenaghan (2001), advertising seeks to “exploit” emotion, while sponsorship strives to “connect” with the emotion inherent in the property (e.g. sport). The association between the two parties (sponsor and sponsee) is often felt to be the key differentiator from advertising as it enhances the relationship beyond a basic cash purchase of promotional value (e.g. McCarville & Copeland, 1994). For Crimmins and Horn (1996), sponsorship is a way to persuade consumers indirectly through an association with a property. If used strategically, it is suggested that a sponsorship programme can be developed into a distinctive competence, thus creating competitive advantage (Amis, Slack & Berrett, 1999).

Gaining a competitive advantage is becoming more of a challenge within a cluttered market place in which there are increased incidences of ambush marketing (Sandler and Shani, 1998; Hoek and Gendell, 2001; Crompton, 2004; Seguin, Teed & O’Reilly, 2005). This has led corporations to explore new ways to activate (leverage) their investments and maximize sponsorship return. Tripodi (2001) suggests that firms should employ an integrated approach and use sponsorship with other elements of the communications mix (publicity, advertising, sales promotion, personal sales). A synergetic effect will not only maximize communications effectiveness, but also contribute to building brand equity (Tripodi, 2001; Keller, 2003). The literature provides evidence that sponsorship’s membership in the promotional mix should be legitimate, however, uncertainty remains. The current research involves a case study designed to further support this literature.

Case Methodology

The case study presented in this paper was selected from a number of in-depth interviews conducted by the authors as part of a major research project currently underway in Canada. More than thirty-five sport properties, corporate sponsors, and sport marketing firms took part in this research, which examined a number of questions related to sponsorship acquisition, sponsorship objectives, sponsorship leveraging, and sponsorship evaluation. The case presented herein comes from a successful Canadian marketing firm called K.Mac & Associates (K.Mac) that specializes in sport sponsorship. The analysis of this case will shed some light on sponsorship’s true strategic position with respect to the promotional mix.

The data was collected through an in-depth semi-structured interview over two hours with Keith McIntyre, the founder and CEO of K.Mac & Associates, on January 26th, 2005. Scripted questions and open discussion took place, with the entirety of the conversation being recorded, transcribed, and analyzed. In addition, secondary data was obtained in the form of case studies, marketing strategies, and websites.

The Company: K.Mac & Associates

K.Mac & Associates (K.Mac) was founded in 1992, at a time when sponsorship in Canada experienced significant growth. For the first few years, K.Mac worked closely with a number of professional hockey players with the main objective of maximizing potential revenues from promotional activities. The firm quickly expended its services and has since worked with hundreds of Olympic and professional athletes, properties, sponsors, and events, including Major League Baseball, hockey superstar Mark Messier, Procter & Gamble (for eleven years), the National Football League, Pfizer (for five years), UPS, the Canadian Olympic Committee, General Mills (for seven years), and the National Hockey League. The founder attributes much of K.Mac’s success to being a “marketing resource company with sport as the playing field, and not a sport marketing firm” (McIntyre, personal communication, Jan. 26, 2005). Sponsorship “leveraging” comprises a significant portion of its business, driven by the firm’s focus on ‘superior execution’ and ‘measurable outcomes linked to objectives’ which K.Mac can perform at a level of detail and execution that a large agency cannot.

Specific to sponsorship, K.Mac follows a detailed, focused four-step process of ‘relevance, differentiation, integration, and activation’ in which the client’s objectives and motivations (either sponsor or sponsee) are researched in detail and followed through on each step. In implementing its process, K.Mac has assembled a tool box of key leveraging tactics that it customizes based on the situation. Further, it has developed its own evaluation methodology based on incremental sales: “we are tracked on incremental units…I can tell you the incremental sales we’ve provided to our clients” (McIntyre, personal communication, Jan 26th, 2005). Being able to track sales has been identified as a key factor by K.Mac, if sponsorship is to be taken as a serious component of the promotional mix. Shank (2005) noted that the eventual objective for nearly all organizations involved in sponsorship programmes is an increase in sales. This seems to be supporting Crompton’s (2004) argument, which suggested that enhancing profitability by generating additional sales remains the ultimate goal of sponsorship. However, measuring increases in sales that are directly linked to a sponsorship remains a challenge.

Results: ‘Sponsorship in the Trenches’

Given that the initial investment only buys the rights to an association, it has been suggested that sponsors “leverage” their sponsorship by spending resources into additional communications/promotional activities (Meenaghan, 1991, 1998, 2001; Sandler & Shani, 1993, 1998; Amis et al.; 1999; Tripodi, 2001; Crompton, 2004; and Seguin et al., 2005). This can be achieved by using a variety of marketing communications elements (Ludwig & Karabetsos, 1999; Meenaghan, 2001). By supporting sponsorship with other marketing components such as advertising, sales promotion, point-of-purchase, on-pack signage, and production of merchandise, a corporation may be in a better position to claim its space in an increasingly cluttered sponsorship environment (Séguin, 2003). It has been suggested that the sums required for successful leveraging may be up to three to five times the initial expenditure (Abratt & Grobler, 1989, Witcher et al., 1991; Shanklin & Kuzma, 1992). Such an approach to sponsorship was found to be embraced in practice by K.Mac: “sponsorship is not a money grab…it is about business, selling product. I work in the trenches and that is where it is” (McIntyre, personal communication, Jan. 26, 2005).

Given sponsorship’s varied roots in philanthropy and advertising, it is interesting to note that, according to K.Mac, sponsorship is still misunderstood within corporate Canada, especially within the advertising industry. For many, sponsorship is no different than advertising and, in fact, is treated as advertising:

I’ve got clients that disagree, one says ‘if I want foot-steps in my store I’ll put my most popular product on sale’…this is not helpful except for awareness building. We are interested in effecting consumer behaviour. What is the incremental [sales] volume? That is what we want…to drive business” (McIntyre, personal communication, Jan. 26, 2005).

While more research is needed to identify the reasons for this lack of differentiation between sponsorship and advertising, it is suggested that large advertising agencies understand advertising and public relations, but not necessarily the role of sponsorship. As a result, “they may fail to see that sponsorship meets the needs of sponsor and sponsee driven by the passion of both the property and the consumer, as opposed to an advertising deal which is one directional” (McIntyre, personal communication, Jan. 26, 2005). Accordingly, it is suggested that a number of marketing executives working for sport properties also have advertising backgrounds.

To further support this argument, K.Mac. provides an example of how sponsorship has been used to ‘ambush’ an advertising campaign from a competitor. While in the past, ambush marketing research has primarily examined the effects of ‘ambushers’ (eg. corporations not having official rights) over official sponsors, it is suggested that ambush may be examined from a different perspective. For example, a company that acquires the rights to sponsor a National Olympic Committee (General Mills in Canada), begins negotiation with the official broadcaster (CBC) to buy all advertising inventory within the specific product category. As the bidding with a competitor (Kellogg) intensifies, the company (General Mills) pulls out just prior to the deadline. While the competitor may have won the television advertising rights for Olympic coverage, the official sponsor has the opportunity to ‘ambush’ the advertiser by focusing its efforts at the retail: “let them [competitor] buy advertising and let sponsorship with its direct, authentic association ambush [the advertising]” (McIntyre, personal communication, Jan. 26, 2005). In this context, sponsorship is the only way to have “authentic” association and sponsors must be strategic in providing promotional programs that take advantage of this ‘authenticity’. This strategy was used by General Mills during the 2000 and 2002 Olympic Games. A Canadian Olympic Committee sponsor, General Mills leveraged its sponsorship by sponsoring “Team Cheerios” which consisted of a group of selected Olympians featured on cereal boxes (Cheerios). This included pictures, bios, and personal stories of the athletes. This was an excellent way for consumers to discover the athletes and ‘connect’ with them on a personal level. While competitor Kellogg’s cereal brand “Victor” secured television advertising rights for both Olympics, General Mills’ strategy was focused at the retail. “We worked closely at developing relationships with key accounts at the retail, making sure they understood that we held the authentic association with the Olympic rings, the Games and the athletes, we owned the space!” (McIntyre, personal communications, Jan.26, 2005). General Mills’ approach to Olympic sponsorship was fully integrated into the marketing and promotional mix. This included product packaging (integrating rings and athlete profiles on boxes), pricing (special pricing leading up to and during Olympics), distribution (working with key retail accounts, developing in-store positioning) and promotional mix (developing sales promotion campaigns, athletes’ appearances, personal selling programs, advertising, publicity). The promotional campaign provided something “meaningful” to consumers and received tremendous publicity. This approach to sponsorship enables associations linked to emotions and passion, as opposed to signage or rink boards. Meenaghan’s (2001) work on sponsorship effects on consumers supports the importance of the emotional connection in sponsorship. When asked to summarize his view on sponsorship in the promotional mix, K.Mac clarified:

I look at sponsorship as part of the promotional mix – and as one of the four or five components of how you build a communications program. Signage [advertising] used to be a big deal but that is now just wallpaper. Sponsorship now is about defining your consumer (demographics plus psychographics, what makes them tick). Then you know what they want and can identify a sponsorship that meets those criteria. Then, you build that link by telling them why it is important to them [consumer]. You also need to tell them [consumer] on their own terms. Get them excited and meet their needs and wants. It has to get really deep these days to really actually make it work (McIntyre, personal communication, Jan. 26, 2005).

Clearly, such an approach makes sponsorship, like advertising, as valid a promotional tool as any member of the promotional mix. In fact, the evidenced above is supporting that in some cases, it may be a superior communications choice when the objective is to drive sales. In this regard, K.Mac points out: “our Litmus test is [to ask] what will sponsorship do to effect consumer behaviour? What will it do to meet the client’s pillars [objectives] of evaluation?” (McIntyre, personal communication, Jan. 26, 2005).

The case of K.Mac provides additional support for sponsorship as a legitimate member of the promotional mix. First, as suggested in the literature, a key benefit associated with sponsorship is the ability to target specific markets (Sandler & Shani, 1993; Irwin & Sutton, 1994; Mullin et al., 2000; Shank, 2005). This is supported in this case, as K.Mac suggests “Sponsorship is very target driven…corporations want to communicate with their core targets” (McIntyre, personal communication, Jan. 26, 2005).

Second, sponsorship’s ability to focus on the exact objectives of the sponsor was highlighted. “Companies do not care about what is going on behind the scenes. They are very sophisticated. Signage…is not enough. Brand managers’ careers are on the line, they do not take big risks and throw up signs; they want return (McIntyre, personal communication, Jan. 26, 2005). A number of authors (e.g. Arthur, Scott, Woods, & Booker, 1998; Amis, Slack & Berrett, 1999; Fahy, Farrelly, & Quester, 2004) identify sponsorship’s ability to achieve specific objectives as an efficient way by which to differentiate a sponsor from competitors.

Third, the ability to build a promotional campaign around a sponsorship was highlighted and the need to leverage a sponsorship was strategically stressed by McIntyre: “a major threat to my business is when a sponsor occupies a category but does not leverage [that sponsorship]” (McIntyre, personal communication, Jan. 26, 2005). Further, he added that “my rule of thumb is that you must leverage at least 2:1. Tylenol [sponsor of Rowing Canada and Canadian Olympic Committee] leveraged 6:1 as a minimum, maybe even 8:1 with TV” (McIntyre, personal communication, Jan. 26, 2005). Leveraging is a topic that has been touched on by numerous authors in the literature. It provides ways to communicate with the consumers and has also been identified as an effective way to combat ambush marketing (Sandler & Shani, 1993; Meenaghan, 1998; Shani & Sandler, 1998; Crompton, 2004; Seguin et al., 2005).

Fourth, the need to evaluate sponsorship in a more sophisticated fashion than eyeballs reached was emphasized. “For the evaluation of my sponsorships, renewal is one thing but ‘up-ing’ [the value of the] sponsorship is another. [For the sponsor], evaluation is tracked on volume from incremental sales volume” (McIntyre, personal communication, Jan. 26, 2005). The evaluation of sponsorship has been a contentious point in the literature. In fact, Slack and Berrett (1995) suggest that evaluation is controversial and a challenging component of sponsorship. As companies seek different objectives from sponsorship, different types of measures and designs should be required for each objective (Crompton, 2004). Despite such planning, the plurality of objectives pursued by corporations and the use of other promotional tools for leveraging purposes makes sponsorship evaluation a complicated task (Meenaghan, 1983; Berrett, 1993, Crompton, 2004). This case suggests that perhaps the industry has found ways to measure sponsorship return that have yet to be tested or reported from academics (see Hoek & Gendell, 2001).

Finally, the case study provides that future trends may increase sponsorship’s effectiveness as compared to advertising:

…think about the playing field for a minute. TV is powerful, 700 channels. Mass advertising moves awareness of new products but it does not do the trick for sales. People can easily avoid commercials…so unless there is an inherent interest or association [in the product that] people have, it [the promotion] doesn’t work (McIntyre, personal communication, Jan. 26, 2005).

Table 1 below summarizes these six supporting points.

Table 1: Additional Support Points for Sponsorship in Promotional Mix

Point Support Related Literature
1 ability to target specific markets Shank, 2005; Mullin et al., 2000; Irwin & Sutton, 1994; Sandler & Shani, 1993
2 ability to specifically address sponsors objectives Arthur, 1998; Fahy, Farrelly, & Quester, 2004; Amis, Slack & Berrett, 1999
3 ability to use sponsorship as the basis for a complete promotional campaign Crompton, 2004; Seguin et al., 2005; Sandler & Shani, 1998, 1993; Meenaghan, 1998
4 the fact that sponsorship requires more sophisticated evaluation than advertising Hoek & Gendell, 2001; Meenaghan, 1983; Berrett, 1993, Crompton, 2004
5 future business trends enabling sponsorship effectiveness Suggested by Séguin & O’Reilly, 2005

Conclusion

The significance of the K.Mac case to the sport industry is its demonstration that, in practice, sponsorship is used as part of the promotional mix of sponsors. In general, results strongly support that authors of journal articles and textbooks need to consider what is happening in practice in their future writings. This would, in turn, provide students and practitioners with the knowledge of the strategic potential that sponsorship possesses, “sponsorship will play an increased marketing role in the future. It gives you the platform of a unique association that allows you to get your message out to a targeted passionate and emotional group” (McIntyre, personal communication, Jan 26th, 2005). Industry trends and sponsorship spending trends support this continued growth of sponsorship in the future, further supporting the need to formalize its strategic place in marketing strategy classifications.

Although limited to a single case, current research provides an impetus for continued work in establishing sponsorship as a legitimate member of the promotional mix alongside advertising, public relations, publicity, sales promotion, and personal selling. In this regard, quantitative research on large samples of promotional activity is required to demonstrate sponsorship’s distinction from advertising and its important role in collaboration with the other established members of the promotional mix.

References

Abratt, R. and Grobler, P.S. (1989) The evaluation of sports sponsorship. International Journal of Advertising, 8, p. 351-362.

Amis, J., Slack, T., & Berrett, T. (1999). Sport sponsorship as distinctive competence. European Journal of Marketing, 33(4), 250-272.

Arthur, D., Scott, D., Woods, T., and Booker, R. (1998). Sport sponsorship should… A process model for the effective implementation and management of sport sponsorship programs. Sport Marketing Quarterly, 7(4), 49-60.

Berrett, T. (1993). The sponsorship of amateur sport – government, national sport organization, and the corporate perspectives. Leisure and Society 16, 323-46.

Bloxam, A. (1998). Brand affinity and television programme sponsorship. International Journal of Advertising, 17(1), 89-98.

Brooks, C.M. (1994). Sports Marketing – Competitive Business Strategies for Sports. Prentice Hall: Englewood Cliffs, New Jersey, USA.

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