The Importance of Driving Distance and Driving Accuracy on the PGA and Champions Tours

### Abstract

The question of whether driving distance or driving accuracy is more important to a golfer’s overall level of performance is a question that has long been debated. No conclusive answer has been found despite the efforts of numerous researchers who have investigated the relative importance of these two shot-making measures along with other shot-making measures such as greens-in-regulation and putting average. There are various reasons why this particular question has gone unanswered for so many years and many of these reasons are methodological in nature. However, the results in this paper, using data from the 2006-2009 seasons of the PGA and Champions Tours and a new methodological approach, indicate that the relative importance of driving distance and driving accuracy depends upon both the type of hole (Par 4 hole versus Par 5 hole) and the age of the golfer. For younger PGA Tour members, driving accuracy was more important than driving distance on Par 4 holes, but the opposite was true on Par 5 holes. For older Champions’ Tour members, driving distance was more important than driving accuracy on both Par 4 and Par 5 holes. Additional analyses revealed that the quality of the drive, in terms of both its distance and accuracy, was relatively more important to a golfer’s performance on the Champions Tour than it was on the PGA Tour.

**Key Words:** Golf, Driving Distance, Driving Accuracy, importance, performance

### Introduction

Which is more important to a golfer’s success – how far they drive the ball or how accurate they are with their drive? Past attempts to answer this age-old question have been unsuccessful for a variety of reasons, including the utilization of flawed methodological procedures as well as the failure of researchers to consider that the relative importance of driving distance and driving accuracy might actually depend upon the combination of a number of different factors. The literature contains numerous studies that look at the extent to which driving distance and driving accuracy, along with other shot-making skills measures such as greens-in-regulation, putting average, and sand saves, were correlated to a golfer’s overall level of performance. Consistently, in these analyses, greens-in-regulation and putting average were found to be more highly correlated with scoring average and total earnings than either driving distance or driving accuracy (3,5,10). Further, in many instances, neither driving distance nor driving accuracy was statistically significant. These past analyses were typically based upon the performance of PGA Tour members, although the performances of members of other professional golf tours and amateur golfers have also been analyzed (2,6,7,8,11).

There are a number of methodological issues that need to be examined when attempting to evaluate the relative importance of driving distance and driving accuracy, especially when these two measures are considered in conjunction with other predictor measures. Failure to do so can result in faulty conclusions being made. In this paper, the distance versus accuracy question is examined by conducting separate analyses for members of the PGA Tour and the Champions Tour.

### Methods

#### Populations

The populations of interest in the study are members of the PGA Tour and the Champions’ Tour for the last four tour seasons, 2006-2009. The latter tour is for golfers who are at least 50 years of age. Data used for both tours in this analysis came from the PGA Tour website (www.pgatour.com).

#### Dependent Variables

scoring average has frequently been used as an overall performance measure in analyses that examined the effects of various shot-making skills. However, in the present study, which compares the relative importance of driving distance and driving accuracy, scoring average should not be used as the dependent variable measure. The reason for this is that scoring average is based on all 18 holes in a round, and golfers will typically use a driver only on Par 4 and Par 5 holes and not on Par 3 holes. The fact that there may be as many as five or six Par 3 holes in a round makes scoring average an inappropriate performance measure for the purpose of this study.

The total earnings of a professional golfer on a particular tour are another measure that has been used for the dependent variable. Like scoring average, total earnings have problems associated with its use in the present study. The first problem is that tournaments on the various professional golf tours do not offer the same amount of prize money. As a result, total earnings is more heavily weighted to how well a golfer performs in tournaments that have the largest purses than to how well a golfer performs in all of the tournaments in which they play. A second problem is that total earnings do not take into account the number of tournaments played in a season. Accordingly, low total earnings may be due either to poor performances or to a small number of tournaments having been played.

Due to the problems associated with both scoring average and total earnings, it was decided to use two different dependent variable measures for determining the relative importance of driving distance and driving accuracy. These two measures are (i) scoring average obtained only on Par 4 holes and (ii) scoring average obtained only on Par 5 holes. By having these two distinct measures, it is possible to determine whether the relative importance of driving distance and driving accuracy varies by type of hole. Further, the use of these two measures also eliminates the previously discussed problems associated with both scoring average based on 18 holes and with total earnings.

#### Independent Variables

Besides driving distance and driving accuracy, there are other variables or shot-making skills that have been commonly used in analyses that sought to determine the key factors that are related to a golfer’s overall performance. Three of the most frequently used measures will be used in this study. They are:

– **Greens-in-regulation:** The percentage of times that a golfer is able to land his or her ball on the green in two strokes on a Par 4 hole and in three or fewer strokes on a Par 5 hole.

– **Putting average:** The average number of putts per greens-in-regulation.

– **Sand saves:** The percentage of times a golfer takes two or fewer shots to put their ball in the hole from a greenside sand bunker.

Analysis
Descriptive statistics will be obtained and regression analyses were conducted in order to determine the relative importance of driving distance and driving accuracy. However, it should be noted that a potential problem exists when using highly correlated predictor variables in a regression analysis. This is the problem of multicollinearity and this problem is one that is often present in studies that seek to determine the relative importance of various shot-making skills. For example, Heiny (5) did not explicitly consider the effects of multicollinearity when he concluded, using data from the 1992-2003 PGA Tour seasons, that the two driving measures were of far less importance to a golfer’s overall level of performance than either greens-in-regulation or putting average. The problem of multicollinearity arose since driving distance and driving accuracy were both highly correlated with greens-in-regulation and because these three measures were all used in the regression model. Due to multicollinearity, the relative importance of the two driving measures could not be accurately determined. Since the focus of this study is on driving distance and driving accuracy, primary attention will be placed on these two measures.

### Results

#### Descriptive Statistics

Descriptive statistics for driving distance and driving accuracy for members of each tour during the 2006 to 2009 seasons are given in Table 1. The scoring average on both Par 4 and Par 5 holes for each of the tours remained fairly constant over this period of time. On the shorter Par 4 holes, the average score on both tours was virtually identical and slightly over par. On the Par 5 holes, the average score was under par on both tours, but Champions’ Tour golfers had a slightly higher stroke average compared to their PGA Tour counterparts.

**Table 1**
Means and Standard Deviations for Scoring Average, Average Driving Distance and Driving Accuracy Percentage for Golfers on the PGA and Champions Tours: 2006-2009

2006 2007 2008 2009
Tour/variable Mean SD Mean SD Mean SD Mean SD
PGA Tour
Scoring average on Par 4 holes 4.06 0.04 4.07 0.04 4.07 0.03 4.06 0.04
Scoring average on Par 5 holes 4.68 0.07 4.69 0.06 4.70 0.07 4.69 0.07
Average driving distance (yards) 289.5 8.7 289.1 8.6 287.6 8.6 288.1 8.6
Driving accuracy (%) 63.4 5.4 63.5 5.2 63.4 5.5 62.3 5.5
(n) (196) (196) (197) (202)
Champions Tour
Scoring average on Par 4 holes 4.06 0.06 4.05 0.06 4.05 0.07 4.06 0.07
Scoring average on Par 5 holes 4.73 0.10 4.71 0.09 4.73 0.08 4.72 0.11
Average driving distance (yards) 270.2 9.4 273.7 9.3 272.6 8.9 277.0 10.5
Driving accuracy (%) 71.4 5.0 69.2 5.3 69.1 5.8 68.6 5.4
(n) (80) (77) (75) (81)

During the four year period, the average driving distance on the PGA Tour was between 287.6 yards and 289.5 yards. The big jump in terms of average driving distance on the PGA Tour came between 1995 and 2003 when a spring-like effect in drivers was permitted. This development, together with a new a multi-layered ball, allowed golfers to launch balls higher and with less spin, thus creating optimum launch conditions and longer driving distances. This has resulted in the average driving distance leveling off in recent years on the PGA Tour. However, on the Champions’ Tour, the distance of the average drive increased from 270.2 yards in 2006 to 277.0 yards in 2009. This recent increase was due, in part, to a number of older tour members retiring and being replaced by longer-hitting younger golfers. In 2009, the differential between the PGA Tour and the Champions’ Tour in terms of the length of the average drive was just 11.1 yards compared to 19.3 yards in 2006.

The driving accuracy percentages were in a narrower range on the PGA Tour compared to the Champions’ Tour. In addition, the Champions’ Tour accuracy percentages exhibited a steady decline over the four year period and, on each tour, the percentage was at its lowest level in 2009. In terms of the variability of the two scoring averages as measured by the standard deviation, there was considerably more variability in the average scores on both the Par 4 and Par 5 holes for members of the Champions’ Tour than for members of the PGA Tour. The variability was also greater on the Champions’ Tour with respect to both driving distance and driving accuracy, but the variability differentials were not as large as they were for the two scoring average measures.

A moderately strong negative correlation existed between Driving Distance and Driving Accuracy for golfers on both tours during the 2006-2009 seasons. These correlations, which were all significant at the .01 level, are given in Table 2. The nature of the relationship found in this study was similar to that obtained by Wiseman et al (12) for members of the PGA Tour during the 1990-2004 seasons. The results also indicate that during the last two years, there was a weakening of the relationship for members of the Champions’ Tour.

**Table 2**
Correlation between Driving Distance and Driving Accuracy on the PGA and Champions Tours: 2006-2009

Tour 2006 2007 2008 2009
PGA Tour -.59 -.64* -.61* -.57*
Champions Tour -.53 -.52 -.47 -.37

∗ Correlation is significantly different from zero (p < .01) in that year.

For each tour, a golfer’s average driving distance and driving accuracy percentages were correlated with their scoring average on Par 4 and Par 5 holes. The obtained correlations are presented in Table 3. Most signs are negative, as expected, since long drives and a high driving accuracy percentage are associated with good performance and low scores. However, there were distinct differences in the correlations depending upon the tour and the type of hole.

**Table 3**
Driving Distance and Driving Accuracy Correlations with Scoring Average on Par 4 and Par 5 Holes for the PGA and Champions Tours: 2006-2009

2006 2007 2008 2009
Tour/type of hole Distance Accuracy Distance Accuracy Distance Accuracy Distance Accuracy
PGA
Par 4 -.06 -.36* -.00 -.32* -.06 -.33* -.12 -.37*
Par 5 -.37* .03 -.36* -.17** -.39* .14 -.43 .12
Champions
Par 4 -.49* -.14 -.49* -.12 -.38* -.29* -.40* -.30*
Par 5 -.62* .13 -.60* .02 -.46* .01 -.54* -.08

∗ Correlation was significantly different from zero for that year (p < .01).
∗∗ Correlation was significantly different from zero for that year (p < .05).

On Par 4 holes, the correlation between driving distance and scoring average for golfers on the Champions’ Tour was much stronger than for golfers on the PGA Tour. These correlations were between r = -.38 and r = -.49 for Champions’ Tour members, but only between r = -.00 and r = -.12 for PGA Tour members. These latter correlations indicated that there was virtually no relationship between driving distance and scoring average on Par 4 holes for PGA Tour golfers. The opposite was true for driving accuracy. The correlation between driving accuracy and scoring average on the PGA Tour was stronger than on the Champions’ Tour. Correlations for driving accuracy and scoring average were between r = -.32 and r = -.37 for golfers on the PGA Tour and between r = -.12 and r = -.30 for golfers on the Champions’ Tour. In the last two years, the relationship between driving accuracy and scoring average on the Champions’ Tour has strengthened. The above results suggest that on Par 4 holes, driving distance was far more important than driving accuracy for Champions’ Tour golfers, while driving accuracy was far more important than driving distance for PGA Tour golfers.

With Par 5 holes, driving distance was more highly correlated with scoring average than was driving accuracy on both tours. The correlations were stronger, however, on the Champions’ Tour and were between r = -.46 and r = -.62. On the PGA Tour, the correlations were between r = -.36 and r = -.43. For driving accuracy, the correlations were weak on both tours. These results suggest that on Par 5 holes, driving distance was more important than driving accuracy for players on both the PGA Tour and the Champions’ Tour.

#### Regression Analyses

Regression analyses were conducted to determine the extent to which driving distance and driving accuracy taken together could explain the variability in scoring average on Par 4 and Par 5 holes. A large R2 value would indicate the drive was a key factor in terms of explaining overall performance, while a small R2 value would indicate the opposite. Results are shown in Table 4.

**Table 4**
Estimated Linear Regression Equation Coefficients and R2 Values when Driving Distance and Driving Accuracy were used to Predict Scoring Average

Tour / type of hole / year Estimated Linear Regression Coefficients
b0 Constant b1 Driving distance b2 Driving accuracy R2
PGA
Par 4
2009 5.076 -.0024* -.0050* .30
2008 4.469 -.0008** -.0026* .14
2007 4.716 -.0014* -.0037* .18
2006 4.826 -.0017* -.0041* .24
Par 5
2009 6.176 -.0046* -.0026** .21
2008 5.932 -.0038* -.0019 .16
2007 5.665 -.0031* -.0011 .14
2006 6.081 -.0041* -.0035* .19
Champions
Par 4
2009 5.710 -.0042* -.0071* .39
2008 5.904 -.0050* -.0071 .42
2007 5.925 -.0052* -.0063* .44
2006 5.998 -.0053* -.0070* .45
Par 5
2009 7.172 -.0072* -.0068* .38
2008 6.493 -.0055* -.0038** .26
2007 7.372 -.0080* -.0069* .47
2006 7.238 -0.0080* -.0054** .44

∗ Estimated regression coefficient is significantly different from zero (p < .01).
∗∗ Estimated regression coefficient is significantly different from zero (p < .05).

On the Champions’ Tour, the value of R2 ranged between .38 and .47 during the 2006-2009 seasons for each type of hole, except for Par 5 holes in 2008 when R2 = .26. The regression coefficients for driving distance and driving average were all significant at the .01 level, except in 2006 and 2008 when the coefficient associated with driving accuracy was significant at the .05 level. Results on the PGA Tour differed as far less of the variability in scoring average could be explained by the drive alone. R2 values ranged between .14 and .24 in the four years and on each type of hole, except on Par 4 holes in 2009 when R2 = .30. The regression coefficient for driving distance was significant at the .01 level in each year, except in 2008 where the significance level was .05. The regression coefficient for driving accuracy on Par 4 holes was significant at the .01 level in each year, but on Par 5 holes, there were two years in which the coefficient was not statistically significant.

Additional regression analyses were conducted to determine the extent to which three other variables (greens-in-regulation, putting average and sand saves) could explain the variability in scoring average that could not be explained by either driving distance or driving accuracy. The R2 values presented in Table 5 indicate that the five measures used together could explain more of the variability in scoring average on the Champions’ Tour than on the PGA Tour. R2 values ranged from .69 to .89 on the Champions’ Tour and from .41 to .75 on the PGA Tour.

**Table 5**
R2 values when Five Skills Measures were used to Predict Scoring Average on Par 4 and Par 5 Holes for the PGA and Champions Tours: 2006-2009*

Tour / type of hole 2006 2007 2008 2009
PGA
Par 4
Par 5
Champions
Par 4
Par 5

∗ The five measures were Driving Distance, Driving Accuracy, Greens-in-Regulation, Putting Average, and Sand Saves.

**Table 6**
Proportion of Total Explained Variability in Scoring Average Directly Attributable to Driving Distance and Driving Accuracy on Par 4 and Par 5 Holes for the PGA and Champions Tours: 2006-2009

Tour / type of hole 2006 2007 2008 2009
PGA
Par 4 (.24/.67) = .36* (.18/.66) = .27 (.14/.61) = .23 (.30/.75) = .40
Par 5 (.19/.53) = .36 (.14/.41) = .32 (.16/.48) = .33 (.21/.56) = .38
Champions
Par 4 (.45/.88) = .51 (.44/.88) = .50 (.42/.89) = .47 (.39/.78) = .50
Par 5 (.44/.78) = .56 (.47/.80) = .59 (.26/.69) = .38 (.38/.78) = .51

∗ Values obtained by dividing R2 values given in Table 4 by the corresponding R2 values given in Table 5.

The ratios of the corresponding R2 values in Tables 4 and 5 are given in Table 6. These ratios indicate the relative importance of the drive compared to the other three predictor measures. The higher the ratio, the greater the variability in scoring average that could be explained by using the two driving measures compared to the three other predictor measures. As shown in the table, the ratios are higher in each case for the Champions’ Tour than for the PGA Tour. This indicates that the drive, compared to the other three measures that were used, was relatively more important for golfers on the Champions’ Tour than for golfers on the PGA Tour.

### Discussion

This study examined the relative importance of driving distance and driving accuracy on two professional golf tours from 2006-2009. Based upon independent analyses on Par 4 and Par 5 holes for each tour, the findings indicated that the relative importance of driving distance and driving accuracy varied by both tour and type of hole.
Other researchers have recently investigated the physical (1,9) and mental (4) effects of aging on the ability of professional golfers to compete at a high level. These studies described the nature of declines that take place with aging as well as compensating offsets, for example, shorter, but more accurate drives. In the present study, one possible explanation for the changing relative importance of driving distance relates to the physical changes that occur as people age. Individuals lose strength and agility over time, which in golf is frequently demonstrated by both shorter and more accurate drives. However, for Champions’ Tour golfers this improvement in driving accuracy is not enough to offset the loss in driving distance which, in turn, results in higher scoring averages. On long Par 4 holes, a short drive for these players means fewer birdie opportunities because it is more difficult to reach the green in regulation. For PGA Tour golfers, a relatively short drive on a lengthy Par 4 hole is not necessarily an impediment to reaching the green in regulation, even if the tee shot does not come to rest on the fairway.
This study also demonstrated that the drive was relatively more important to a golfer’s overall performance than was previously thought based upon a number of similar studies. This increased level of relative importance could be attributed, in part, to the fact that in the present analysis, separate scoring averages on Par 4 and Par 5 holes were used rather than a single scoring average based upon all 18 holes. Additionally, by conducting the analysis in two phases, it was shown that approximately half of the total explained variability in scoring average on both Par 4 and Par 5 holes on the Champions’ Tour, and approximately one-third of the total explained variability in scoring average on the PGA Tour, could be directly attributed to the drive alone. These results highlight the need for careful attention to the performance measures that are used in future studies.

### Conclusion

This paper investigated whether driving distance or driving accuracy was more important to a golfer’s performance. The results indicated that the answer to the question depended not only on the type of hole (Par 4 or Par 5), but also on the age of the golfer. For the 50 years of age and over golfer playing on the Champions’ Tour, driving distance was clearly a more important factor regardless of the type of hole. However, for the under 50 years of age golfer on the PGA Tour, driving accuracy was more important on Par 4 holes, while driving distance was more important on Par 5 holes. In addition, the investigation revealed that the quality of the drive in terms of the combined effects of both driving distance and driving accuracy was more important to a golfer’s success on the Champions’ Tour than it was on the PGA Tour.

### Applications in Sport

This study is relevant to all golf teaching professionals because instructors debate the amount of time golfers should spend in practicing their driving techniques. Traditionally, golfers have been told to spend less time on driving and more on other facets of the game. This study has shown that except for young professional golfers, the drive is very important in trying to achieve lower scores.

### References

1. Baker, J., Deakin, J., Horton, S. and Pearce, W. (2007). Maintenance of Skilled Performance with Age: A Descriptive Examination of Professional Golfers. Journal of Aging and Physical Ability, 15, 299-316.

2. Callan, S. & Thomas, J. (2006). Performance in Amateur Golf: An Examination of NCAA Division I Golfers. The Sport Journal, 9, 3. Available online at: <http://www.thesportjournal.org/article/gender-skill-and-performance-amateur-golf-examination-ncaa-division-i-golfers/>.

3. Engelhardt, G.M. (1995). It’s not how you drive, it’s how you arrive: the myth. Perceptual and Motor Skills, 80, 1135-1138.

4. Fried, Harold O. & Loren W. Tauer. (2009). The impact of age on the ability to perform under pressure: golfers on the PGA tour. Journal of Productivity Analysis. Available online at: <http://www.springerlink.com/content/337g8rv212w45423/?p=7d7abc1e32d744f3906e83014cf31f51&pi=4>.

5. Heiny, E. (2008). Today’s PGA Tour Pro: Long but Not so Straight. Chance, 21, 1, 10-21.

6. Moy, R.L. & Liaw, T. (1998). Determinants of golf tournament earnings. The American Economist, 42, 65-70.

7. Rishe, P. (2001). Differing Rates of Return to Performance. Journal of Sports Economics, 2, 285-296.

8. Shmanske, S. (2000). Gender, Skill and Earnings in Professional Golf. Journal of Sports Economics, 1, 385-200.

9. Tirunch, G. (2010). Age and Winning Professional Golf Tournaments. Journal of Quantitative Analysis in Sports, 6, 1. Available online at: <http://www.bepress.com/jqas/vol6/iss1/5/>.

10. Wiseman, F. & Chatterjee, S. (2006). Comprehensive Analysis of Golf Performance on the PGA Tour: 1990-2004. Perceptual and Motor Skills, 102, 109-117.

11. Wiseman, F., Chatterjee, S., Wiseman, D., & Chatterjee, N. (1994). An Analysis of 1992 Performance Statistics for Players on the US PGA Tour, Senior PGA and LPGA Tours, in A. Cochran & M.R. Farrally (Eds.) Science and Golf II. Proceedings of the World Scientific Congress of Golf. London: E & FN Spon. Pp. 199-204.

12. Wiseman, F., Habibullah, M., & Yilmaz, M. (2007). A New Method for Ranking Total Driving Performance on the PGA Tour. The Sport Journal, 10, 1. Available online at: <http://www.thesportjournal.org/article/new-method-ranking-total-driving-performance-pga-tour>.

### Corresponding Author

**Frederick Wiseman, Ph.D**
202 Hayden Hall
College of Business Administration
Northeastern University
Boston, MA 02115
<f.wiseman@neu.edu>
(617) 373-4562

### Author Bios

#### Frederick Wiseman
Frederick Wiseman is Professor of Statistics at the Northeastern University College of Business Administration

#### Mohamed Habibullah
Mohamed Habibullah is a Lecturer in Statistics at the Northeastern University College of Business Administration

#### John Friar
John Friar is Executive Professor in Entrepreneurship and Innovation at the Northeastern University College of Business Administration

2013-11-25T16:31:21-06:00March 16th, 2011|Contemporary Sports Issues, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on The Importance of Driving Distance and Driving Accuracy on the PGA and Champions Tours

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?

Factors that Influence African-American Millennials to Purchase Athletic Shoes

### Abstract

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

### Introduction

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

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

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

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

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

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

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

### Methods

#### Procedures

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

#### Instrument

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

#### Participants

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

#### Statistical Analysis

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

### Results

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

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

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

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

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

### Discussion

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

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

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

### Sport Marketing Implications

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

### Recommendations

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

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

### Tables

#### Table 1
Factor Group Mean Scores

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

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

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

#### Table 3

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

#### Table 4

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

### References

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

2013-11-25T16:36:38-06:00January 25th, 2011|Contemporary Sports Issues, Sports Facilities, Sports Studies and Sports Psychology|Comments Off on Factors that Influence African-American Millennials to Purchase Athletic Shoes

Coping Skills and Self-efficacy as Predictors of Gymnastic Performance

### Abstract
The purpose of this study was to examine the way that gymnastic performance can be discriminated based on psychological skills and self-efficacy. The sample of the study was 101 gymnasts (Mage = 11.8 ±­.74 years, 22 male and 79 female), who competed at the Hellenic Championship of Rhythmic Gymnastics and the Hellenic Championship of Artistic Gymnastics. Each completed a Self-efficacy scale one day prior to the competition and the Athletic Coping Skill Inventory – 28 immediately following the event. All subscales of the ACSI-28 showed adequate internal consistency (α>.64). A discriminant function analysis suggested that the predictors for distinguishing between poor and high level performance were: Coping with adversity (F=9.3, p<.01); Goal Setting/mental preparation (F=8.58, p<.005); Confidence (F=8.81, p<.005); Freedom from Worry (F=4.83, p<.05); Coachability (F=6.81, p<.01); and Self-efficacy (F=18.9, p<.001). The results indicated that best performance was achieved by those gymnasts who believed they could relax and compete with enthusiasm and certainty, set goals and prepare themselves for the competition, did not worry excessively about their performance, and showed confidence they could perform at a high level. According to the findings of this study, ability alone must not be the only concern of coaches. They also need to enhance certain psychological skills of their gymnasts at an early age, in order for them to have successful outcomes in a competition. More specifically, gymnasts need to learn how to cope with adversity and free themselves from worry, how to use goal setting techniques and prepare themselves for the competition, and how to improve their self-efficacy and confidence.

**Key words:** coping skills, self-efficacy, artistic and rhythmic gymnastics

### Introduction

Competitive sports place very high demands on athletes in terms of physical and psychological performance. Athletes are called to withstand significant stress both during competition and daily training, all from the very young starting age required by sports at a high level. Furthermore, elite gymnasts were found to exhibit very high anxiety levels in comparison to similarly skilled athletes in other sports (12).

According to Fitzpatrick (4) the most commonly reported attributes distinguishing between high and low levels of gymnastic performance were psychological factors, in contrast with the general belief that successful performance is mainly influenced by ability (29). Thus, the psychological skills of gymnasts can influence their capability to perform successfully in a competition. These coping skills refer to the cognitive and behavioral efforts to overcome, reduce or tolerate internal and/or external demands caused by a stressful situation. Coping with stress is not directly related to the final outcome of the effort. This means that coping is defined by the efforts to control the challenge of a situation, regardless of an athlete’s success (5).

The most widely used instrument for measuring athletes’ coping skills in gymnastics is the Athletic Coping Skill Inventory – 28 (23). ACSI-28 measures seven factors: Coping with adversity, Peaking under pressure, Goal setting and mental preparation, Concentration, Freedom from worry, Confidence and achievement motivation, and Coachability.

It has been shown that the psychological characteristics measured by the ASCI-28 are closely linked to performance in sports such as professional baseball (22, 10), golf (2), basketball (6, 8), swimming (19) and gymnastics (28). Specifically, Waples’ (28) study on gymnastics focused on athletes 10-18 years old, of different competition levels (7 to 11 according to the USAG level format). The specific competition level of each athlete was determined by skill level, training age, competitiveness and overall time and training commitment to the sport. The results of this study supported the hypothesis that psychological differences exist between elite athletes and non-elite athletes. Significant differences were shown mainly for the Coping with adversity, Goal setting and mental preparation, Concentration, and Confidence and achievement motivation subscales.

It has been also demonstrated that, in relation to young athletes, the support offered by their coaches and fellow athletes plays a very important role in coping effectively with stress (16). In this respect the ways in which children and teenagers deal with stress are influenced by the feedback and the behavior of parents, trainers and others. When a child enters puberty the importance placed on “wins” increases substantially. This in turn amplifies the feeling of being “pressured to perform”, a feeling which is carried over into puberty and adulthood. Vaillant (27) stated that the particular way in which someone deals with stress is developed during puberty and becomes entrenched during adult life. It is therefore important to initiate coping skills development regimes and programs for competitive sports at an early age. Such programs, according to Vaillant (27), should begin during childhood or puberty.

It is thus necessary to examine the coping skills and methods of young athletes in order to evaluate the effect of these methods on their performance. This in turn might allow for the more effective learning and actual use of such stress coping methods by athletes of this particular age group.

Lee (13) suggested that self-efficacy is a good indicator of final performance, in fact more so than previous performance. The term is used to describe one’s perception that he or she can perform successfully in a specific manner in order to achieve a goal or task. Bandura’s theory of self-efficacy (1) examines the influence of personal belief on the actual capability to perform, with final performance being affected by two parameters: a) the strength of a person’s belief in his or her ability to perform a certain task; and b) the presence of an accepting and responsive environment (14).

The effect of self-efficacy on performance has been further examined in a series of research projects. It has been used to predict to a significant degree the actual performance in football (17), serving in tennis (25), darts (11), basketball free-throws (9), and gymnastics (13). According to Lee (13) the performance of female artistic gymnasts has been shown to vary according to self-efficacy expectations. Weiss, Wiese and Klint (30) have demonstrated that artistic gymnasts with higher expectations of final achievement before a tournament tended to be more successful than gymnasts with low expectations of success.

Locke and Latham (15) declare that self-efficacy, together with other factors such as ability and commitment towards a goal, can positively influence performance. Additional research suggests that self-efficacy is a predictor of both motivation and performance regardless of the skill (21) or the level at which it was performed (18).

The purpose of this study was to examine the way that performance can be discriminated based on psychological skills and self-efficacy of young gymnasts.

### Method

#### Participants

One hundred sixty-one athletes of Rhythmic and Artistic Gymnastics had competed at the Hellenic Championship of Rhythmic Gymnastics and the Hellenic Championship of Artistic Gymnastics, respectively. Out of this population, 132 athletes completed a Self-efficacy scale one day prior to the competition and the Athletic Coping Skill Inventory – 28 immediately following the event. The researchers ended up with 101 (Mage = 11.8 ±­.74 years) usable questionnaires (31 athletes were excluded from the analysis due to incomplete questionnaires). The sample of the study consisted of 22 male (artistic gymnastics) and 79 female (artistic and rhythmic gymnastics) athletes.

In order to participate in these Championships, an athlete had to achieve a mean score of at least seven out of ten (mid to high level performance) in the apparatuses involved at the preliminary competition of each Championship held throughout Greece.

#### Apparatus

##### Self-efficacy scale

The scale evaluates the athlete’s perception of self-efficacy on all-around performance. The athletes were asked to respond to the following three questions: “How certain are you of performing your best?”; “How certain are you of being among the eight best on the all-around performance?”; “How certain are you of being among the three best on the all-around performance?” The items were measured on a ten-point Likert scale (1 = not at all certain to 10 = completely certain).

##### Athletic Coping Skill Inventory – 28

This is the version of ACSI-28 (23) adjusted to the Greek language (6). It consists of 28 items that measure seven factors:

1. Coping with Adversity: “I remain positive and enthusiastic during competition, no matter how badly things are going.”
2. Peaking under Pressure: “I tend to perform better under pressure because I think more clearly.”
3. Goal Setting/ Mental Preparation: “On a daily or weekly basis, I set very specific goals for myself that guide what I do.”
4. Concentration: “When I am doing gymnastics, I can focus my attention and block out distractions.”
5. Freedom from Worry: “I worry quite a bit about what others think about my performance.”
6. Confidence/ Achievement Motivation: “I get the most out of my talent and skills.”
7. Coachability: “When a coach criticizes me, I become upset rather than helped.”

The answers were given on a six-point Likert scale (1= never to 6 = always).

For the needs of this study, the ACSI-28 has been adjusted for 11- to 14-year-olds and evaluated on a preliminary study (3).

##### Performance Evaluation

Performance was measured according to the final scores achieved by the athletes during the Hellenic Championship of Rhythmic Gymnastics and the Hellenic Championship of Artistic Gymnastics. For the purpose of the study, two performance groups were created: poor (n=41) and high (n=60), according to the Hellenic Gymnastics Federation’s recommendation.

#### Procedure

One day prior to the competition athletes completed the self-efficacy scale, and immediately following the competition they completed the ACSI-28. Performance evaluation was taken from the archives of the Hellenic Gymnastics Federation.

#### Statistical Analysis of Data

The collected data were analyzed using SPSS version 15.0 for Windows (24). A Direct Discriminant Function Analysis was used in analyzing results.

### Results

The internal consistency of ACSI-28 and self-efficacy were examined with the use of the Cronbach coefficient α (Table 1). All subscales showed adequate internal consistency (Coping with adversity α=.70; Peaking under pressure α=.64; Goal Setting/mental preparation α=.79; Concentration/achievement motivation α=.72; Freedom from worry α=.65; Confidence α=.65; Coachability α=.82).

#### Discriminant Function Analysis

In order to evaluate the discriminatory power of each coping skill and self-efficacy, a direct discriminant function analysis was performed. The function significantly discriminated between the two levels of performance (Canonical R = .52, Eigenvalue = .38) (Table 3). The loading matrix of the correlations suggested that the predictors for distinguishing between poor and high level performance were Coping with adversity (F=9.3, p<.01); Goal Setting/mental preparation (F=8.58, p<.005); Confidence (F=8.81, p<.005); Freedom from Worry (F=4.83, p<.05); Coachability (F=6.81, p<.01); and Self-efficacy (F=18.9, p<.001). The best predictors of performance were Coping with adversity, Goal setting/mental preparation, Confidence and Self-efficacy.

### Discussion

Coping skills combined with self-efficacy were found to be a powerful indicator of performance. Most of the coping skills were found to predict the level of performance. More specifically, athletes with high performance scores (>75% of maximum performance) also had higher scores of Coping with adversity, Goal setting/mental preparation, Confidence/achievement motivation, Freedom from Worry, Coachability and Self-efficacy than athletes with low performance scores (<75% of maximum performance). Best performance was achieved by athletes who believed they could control themselves in stressful situations by relaxing and competing with enthusiasm and certainty, without worrying about their performance. They set goals and prepared themselves for the competition, listened to their coaches’ instructions and felt certain they could perform at their best. The results of this study were consistent with previous findings for Coping with adversity (28), goal setting techniques (20,28) and Confidence /Achievement motivation (2,28).

Not unexpectedly, athletes with low levels of control over a stressful situation, who didn’t set goals, got angry with their coaches’ instruction and did not believe they could be among the best athletes of the competition, performed poorly. These findings are consistent with previous studies, where gymnasts, in a wider range of age group and level of sport competition (2-12 years of gymnastics participation), with higher anxiety and lower ability to cope with adversity, were more likely to discontinue training (7). The findings also agree with Unestahl’s (26) suggestions that gymnasts with better inner mental training show higher level of competence.

The results of this study strengthen the accepted notion that ability alone is not the most important cause of successful outcomes (29). This has also been demonstrated in the past in gymnastics as well (4).

In contrast to much of the pre-existing research, which centered mainly on adult Greek populations, this study was carried out using a sample of male and female athletes of a younger age (11-14 years). It is thus necessary to further test and evaluate a number of additional important hypotheses in different sports in order to allow us to reach conclusions that can be generalized effectively. Doing so could, in turn, lead to a more systematic psychological intervention effort in these age groups with the ultimate goal being the prevention of negative effects in performance.

### Conclusions

The present study provides adequate evidence of the importance of coping skills and self-efficacy on gymnastic performance. According to previous studies, various psychological skills can influence performance. The findings of this study indicate that the most important psychological skills for gymnastic performance are Coping with adversity, Goal setting, Confidence, Freedom from Worry, Coachability and Self-efficacy.

### Applications in Sport

According to the findings of the present study, several suggestions can be made for the enhancement of athletic performance of young gymnasts. Coaches need to enhance certain psychological skills in their gymnasts at early ages, in order for them to have successful outcomes in a competition. Gymnasts need to learn how to cope with adversity and free themselves from worry. It is also important to use goal- setting techniques, and to prepare mentally for the competition. Furthermore, they need to improve their self-efficacy and confidence. This entails learning to control their emotions in stressful situations, relaxing and competing with enthusiasm, even when poor athletic performance occurs. Athletes also need to learn to set goals effectively, a tool which has proven useful in competitive situations, and to believe in their ability to perform successfully in competitions.

### Tables

#### Table 1
Mean Scores and Standard Deviations on Coping Skills, Self-efficacy and Performance

M SD
Coping with adversity 4.25 1.05
Peaking under pressure 3.63 1.03
Goal setting / mental preparation 4.76 1.03
Concentration / achievement motivation 4.92 0.97
Confidence 4.82 0.86
Freedom from worry 3.57 0.99
Coachability 4.97 1.08
Self-efficacy 6.73 2.84
Performance 7.18 1.32

#### Table 2
Direct Discriminant Function Analysis for Coping skills and Self-efficacy as predictors of Gymnastic Performance

Predictors Correlations of predictors with discriminant function Univariate F (1.99) p
Coping with Adversity 0.498 9.3 0.005
Goal Setting/ Mental Preparation 0.478 8.6 0.005
Confidence 0.485 8.8 0.005
Freedom from Worry -0.359 4.8 0.05
Coachability 0.426 6.8 0.01
Self-efficacy 0.712 19 0.001

Canonical R = .52, Eigenvalue = .38

### References

Bandura, A. (1997). Self-Efficacy: The Exercise of Control.: New York, NY: Freeman

Christensen, D. S. (2000). Self-efficacy, cognitive interference, sport anxiety, and psychological coping skills as predictors of performance in intercollegiate golf (Unpublished doctoral dissertation). University of Washington, Seattle, WA.

Daroglou, G. (2001). Personality characteristics, coping skills and performance in individual sports (Unpublished master thesis). Aristotelian University of Thessaloniki, Thessaloniki, Greece. (In Greek: English abstract).

Fitzpatrick, J. M. (1998). Causal attributions of elite youth female gymnasts: An investigation of types and antecedents of attribution (Unpublished doctoral dissertation). Michigan State University, East Lansing, MI.

Folkman, S. (1984). Personal control and stress and coping processes: A theoretical analysis. Journal of Personality and Social Psychology, 46(4), 839-852.

Goudas, M., Theodorakis, Y., & 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.

Hayashi, S. W. (1998). Understanding youth sport participation through perceived coaching behaviors, social support, anxiety and coping (Doctoral dissertation, Michigan State University). Eugene, OR, Microform Publications, University of Oregon.

Karamousalidis, G., Bebetsos, E., & Laparidis, K. (2006). Psychological Skills of Greek Basketball Players. Inquiries in Sport & Physical Education, 4(3), 442 – 448 (In Greek: English abstract).

Kavussanu, M., Crews, D., & Gill, D. (1998). The effects of single versus multiple measures of biofeedback on basketball free throw shooting performance. International Journal of Sport Psychology, 29(2), 132-144.

Kimbrough, S. K., DeBolt, L., & Balkin, R. (2007). Use of the Athletic Coping Skills Inventory for prediction of performance in collegiate baseball.  The Sport Journal, 10(1), Winter 2007.

Kitsantas, A., & Zimmermann, B. (1998). Self regulation of motoric learning: A strategic cycle view. Journal of Applied Sport Psychology, 10(2), 220-239.

Kolt, G., & Kirby, R. J. (1994). Injury, anxiety and mood in competitive gymnastics. Perceptual and Motor Skills, 78, 955-962.

Lee, C. (1982). Self-efficacy as a predictor of performance in competitive gymnastics. Journal of Sport Psychology, 4, 405-409.

LeUnes, A., & Nation, J. (1996). Sport Psychology. Chicago, IL: Nelson-Hall Publishers.

Locke, E., & Latham, G. (1990). A theory of goal setting and task performance. Englewood Cliffs, NJ: Prentice Hall.

Madden, C. C. (1995). Ways of coping. In T. Morris & J. Summers (Eds.). Sport Psychology: Theory Applications and Issues (pp.288-310). Melbourne: John Wiley & Sons.

Mandell, R. A. (1994). The influence of role status, self-efficacy and soccer performance (Master thesis). Microform Publications, Int’l Institute for Sport and Human Performance. University of Oregon, Eugene.

Miller, M. (1993). Efficacy strength and performance in competitive swimmers of different skill levels. International Journal of Sport Psychology, 24(3), 284-296.

Mummery, W. K., Schofield, G., & Perry, C. (2004). Bouncing back: The role of coping styles, social support and self-concept in resilience in sport performance. Athletic Insight, 6(3).

Nieh, J. C. L., & Lu, F. J. H. (2001). Relationships among Psychological Skills, Performance and Flow Experience on Intercollegiate Athletes. AAASP Conference Proceedings, p.53, Orlando, U.S.A., FL.

Shunk, D. H. (1995). Self-efficacy, motivation, and performance. Journal of Applied Sport Psychology, 7(2), 112-137.

Smith, R. E., & Christensen, D. S. (1995). Psychological Skills as Predictors of Performance and Survival in Professional Baseball. Journal of Sport and Exercise Psychology, 17(4), 399-415.

Smith, R. Ε., Smoll, F. L., Schutz, R. W., & Ptatek, J. T. (1995). Development and validation of a multidimensional measure of sport specific psychological skills: The athletic coping skills inventory – 28. Journal of Personality and Social Psychology, 17, 379-398.

SPSS Version 15.0 [Computer Software]. (2006). Chicago, IL: SPSS.

Theodorakis, Y. (1996). The influence of goals, commitment, self-efficacy and self-satisfaction on motor performance. Journal of Applied Sport Psychology, 8, 171-182.

Unestahl, L. E. (1983). The mental aspects of gymnastics. Orebro, Sweden: VEGE Publishers, Inc.

Vaillant, G. E. (1977). Adaptation to life. Boston MA: Little, Brown Co.

Waples, S. B. (2003). Psychological characteristics of elite and non-elite level gymnast (Doctoral dissertation). Texas A&M University, Texas, USA.

Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92, 548-573.

Weiss, M. R., Wiese, D. M., & Klint, K. A. (1989). Head over heels with success: The relationship between self-efficacy and performance in competitive youth gymnastics. Journal of Sport and Exercise Psychology, 11, 444-451.

### Corresponding Author

**Garifallia, Daroglou**
Department of Physical Education and Sport Science, Aristotelian University of Thessaloniki
54006, Thessaloniki, Greece
<filio@phed.auth.gr>
+302310992225, +306977293221

2013-11-25T16:37:34-06:00January 19th, 2011|Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on Coping Skills and Self-efficacy as Predictors of Gymnastic Performance

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

Cantu, R. C. (1998). Second-impact syndrome. Clinics in Sports Medicine, 17(1), 37-44.

Cantu, R. C. (2001). Posttraumatic retrograde and Anterograde amnesia: Pathophysiology and implications in grading and safe return to play. Journal of Athletic Training, 36(3), 244-248.

Centers for Disease Control and Prevention. (2009). Heads up: Concussion in high school sports. Retrieved November 13, 2009 from <http://www.cdc.gov/TraumaticBrainInjury/coachestoolkit.html>.

Centers for Disease Control and Prevention (CDC). (1997). Sports-related recurrent brain injuries – United States. Morbidity and Mortality Weekly Report, 46(10), 224-227.

Collins, M. W., & Hawn, K. L. (2002, February). The clinical management of sports concussion. Current Sports Medicine Reports, 1(1), 12-22.

Collins, M. W., Lovell, M. R., Iverson, G. L., Cantu, R., Maroon, J., & Field, M. (2002). Cumulative effects of concussion in high school athletes. Neurosurgery, 51(5), 1175-1179.

Field, M., Collins, M. W., Lovell, M. R., & Maroon, J. (2003). Does age play a role in recovery from sports-related concussion? A comparison of high school and collegiate athletes [Electronic Version]. Retrieved April 6, 2006, from <http://www.impacttest.com/ArticlesPage_images/Articles_Docs/7DHSvsCollege%20AthleteJPediatrics2003.pdf>.

Giza C. C., & Hovda, D. A. (2001, September). The neurometabolic cascade of concussion. Journal of Athletic Training, 36(3), 228-235.

Goldstein, M. (1990). Traumatic brain injury: a silent epidemic. The Journal of the American Medical Association, 27, p. 327.

Guskiewicz, K. M., Ross, S. E., & Marshall, S. W. (2001, September). Postural stability and neuropsychological deficits after concussion in collegiate athletes. Journal of Athletic Training, 36(3), 263-273.

Guskiewicz, K. M., Bruce, S. L., Cantu, R. C., Ferrara, M. S., Kelly, J. P., McCrea, M., & Valovich-McLeod, T. C. (2004, September). National Athletic Trainers’ Association position statement:

Management of sport-related concussion. Journal of Athletic Training, 39(3), p. 280-295.

Guskiewicz, K. M., McCrea, M., Marshall, S. W. (2003). Cumulative effects associated with recurrent concussion in collegiate football players: The NCAA concussion study. Journal of the American Medical Association, 290(19), 2549-2555.

Kelly, J. P., Nichols, J. S., Filley, C. M., Lillehel, K. O., Rubinstein, D., & Kleinschmidt-DeMasters, B. K. (1991). Concussion in sports: Guidelines for the prevention of catastrophic outcome. Journal the American Medical Association, 226(20), 2867-2869.

King, N. S. (2003). Post-concussion syndrome: Clarity amid the controversy? British Journal of Psychiatry, 183(4), 276-278.

Max’s Law, Oregon Statutes, 2009, S.B. 348 § 1. (2009)

McCrea, M., Hammeke, T., Olsen, G., Leo, P., & Guskiewicz, K. (2004). Unreported concussion in high school football players: Implications for prevention. Clinical Journal of Sports Medicine, 14(1), 13-17.

McCrory, P., Meeuwisse, W., Johnston, K., Dvorak, J., Aubry, M., Molloy, M., & Cantu, R. (2009, May). Consensus statement on concussion in sports, 3rd international conferene on concussion in sport held in Zurich, November 2008. Clinical journal of sport medicine, 19(3), 185-2000.

Micheli, L. J., Glassman, R., Klein, M. (2000). The prevention of sports injuries in children. Clinical Journal of Sports Medicine, 19(1), 821-834.

Mogabgab v. Orleans Parish School Board, 239 (So.2d 456).

National Athletic Trainers’ Association. Guidelines for preparedness and management of sudden cardiac arrest among high school and college athletes. Available at: <http://www.nata.org/newsrelease/archives/000547.htm>. Accessed November 8, 2009.

Powell, J. W., & Barber-Foss, K. D. (1999b). Traumatic brain injury in high school athletes. Journal of the American Medical Association, 282(20), 958-963.

Saunders, R. L., & Harbaugh, R. E. (1984). The second impact on catastrophic contact-sports head trauma. Journal of the American Medical Association, 252(4), 538-539.

Searles v. Trustees of St. Joseph’s College, 695 (A.2d 1206).

Shrier, I. (2005, September). Concussion risk factors and return-to-play variables. Physician & Sports Medicine, 33(9), 6-7.

Silver, J. M., McAllister, T. W., & Yusodaky, S. C. (2004). Textbook of traumatic brain injury. Arlington, VA: American Psychiatric Publishing.

Stineman v. Fontbonne College, 664 (F.2d 1082).

Strauss, A., & Corbin, J. (1998). Basics of qualitative research. Thousand Oaks, CA: Sage Publishing.

Theye, F., & Mueller, K. A. (2004). “Heads up”: Concussions in high school sports. Clinical Medicine and Research, 2(3), 165-171.

Thurman, D., Branche, C., & Sniezek, J. (1998). The epidemiology of sports-related traumatic brain injuries in the United States. Journal of Head Trauma and Rehabilitation, 13(2), 1-8.

Toto, C. (2002, May 7). Concussion. In The Washington Times, Retrieved November 29, 2002, from <http://www.washtimes.com/ metro/20020507-70377560.htm>.

Van Kampen, D. A., Lovell, M. R., Pardini, J. E., Collins, M. W., & Fu, F. H. (2006). The “value added” of neurocognitive testing after sports-related concussion. The American Journal of Sports Medicine, 34(10) [Electronic Version] Retrieved November 6, 2009 from <http://www.impacttest.com/pdf/ValueAddedNeuro.pdf>.

Will’s Bill, Texas S.B. 82, TX EDUC § 33.202 (2007).

Yin, R. K. (2002). Applications of case study research: Applied social research (2nd ed.). Beverly Hills, CA: Sage Publishing.

Zackery Lystedt Law, Washington Statutes, 2009, E.H.B. 1824 § 1. (2009).

### 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
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