Does the Media Impact Athletic Performance?

“It is not the critic who counts, nor the man who points out how the strong man stumbles or where the doer of deeds could have done them better. The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood, who strives valiantly, who errs and comes up short again and again because there is no effort without error and shortcomings, who knows the great devotion, who spends himself in a worthy cause, who at best knows in the end the high achievement of triumph and who at worst, if he fails while daring greatly, knows his place shall never be with those timid and cold souls who know neither victory or defeat.”–26th President Theodore Roosevelt


Many books, articles, and papers have been published relative to the relationship between an athlete’s mental state and his or her performance. A point of consensus clearly stated in these sources is that athletic performance efficiency is reduced by distraction. It is believed that distractions interfere with an athlete’s ability to focus. Distractions evoke negative mood responses, detrimental arousal and anxiety levels, and stress, thus resulting in the consumption of mental energy. Mental energy is a vital element needed to be able to concentrate one’s attention and maintain a positive mental attitude. By concentrating effectively, an athlete can conserve physical energy by maintaining good technique and focus, executing skills properly, and pushing the body through pain and fatigue barriers. Time spent fretting over distractions drains mental energy so that performance suffers (Manktelow, 2006). As Haverstraw (2002) noted, distractions may arise from various sources including: the presence of loved ones you want to impress, family or relationship problems, teammates and other competitors, coaches, underperformance or unexpected high performance, frustration at mistakes, poor refereeing decisions, changes in familiar patterns, unjust criticism, and the media.

The purpose of this paper is to initiate an examination of the influence of the media as a distraction and its impact on athletic performance. For the purposes of this paper it is important to have a common definition and understanding of media, arousal, stress, anxiety, and mood. Media will be defined as individuals who publicly report or make public statements relative to an athlete’s performance. In this context, media can be newspaper reporters, paparazzi, television newscasters, or fans and critics who publicize their critiques of athletic performance through the use of public forums and blogs.

In order to differentiate between arousal, anxiety, and stress in this text, specific definitions will be used. Arousal will refer to a state of alertness as the body prepares itself for action. It is associated with increases in physiological and psychological activity, such as heart rate and attention (Landers, 1980). Stress is defined as a state that results from the demands that are placed on the individual which require that person to engage in some coping behavior (Jones, 1990). Anxiety results when one doubts his or her ability to cope with the situation that causes him or her stress (Hardy et al., 1996). Additionally, for this text, mood is defined as a group of persistent feelings associated with evaluative and cognitive states which influence all the future evaluations, feelings, and actions (Amado-Boccara et al., 1993). Now that there is a common understanding of these terms, it is important to understand their relationship to athletic performance.

Arousal and Anxiety

In the field of Sport Psychology, many models have been created to explore arousal and anxiety levels as they relate to athletic performance. Following criticisms of lack of support, popular unidimensional models such as the Inverted U-Theory and the Catastrophe Theory are being replaced with multidimensional-type models (Weinberg, 1990). The Multidimensional Anxiety Theory by Martens et al. (1990), for instance, focuses on the anxiety response that accompanies high levels of stress. It takes into consideration two different elements: cognitive anxiety and somatic anxiety. Cognitive anxiety signifies distractions which involve inability to concentrate, disruptions in attention, and negative performance expectations (Martens et al., 1990). Additionally, the somatic anxiety element signifies perceived physiological arousal such as elevated heart rate and increased perspiration (Martens et al., 1990). In general, The Multidimensional Anxiety Theory hypothesizes that as cognitive anxiety increases, athletic performance decreases. Also, it concludes that an inverted-U relationship explains the correlation between somatic anxiety and athletic performance. This inverted-U relationship illustrates that as somatic anxiety increases from low to moderate levels, there is an associated improvement in performance. Performance level decreases, however, once intensity levels either exceed or fall below this moderate range (Davidson & Schwartz, 1976).

Arousal and Stress

In sport competition, athletes must often think fast and make sharp decisions regarding the task at hand. For example, when a basketball player is receiving a pass from a teammate, he or she must complete necessary cognitive functions quickly in order to catch the pass. According to a model created by A.F. Sanders, one entity that may affect one’s cognitive functions is arousal level. If the basketball player exhibits a low level of arousal, his or her perception declines. However, the player’s perception is sped up with a high level of arousal. When the arousal level is too high, though, perception becomes less efficient. Additionally, Sanders proposes that stress commonly results from one’s failed efforts in correcting a level of arousal that is too high or too low. Moreover, high levels of stress accompany increased anxiety (Sanders, 1983).


Sport psychologists, coaches, and others are eager to learn how to tailor athletes to perform at the highest level possible. In their attempts to accomplish this, mood in relationship to performance is being studied. Lane and Terry (2000) created a conceptual model of mood and performance. In this model, the authors focus on mood during pre-competition and its effects on subsequent performance. It is suggested that pre-competitive mood influences athletic behavior. Depressed mood, specifically, acts as a catalyst for reduced vigor, increased anger, confusion, fatigue, and tension, thereby debilitating performance (Mellalieu, 2003). These depressive symptoms involve negative cognitive views individuals have of themselves in relation to their past, present, and future social experiences.

To examine influences on elite athlete performance, Greenleaf et al. (2001) interviewed Olympians from the Atlanta and Nagano Olympic Games. Although positive factors existed, the Olympians cited many negative factors influencing performance. One such factor noted was media distractions. It was found that factors, such as media distraction, are psychological in nature, thus, demonstrating the importance that mental factors play in elite sport performance (Greenleaf et al., 2001).

The theoretical and empirical data regarding arousal, anxiety, stress, and mood will be used to explore the influence media may have on athletic performance. In order to apply this information, it is necessary to first provide the following individual examples where media may have impacted athletic performance.

Media’s Influence on Athletic Performance

Many athletes are targets of media prey. Win or lose, their performance and life is publicly dissected by the media. Winning brings about media glorification and expectation, and/or jealousy and criticism. Losing brings forth negative judgment and more criticism. Howard Ferguson (1990) in his book, The Edge, said, “Criticism can be easily avoided by saying nothing, doing nothing, and being nothing. Mediocre people play it safe and avoid criticism at all costs. Champions risk criticism every time they perform.” One such athlete who risked media criticism was Miki Ando.

Miki Ando was a two-time Japanese national figure skating champion and 2004 Junior World champion. She also became the first female skater to successfully complete a quadruple jump in competition. Ando is very popular in Japan and receives a lot of attention from gossip magazines and other Japanese media. Ando’s athletic performance struggled in 2005 and 2006, and media coverage turned negative. When the Japanese Skating Federation (JSF) selected her to be on its 2006 Olympic woman’s figure skating team, the press said she did not deserve to go to Torino. They also frowned on her for wearing mini skirts. The JSF was so concerned media coverage would negatively affect Ando as she prepared for the Olympics, they sent formal written requests to several magazine publishers asking them to cut back on their coverage (NBC, 2006).

The JSF was not the only organization concerned with media impact on their 2006 Olympic athletes. The Canadian Olympic Committee (2006) recognized the potential of the media as a distraction to their athletes as well. In an effort to divert any negative media influence, the Committee publicly announced the following communications objective in their victory management plan: A media training section emphasizing the notion to support athletic performance by removing media as a distraction (Canadian Olympic Committee, 2006).

Were these concerns founded? Some in the Republic of China believe so. After China won the first gold medal in the 2004 Olympic Games and had some major unexpected wins during the first few days of Olympic competition, Chinese newspaper and television stations touted predictions of gold medals their athletes would claim. The predictions, however, did not come to fruition. Athletes the media advertised would take first, such as the Chinese male gymnasts, did not even make it to the award stand. Badminton player Lin Dan was beaten in the first round of competition and Ma Lin, China’s top table tennis player, was defeated by 20th-ranked Swede Jan-Ove Waldner (China Daily, 2004).

On August 19, 2004, China Daily blamed the losses on exaggerated hypes of gold made by the media. The editorial claimed the hype caused the athletes to become overconfident and resulted in athletic incompetence. Chinese diver Peng Bo agreed. After his partner’s last-minute error cost the men’s springboard double gold, Peng Bo said, “We’re ordinary people. We feel pressure, and sometimes we can’t help having some distracting thoughts. Please understand us” (China Daily, 2004).

At the 2006 Torino Olympics, Ando did not quite meet the gold medal goal coveted by all Olympian athletes. She placed eighth in the Ladies figure skating short program and 15th in the freestyle competition. Canada’s athletes, however, exceeded the expectations of many by leaving Torino with a best-ever 24 medals, the third-most of any country (CBC, 2006). Whether or not Ando’s less than expected performance was a result of media distraction, or the Canadian athletes’ successes were a direct result of media discipline is hard to say, but should be explored further.

Because there have been no empirical examinations on the influence of the media on athletic performance, the following will provide examples of some famous athletes who have been subjected to intense media scrutiny, provide their reaction to the media attention, and present the impact, if any, the media had on their athletic performance.

The Stones that Critics Hurl

Kenny Rogers

Baseball player Kenny Rogers has had a volatile relationship with media. During the 2005 season, Rogers refused to talk to media after they published a report saying he would retire if the Rangers did not give him a contract extension. Then on June 29, 2005, while walking onto the field for a pre-game warm-up, he had an altercation with two cameramen. Rogers first shoved Fox Sports Net Southwest photographer David Mammeli, yelling at him to get the cameras out of his face. Next, Rogers charged cameraman Larry Rodriguez, wrestled the camera from him, threw it to the ground, and kicked it.

As a result of the tirade, Rodgers was suspended and fined. Before all of his run-ins with the media, Rogers was having a career best season. However, following the suspension, in his August 11, 2005 return to the mound, Rogers allowed five runs and seven hits in five innings, on the way to a 16 to 5 loss. He also gave up a two-run homer in the all star game where he was booed by the crowd.

This indicates a possible causal relationship between stress and the media influence on Rogers. His adversarial relationship with the press caused him to publicly lose his temper and become violent, which cost him playing time, salary, and the respect of the fans. Moreover, it affected his performance and his season’s statistics declined (ESPN, 2006).

Ricky Williams

David Swerdlick’s editorial Ricky Williams – Just Let Him Be, discusses how the constant pressure of the media drove collegiate and professional football standout, Ricky Williams, to quit the sport he loved. According to Swerdlick (2005), Ricky Williams suffered with a debilitating social anxiety disorder and extreme shyness. The aggressive media attention was uncomfortable and frightening for him. In his early pro years he dreaded doing interviews so much he wore his helmet and an eye shade inside his face mask.

The article claims that in order to cope with all the unwanted media attention Williams smoked marijuana. As a result, he failed three NFL drug tests and experienced further embarrassing press. Superstar NFLer, Ricky Williams, loved the sport, but couldn’t handle the media attention that comes with greatness. Swerlick asserts that the media negatively impacted this athlete. Ricky Williams walked out on the Miami Dolphins; lost millions of dollars; lost the respect of his teammates and fans; and still finds himself as media fodder (Swerdlick, 2005). Many disagree with this conclusion, however, as is indicated on numerous blogs. One such blog critic instead credits Williams’s early departure with his overriding desire to smoke marijuana (, 2004).

Mike Tyson

Iron Mike Tyson’s quick rise to the top of professional boxing made him one of the most publicized and admired boxers of all times. His personal turmoil, however, such as being convicted of raping Miss Black America and his volatile escapades such as biting off the ear of opponent, Evander Holyfield, made him one of the most media criticized boxers of all times.

Up until the early 1990s, Tyson, to many boxing enthusiasts, seemed unbeatable. He earned numerous championship titles such as: World Boxing Council (WBC) Heavyweight Title, World Boxing Association (WBA) Heavyweight Title, and International Boxing Federation (IBF) Heavyweight Title. However, as his personal life became mired in legal difficulties, the media had an increased negative focus when reporting about him, and concurrently, Tyson lost all of his previously earned professional boxing titles. His sudden decline in performance may be tied to negative and excessive media attention, effecting his training and mental state. Days prior to a comeback fight, in an interview by writer John Raygoza, Tyson was asked if it bothers him when the media writes negative things about him. He responded, “It’s my job to beat people and win fights…and it’s their job to sell papers. Everything that could’ve been said about Mike Tyson has already been said. I don’t take it personally like I use to.” Here, Tyson admits that the media criticism did impact him but he is beyond that. One has to wonder, though, as Tyson was knocked out in the fourth round of that fight, and his boxing career ended on that night (Raygoza, 2004).

Only the Mentally Strong Survive

The above were examples of athletes whose performance was negatively impacted by media. Tony Dorsett, legendary NFL halfback, said: “You can turn the negative around and use it as a motivating force in your life. One of my biggest desires has always been to prove certain people wrong-to prove to them I can do it despite what they think or say” (Ferguson, 1990).

Like Dorsett, some athletes are able to strive under intense media scrutiny by using it as motivation to achieve success. The following are several reports of athletes who have been able to survive and thrive in spite of the media.

Venus And Serena

In the world of tennis, two standout sisters have received more than their share of negative press. Venus and Serena Williams are not your typical small, cutesy, white, female tennis players. They are black, muscular, and solid. They win with their hard hitting, hard return, power-games. Not only does the media write and talk about them due to them not fitting the stereotypical construct of the usual tennis player, Venus and Serena are also known and criticized for the exotic, colorful, and tight fitting attire they wear on the court.

The two girls grew up in a poor, Los Angeles neighborhood. They could not afford tennis lessons or even tennis balls. Their dad taught them the game from books; they used worn equipment; and they practiced on rundown tennis courts. To illustrate, Venus and Serena’s father comments on the environment and conditions his daughters experienced during practices in East Compton Park, California: “It’s a radical neighborhood. A lot of dope is sold. We play on two courts — that’s all there is –and they look like trash, they’re so slippery” (Sports Illustrated, 2006).

Instead of being commended for overcoming disadvantage, Venus and Serena are criticized and negatively portrayed by media. Those in the tennis world and media constantly criticize that Venus and Serena are not skilled athletes…just hard hitting. Through all of the media attention, however, Venus and Serena have proven tremendous mental toughness that has served them well in their progress and maturation. The girls countered the media by rising to the top of their games and raising the bar for all (Loving, 2002).

Colin Montgomerie

Colin Montgomerie, one of Europe’s top golf pros, has had his share of ups and downs. Among his many accomplishments are victories at the European Tour Order of Merit every year from 1993 to 1999. During this era, he was consistently ranked in the top 10 in the Official World Golf Rankings, reaching the number two ranking at his peak. Then in 2003 and 2004, he began having personal and performance problems, and his ranking slumped to the eighties. To make matters worse, he became the victim of media and fan abuse. Media publicly questioned his ability, and fans called him names, such as Mrs. Doubtfire, because of his noticeable weight gain.

Initially, the negative media and fan criticism had an impact on him and his performance. According to an article written in Golf Today, not only was he performing really badly in an Open Tournament, he was so upset by media criticism he threatened to pull out of the Scandinavian Masters (Lexus Internet Limited, 2002). Moreover, Martin (2002) reported that because of negative media coverage Montgomerie even considered taking a break from the sport.

Eventually though, Montgomerie overcame the criticism and made a comeback in 2005, where he won another European Tour Order of Merit and returned to the top ten in the World Golf Rankings.

Clinton Portis

Washington Redskins running back, Clinton Portis, during the 2005-2006 season, was known for wearing outrageous costumes and playing odd characters during media interviews. In one such costume, he dressed up as a made-up character named “Sheriff Gonna Getcha”. He wore a long, black wig, glasses with oversized eyes, a Led Zeppelin T-shirt, a star-shaped badge, and an unusual necklace. In another interview, he showed up in a black cape, black Lone Ranger mask, clown-style oversized yellow sunglasses, a shaggy black wig, and fake gold teeth. He also created outrageous names for his costumes such as: Dr. I Don’t Know, Dolla Bill, Rev Gonna Change, Kid Bro Sweets, and Coach Janky Spanky (Solomon, 2006).

The stand-out athlete started this charade of characters after being traded by the Denver Broncos to the Washington Redskins in 2004. He was uncomfortable on this new team and had trouble scoring touchdowns. To deflect negative press questions he began dressing up in costumes, and had fun with the press. Five of his teammates got in on the act as well. During one interview, they joined Portis by dressing up in crazy get-ups calling themselves “Clinton’s” Angels. Many may view these stunts as foolish, however, Portis’s tactics proved successful. Instead of negative reporting by the press, the press had fun with the parade of characters and concentrated on this instead of the team’s performance. Portis not only started scoring touchdowns, he broke the Redskins’ record for the most rushing yards in a season in 2006 and became the third runner in league history to reach 1,500 yards in three of his first four seasons (Solomon, 2006). With media criticism gone, the team went from a losing record to playoff contenders. This is a case where media impact could have contributed to poor performance on the field. Instead, Portis used the media to have fun, loosen up the team, and motivate himself (Solomon, 2006).

Application to Theory

Throughout this paper there have been examples of athletes whose performance was impacted by media. Some let media distraction impact them negatively. Kenny Rogers’s ordeal may be explained by Lane and Terry’s (2000) conceptual model of mood and performance. In Roger’s case, media distraction triggered his increased anger and tension, a result of depressed mood. Lane and Terry’s finding that depressed mood is debilitating to performance is evident as Rogers’s potentially career best season quickly declined following the media incident. Ricky Williams experienced much stress as he struggled with the constant pressure and media attention. According to The Multidimensional Anxiety Theory, by Marten et al. (1990), the anxiety responses Williams encountered may be due to elevated stress levels. Specifically, Williams’s increased cognitive anxiety response, due to disruptions in attention and concentration, led to decreased performance. However, Williams tried to counteract his increased cognitive anxiety with the calming effects of marijuana. In Mike Tyson’s situation, The Multidimensional Anxiety Theory and Lane and Terry’s conceptual model of mood and performance are essential in explaining the impact media had on Tyson’s drastic change in performance. Following the extensive media criticisms relating to many of Tyson’s problems and controversial incidents, Tyson’s legendary boxing performances rapidly declined. Decreased concentration, a result of increased cognitive anxiety, affected Tyson’s training prior to competition. Also, during performance, Tyson experienced somatic anxiety levels above a moderate range, thus decreasing his performance. This is evident from the inverted U-relationship. Furthermore, Tyson’s mental state prior to competition, negatively affected his performance. Tyson may have exhibited depressive symptoms which include negative cognitive views individuals take of themselves in relation to their past, present, and future social experiences. If Tyson possessed depressive mood, the effects of increased anger, heightened fatigue, increased confusion, and reduced vigor immensely hindered his performance.

Other athletes, however, used media distraction as inspiration to succeed. Venus and Serena Williams, Colin Montgomerie, and Clinton Portis employed their own coping strategies to deal with the media while flourishing in competition. There are many techniques an athlete can use to overcome the media hurdles. Many hire sports psychologists or counselors. Sport psychology consultants can work with athletes to strengthen their mental preparedness in order to enhance and improve athletic performance. Sport psychology consultants are trained to help athletes understand how pressure affects them, and then introduce them to strategies to help them overcome the effects of pressure. The consultant educates athletes on mental techniques such as goal setting, motivation, confidence, relaxation, focus and concentration, team cohesion, and communication (Dunn, 2005). Moreover, sport psychologists are interested not only in helping athletes use psychological principles to enhance athletic performance, but also to achieve optimal mental health when facing tough situations brought about by sport such as pressure from family and fans, harsh comments from coaches, or media criticism.

Positive Vibes

While media has potential to negatively impact athletic performance, this medium can also be used to cultivate or bring out the best in an athlete. In an excerpt from the book, Coaching Wrestling Successfully, Dan Gable, a gold medalist in freestyle wrestling in the 1972 Olympics and former head wrestling coach for the University of Iowa, discuses various ways to motivate wrestlers. Of specific note is his view on using the media as a tool to positively motivate wrestlers. He believes athletes get pumped up from positive media, and media forums should be used extensively as a tool to motivate athletic performance. One specific media outlet he references is the collegiate team’s annual poster. He suggests that if athletes know they will get their picture on the poster if they become an All-American, they are motivated to excel and attain some deserved recognition. He also discussed the advantages of having a media day before the first competition each year. He says this not only serves as a good motivator, but also assists to enhance the athlete’s communication skills in responding to the media. Most importantly, Gable stresses the importance of a coach’s statements to the media and how they can serve as motivators. He believes athletes are inspired when they hear their coach’s positive comments about them (Gable, 1999).


The examples and cases above support the premise that media does impact athletic performance. The cases also reveal or recognize that athletes have two choices: 1) they can succumb to the challenges of media distractions, or 2) they can meet the challenges of media.

American poet Arthur Guiterman wrote, “The stones that critics hurl with harsh intent – a man may use to build a monument”’. As evidenced above, we suggest that a champion can use those stones as momentum to win. Research into the specific mechanisms of how the media influences athletic performance is warranted.

2017-04-18T08:56:37+00:00March 14th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Does the Media Impact Athletic Performance?

Performance Enhancement Drugs: Knowledge, Attitude, And Intended Behavior Among Community Coaches In Hong Kong


The purpose of the study was to elucidate the perceived knowledge, actual knowledge, attitude, and intended behavior of community coaches with respect to performance enhancement drugs (PED). The Theory of Planned Behavior was used as a guiding framework to structure the questionnaire used for data collection. Results of the analyses suggested that community coaches under-estimated their own knowledge about PED. Most respondents are supportive to the anti-doping movement in terms of both attitude and behavior intent. Results of the present study also partially agreed with the Theory of Planned Behavior, perceived knowledge, actual knowledge, and attitude towards PED were found to be significantly related to behavioral intent. Implications of the results were discussed.


The Athlete should not be the only person to be blamed in case of a positive drug test. Numerous studies have pointed out that an athlete’s use of drugs in sport could be attributed to a complex interaction of personal and environmental factors (Nicholson and Agnew, 1989; Tricker, Cook, and McGuire, 1989). Possible contributing environmental factors include attitudes of peer group and parents, accessibility to drugs, and cultural norms and values (Polich, Ellichson, Reuter, and Kahan, 1984; Tricker and Connolly, 1997).

In the coaching literature, coaches are viewed as having a strong influence in regulating athletes’ behavior and attitude (Anshel, 1990; Orlick, 1990). For example, Dieffenbach, Gould, and Moffett (2002) suggested that coaches play crucial roles in influencing quality of coach-athlete relationship, developing achievement goals for the athletes, mentoring athletes’ development and indirectly model the positive skills and characteristics athletes need for success. Therefore, it is argued that coaches could be one of the more important agents in preventing drug use among athletes and should be included in any doping prevention campaigns (Dubin, 1990).

For coaches to function optimally as role models and in assisting young athletes to formulate correct attitudes against doping, they must also possess accurate knowledge and appropriate attitude on doping and drug use. Although coaches can gain information about drug use and drug abuse through various channels, seminars and information packages are the media more favored by Hong Kong community coaches. In Hong Kong, the Sports Federation and Olympic Committee, Hong Kong, China and the Hong Kong Coaching Committee are the major stakeholders to provide such information to community coaches. In order for these agencies to develop appropriately sequenced knowledge, some understanding of the current status of coaches’ knowledge and attitude on drug use and drug abuse is necessary. Therefore, one of the purposes of the present study was to assess the perceived knowledge, actual knowledge, attitude, subjective norms, and behavioral intent related to performance enhancement drug (PED) among Hong Kong community coaches.

In developing this study and in constructing the questionnaire for data collection, the Theory of Planned Behavior (Ajzen and Fishbein, 1988) was used as a guiding framework. According to this theory, a person’s behavior is mainly determined by his/her behavioral intent which, in turn, is influenced by attitude towards the behavior, subjective norms, and perceived behavioral control. As the theory has been successfully used to predict recreational drug use (McMillan and Conner 2003; Orbell, Blair, Sherlock, and Conner, 2001), intentions to use PEDs among collegiate athletes (Allemeier, 1996) and in adolescents (Lucidi, Grano, Leone, Lombardo, and Pesce, 2004), we were confident that it could provide a meaningful structure for the study.



A total of 114 community coaches attending a coach education class during the data collection period were invited to take part voluntarily in the study. The sample is comprised of 93 male and 21 female (Age: 29.3 ± 8.1; mean ± SD). Among the participants, 28% are university graduates, 11% were university students, the remaining 61% are secondary school graduates.


The questionnaire used for data collection was developed by the authors from literature review and consultation with experts working in the area of doping and drug use. The questionnaire is comprised of 61 items. Apart from the demographic section, all other items were designed to elucidate perceived knowledge on PED, actual knowledge about PED, attitude, subjective norms, and behavior intent on drug use in sports. A combination of response types was employed, including likert-type scale and binominal scale. As the possible total scores from items related to perceived knowledge on PED and from items related to actual knowledge about PED differs, the raw score from each category was transformed to allow for parallel comparison. In transforming the scores, the maximum of 100 points was used as the reference.


A summary of means and standard deviations of key constructs examined in this study is presented in Table 1. The score mean for perceived knowledge on PED was 23.7 whereas the score mean for actual knowledge on PED reached 66.1.

Scores on attitude, subjective norm, and intent behaviour were computed in a way that positive scores represent preferred attitude, norm and intentional behavior that support the anti-doping movement. Negative scores, on the other hands, represent the support of the use of doping to take advantage over other athletes. The scores in attitude, subjective norm, and behavioral intent are 1.21 ± 0.91, –0.16 ± 1.01, and 1.37 ± 1.4 respectively. Both attitude and behavioral intent of the Hong Kong community coaches are supportive of the anti-doping movement. However, the score on subjective norm was negative and this suggests that they perceive doping as a problem in the sporting community. Table 2, 3 and 4 show the response pattern of participants to questions on attitude, subjective norm, and behavioral intent, respectively.

In terms of attitude, majority of the respondents agreed (86.2% agreed or highly agreed) that doping is not only a problem in sport but also a social problem. Most respondents did not have strong feeling on whether sanction imposed on doping cases is stringent or not (57.9% have no comment on the issue). The majority disagreed (63.7% disagreed or highly disagreed) that athletes can use drugs to enhance performance if it does not hurt his/her health. Most respondents did not believe (70.1% respondents disagreed or highly disagreed) that refusal to take PEDs equals to refraining from being an elite athlete. Respondents are slightly biased to disagree (43.8% disagreed or highly disagreed and 35.1% had no comment) that scientific research should develop drugs that can pass tests of doping control.

Questions in elucidating subjective norm of the respondents found out that most respondent disagreed (47.4% disagreed or highly disagreed) that most achievement records in sport are related to doping. The majority respondents agreed (73.6% agreed or highly agreed) that doping is a serious problem in international sports. On the other hands, most respondents disagreed (51.8% disagreed or highly disagreed) that doping is a serious problem in Hong Kong sports.

The behaviour intent of the respondents is in general supportive to the anti-doping movement. Most respondents (65.8%) claimed that they would take positive actions against his/her friends or relatives who are on banned substance. The respondents slightly biased towards not working with medical team to produce high quality banned substance (44.3% disagreed or highly disagreed and 41.6 had no comment). The majority of the respondents (62.8%) claimed that they would not find ways to assist his/her friends or relatives to get hold of banned substance.

Table 5 shows the Pearson correlation coefficients among the key constructs of the study. Behavioral intent is significantly correlated to perceived knowledge (r = -.270, p = .004), actual knowledge (r = .304, p = .002), and attitude (r =.335, p = .000) but not to subjective norm (r = .065, p = .493).

Two other significant correlations were identified, namely the correlation between actual knowledge and perceived knowledge (r = -.263, p = .007), and between attitude and actual knowledge (r = .233, p = .018).


According to the Theory of Planned Behavior (Ajzen and Fishbein, 1988), a person’s behavior is mainly determined by his/her behavioral intent which, in turn, is influenced by attitude towards the behavior, subjective norms, and perceived behavioral control. Result of the present study finds partial agreement with the Theory, namely the level of intentions to perform a particular behaviour depends on the individual’s attitude on the behaviour. However, the relationship between subjective norm and behavioral intent was not significant in our study. One of the possible reasons for this discrepancy is that the participants are community coaches who may not perceive themselves as having any significant influence or involvement with the doping problem more commonly found in elite level athletes. The three items used to elucidate information on the subjective norms were biased towards drug use among elite level athletes. Therefore, even though the respondents might have agreed to the presence of doping problem at

the elite level, the items were not sufficiently sensitive to capture their opinions on drug use issues on their day-to-day settings. Further investigation on this issue with refined items would be needed.

The present study also aims at elucidating the Hong Kong community coaches’ current status of knowledge and attitude on PEDs. This group of coaches was found to be relatively supportive to the anti-doping movement according to their attitude (1.21 ± 0.91) and behaviour intent (1.37 ± 1.4) scores. A survey on Norwegian coaches found that coaches have strong and unequivocal attitudes against doping (Figved, 1992). Laure, Thouvenin, and Lecerf (2001) also found that 98.1% of the France coaches consider that they have a role to play to flight against doping. The present respondents’ actual knowledge on PEDs, reached the mean value of 66.1, was fair and yet had rooms for further improvement. This baseline measurement could also be used for monitoring the effectiveness of any intervention programs in the future.

It is interesting to notice that there is a huge discrepancy between the respondents’ perceived knowledge (mean = 23.7) and actual knowledge (mean = 66.1). Participants tend to under-estimate their knowledge in PED and doping control. This conclusion is further supported by the negative correlation between the perceived knowledge and actual knowledge (r = -.263, p = .007). The more knowledgeable they are, the greater their under-estimation. It is possible that the more they know about PED and the doping control system, the more they understand that the problem of drug in sport is more complicated than presented. This implies that any education program designed for the coaches on PEDs could be more effective if it is mandatory. As the individuals with the least knowledge is likely to perceived that they have enough knowledge about the issue.

It is also interesting to note that the low perceived knowledge on doping among coaches was also found in a survey on France coaches. 80.3% of the France coaches consider themselves badly trained in the prevention of doping (Laure, et al., 2001).

Unlike the Hong Kong community coaches, the Norwegian coaches believed that they are well informed about doping (Figved, 1992). This can be due to the fact that the education about PEDs for coaches was more structured and successful in Norway than that in Hong Kong. Furthermore, the difference on cultural background may have lead to the under-estimation of the Hong Kong coaches’ knowledge on PEDs as discussed in the previous paragraph.

Currently, seminars on PEDs are few and infrequent in Hong Kong. A systematic curriculum on doping is also lacking. According to Figved’s study (1992), most coaches believed that seminars, courses, and evening sessions were the best ways of changing attitudes and increasing knowledge. Given the important role of coaches in influencing the direction of fair play in sports and the findings from this study, we suggest the need to develop a systematic and spirally progressive education program on drug use and drug abuse. Furthermore, incentives such as certifications and fee waivers could be developed to encourage coaches to such courses so as to work towards knowledge and attitude development in the area of PED.


  1. Allemeier, M.F. (1996). CIAU athletes’ use and intentions to use performance enhancing drugs: a study utilizing the theory of planned behaviour. Thesis (M.A.) University of British Columbia, Eugene, Ore: Microform Publications Int’l Inst for Sport & Human Performance, University of Oregon.
  2. Anshel, M.H. (1990). Sport psychology: From theory to practice. Gorsuch Scarisbrisk: Scottsdale, AZ.
  3. Ajzen, I. & Fishbein, M. (1988). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice Hall.
  4. Dieffenbach, K., Gould, D., & Moffett, A. (2002). The coach’s role in developing champions. International Journal of Volleyball Research, 5(1), 30-32.
  5. Dubin, C. (1990). Commission of inquiry into the use of drugs and banned practices intended to increase athletic performance. Ottawa, ON: Canadian Government Publishing Centre, 615.
  6. Figved, S.E. (1992). Drug education programs for coaches and leaders. Science Periodical on Research and Technology in Sport, 12(4), 5-9.
  7. Laure, P., Thouvenin, F., and Lecerf, T. (2001). Attitudes of coaches towards doping. J Sports Med Phy Fit, 41, 132-136.
  8. Lucidi, F., Grano, C., Leone, L., Lombardo, C., & Pesce, C. (2004). Determinants of the intention to use doping substances: An empirical contributions in a sample of Italian adolescents. International Journal of Sport Psychology, 35, 133-148.
  9. McMillan, B. & Conner, M. (2003). Applying an extended version of the theory of planned behavior to illicit drug use among students. Journal of Applied Social Psychology, 33, 1662-1683.
  10. Nicholson, N. & Agnew, M. (1989). Education strategies to reduce drug use in sport. Sports Coach, 13(1), 38-41.
  11. Orbell, S., Blair, C., Sherlock, K., & Conner, M. (2001). The theory of planned behavior and ecstasy use: Roles for habit and perceived control over taking versus obtaining substances. Journal of Applied Social Psychology, 31, 31-47.
  12. Orlick, T. (1990). In pursuit of excellence (2nd ed.). Champaign, IL: Human Kinetics.
  13. Parish, C.A. (1973). An epidemiological survey of drug use among secondary school students in grades eight and eleven in eastern South Dakota. Doctoral dissertation, University of South Dakota.
  14. Polich, J.M., Ellichson, P.L., Reuter, P., & Kahan, J.P. (1984). Strategies for controlling adolescent drug use. The Rand Publication Series, Ca.
  15. Tricker, R. & Connolly, D. (1997). Drugs and the college athlete: An analysis of the attitudes of student athletes at risk. Journal on Drug Education, 26, 275-287.
  16. Tricker, R., Cook, D., & McGuire, R. (1989). Issues related to drug abuse in college athletics: Athletes at risk. Sport Psychologist, 2(1), 155-165.


This study was supported by the Faculty Research Grant of the Hong Kong Baptist University.

Table 1

Table 2

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Table 5

2015-03-27T13:13:57+00:00June 7th, 2006|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on Performance Enhancement Drugs: Knowledge, Attitude, And Intended Behavior Among Community Coaches In Hong Kong

Video Recording of Elite Seated Shot Putters During World Class Events


The purpose of this paper is to share useful and practical information coming out of the first experience of systematic video recording of seated shot-putters during the Sydney 2000 Paralympic Games. It is anticipated that this paper will provide valuable information to sport scientists facing the challenge of conducting performance analysis of able-bodied or disabled athletes, such as seated shot-putters, during world-class competitive events. More specifically, this paper provides (1) the practical aspects of the cameras’ setup used during this systematic video recording, (2) the number and usability of attempts recorded, taking into consideration the impact of uncontrollable perturbing factors, and (3) recommendations to improve the video recording procedure in such conditions. Two operators recorded each put using two compact, high-speed digital video cameras placed in different locations such as right, left or front of the shot-putter. In this study, 15% of the attempts were not recorded, 72% were recorded and fully available for analysis, 10% were incomplete and 2% were obstructed (as a percentage of expected attempts). This study suggests that the increase of the number and usability of the attempts recorded relies on the number and position of cameras and the operators as well as on other facilitating actions.


Video recording is the central element of biomechanical analyses based on kinematic data including the range of motion, the linear and angular momentum of each segment as well as the mechanical energy expended. As for all top athletes, kinematic analysis is particularly important for the elite seated shot-putters because it is one of the rational means available that could be used to improve the understanding of their performance. More specifically, athletes, coaches and sports scientists can exploit this data to improve the shot-put technique and the design of the throwing seat.

Video recording during training

Currently, this understanding is only based on video recording of emerging and elite shot-putters during training (Chow and Mindock, 1999, Chow et al., 2000). On one hand, the recording in this situation presented the advantage of easily accommodating usual experimental requirements including the use of active or passive markers, the positions of cameras, the number of attempts recorded, etc. One the other hand, the data collected during training was partially representative of the technique as performed by these elite athletes while competing in a world-class event. This is mainly because elite athletes do not perform at their best, let alone break a world record, during training sessions particularly during the early stage of the preparation for a major event. For instance, the elite shot-putters participating to the Chow et al.’s, 2000 study performed on average 15?9% less than their personal best.

Video recording during world-class competition

Consequently, a sound understanding of the performance of elite seated shot-putters would require a performance analysis based on video recording during a world-class competition. This should allow sports scientists to produce a systematic and more realistic biomechanical analysis. Here, the term “systematic” refers to the recording of all attempts performed by a target group of athletes, such as a class. During a world-class competition every attempt of each athlete must be recorded in order to capture the best put, which is known only at the end of the event. The data collected in these circumstances is more realistic because it takes into consideration a number of external factors influencing the performance. These factors include the stress and pressure due to the presence of other opponents, mass-media, referees in charge of applying the strict rules as well as the use of official equipment to anchor the throwing seat (use of metal plate on the ground rather than pegs), travelling fatigue, etc.

Benefits of video recording during world-class competition

Furthermore, coaches, athletes and sports scientists, as well as classifiers and referees, could benefit from a kinematic analysis based on video recording of elite seated shot-putters during world-class events as the outcomes of such analysis have the potential to enhance performance and increase the fairness of the event (classification and judging).

Coaches and elite shot-putters use video recording regularly during training. Therefore, conducting similar recording during the actual competition is of value to complement their observations obtained during training. Furthermore, the understanding of the actual performance of World Championship and Paralympic medallists could not only contribute to improve both training methods and seat design but also the curricula of existing courses in training and coaching as well as the talent identification system.

Classifiers divide disabled athletes into classes according to their gender and functional level or “movement potential” depending on the control and strength of various muscle groups. This process involves observation of athletes during the event as well as a physical and functional examination assessing muscle power, range of joint movement, backwards and forwards movement, side to side movement and trunk rotation (Higgs et al., 1990, McCann, 1993, Vanlandewijck and Chappel, 1996, Williamson, 1997, Laveborn, 2000, Tweedy, 2002). A video recording of the athletes during the actual event and subsequent kinematic analysis could provide the classifiers with more accurate and true information about the athletes’ functional level as the actual range of motion of the trunk, for instance, can be quantified. Currently, there is no set video recording procedure during the event aimed at determining this true range of motion of the athletes as well as their true compliance during the classification process. In addition, video recording during the event can show if the athletes’ technique follows the rule, indicating that the shot cannot move behind the line defined by the shoulder and the ear. Therefore, the recordings could be used as a medium to settle protests of athletes against referees’ decisions. This aspect was experienced during the Sydney 2000 Paralympic Games where the tapes used in the following study were required on several occasions by referees needing to review their decision.

Implementation of video recording during world-class competition

Theoretical aspects of successful 2-D or 3-D performance analysis of an athletic technique using video recording in experimental conditions have been previously described in detail (Marzan, 1975, Allard, 1995). Some elements of video recording settings during world-class competition could be found for able-bodied shot-putters (Ariel, 1973, Ariel, 1979). However, these elements were not fully relevant since the seated shot-putters use a different technique from the one used by able-bodied athletes. A few studies included key elements of video recording of seated athletes but only in a training environment (Chow and Mindock, 1999, Chow et al., 2000).

While the general principles presented in these articles might be applied to video recording of seated shot-putters during world-class events, adaptations may be required to compensate for the many extra constraints imposed on such data collection, especially if the recording is required to be systematic. For example, a retake of a performance is impossible and every attempt of each athlete must be recorded in order to capture the best puts, which is known only at the end of the event. The recording cannot interfere in any way with the athletes, the officials, the referees or the TV crews. For instance, no active or passive markers may be placed on the athlete. The camera views can be obstructed at any time by several factors such as broadcast TV crews, referees and equipment. Contacts between the operators of the cameras are limited, as no radio communication may be allowed in the stadium. Furthermore, the number of cameras is limited and their position should not interfere with any of the other on-going sporting events.

The systematic video recording of seated shot-putters during actual world-class competition might be eased by the development of discrete, affordable and user-friendly video recording systems matching the requirement of scientific measurements. Most of these systems use two or more compact, high-speed, digital video cameras allowing at least a bi-planar analysis. The use of compact cameras is important for recording in the conditions of a world-class event since the space available is limited. Furthermore, such cameras are not confused with TV cameras, which could be moved on demand. These compact cameras allow transfer of data recorded in a digital format directly onto the computer and avoids the time consuming digitizing process that can lead to a loss of quality of the data. These cameras could acquire data at a rate ranging up to 100 Hz, which should allowed sport scientists to accurately determine the key events of the put (e.g. end of back thrust, instant of release of the shot). This aspect is particularly important since the accuracy of determining the instant of release of the shot is critical in establishing the initial parameters of the shot’s trajectory including the velocity, angle and height of release (Lichtenburg and Wills, 1978, Linthome, 2001).

Need for practical information

The benefits and key elements of the implementation (general principles and technical means available) of the video recording during world-class events have been provided. Nevertheless, there is yet a lack of studies presenting the technical aspects and the outcomes of such video recording.

However, a systematic video recording of seated shot-putters participating in world-class competition was conducted for the first time during the Sydney 2000 Paralympic Games. The primary aim of this recording was to show the principles underlying elite seated shot-putters’ performance. The secondary aim was to determine the true compliance of the athletes during the classification process. These were achieved using a bi-planar kinematic analysis.

The knowledge and understanding of the perturbing factors listed above were essential in the planning of this video recording. However, the literature review conducted in preparation for this systematic video recording revealed that useful and practical information was only partially available. Furthermore, no studies provided information on how to prevent and accommodate these factors as well as their potential impact on camera setup, and the number and usability of attempts recorded.

In conclusion, the literature review brought to light a need for practical information for successful systematic video recording during world-class competitions.


A number of unforeseen difficulties not presented in the literature or listed above were encountered during this initial experience of systematic video recording. Consequently, it was considered desirable to detail these problems so as to recommend protocols to circumvent them at future events.

While the outcomes of the biomechanical analysis as such will be the focus of following articles, the ultimate purpose of this present paper is to share essential and practical information from this experience. This will provide useful benchmark data and guidelines for similar future systematic video recording of the performance of able-bodied and disabled athletes during any world-class event.

The specific purposes of this paper are to provide:

  1. The practical aspects of camera setup for use during the systematic video recording of seated shot-putters suited to kinematic analysis, as well as the location and the field of view of the cameras. This will include essential information about the camera setup for future video recording of seated shot-putters during world-class competition.
  2. The number and the usability of attempts recorded taking into consideration the impact of uncontrollable perturbing factors. This will indicate the amount of usable data that one can expect to collect during world-class competition.
  3. Recommendations to improve the number and the usability of attempts recorded in the conditions of a world-class competition. This section will focus on the camera setup, the number of operators and the reduction of perturbing factors. This will provide a practical guideline for future similar studies.


The video recording as described below aimed to underlie the specific aspects of the elite seated shot-putters performance such as the parameters of the shot’s trajectory and the segmental actions of the athletes (Ariel, 1973, Dessureault, 1978, Lichtenburg and Wills, 1978, Ariel, 1979, Zatsiorsky et al., 1981, McCoy, 1984, Sušanka, 1990, Bartonietz and Borgström, 1995, Tsirakos, 1995, Luhtanen et al., 1997, Chow et al., 2000, Linthome, 2001).

This method could not employ markers on the athletes or any other direct interference with them during the attempt.


A total of 93 seated shot-putters participating in the Sydney 2000 Paralympic Games were selected from 10 classes (F52 to F58) accordingly to the International Stoke Mandeville Wheelchair Sports Federation (ISMWSF) classification system (Laveborn, 2000). This included 30 women (7 in F52-F54, 5 in F55, 10 in F57, 8 in F58) and 63 men (13 in F52, 9 in F53, 13 in F54, 7 in F55, 10 in F56, 11 in F57). The classes F52 and F54 women were grouped together due to the lack of athletes in each class.

The informed consent for each athlete to participate in this study was obtained through the International Paralympic Committee (IPC).

Camera setup for bi-planar video recording

Ideally, a complete understanding of the action of the body segments and the parameters of the shot’s trajectory requires a three-dimensional kinematic analysis as it provides the actual position and orientation of a given segment in relation to another. In principle, a marker or a given anatomical body landmark must be seen simultaneously by at least two and preferably more cameras in order to be reconstructed and used in subsequent three-dimensional kinematic analysis (Abdel-Aziz and Karara, 1971, Marzan, 1975). Consequently, a three-dimensional kinematic analysis of elite shot-putters will necessitate at least four cameras aligned diagonally with each corner of the plate and preferably a fifth one located above the athlete. While this setup appears trouble-free to implement in an experimental framework, it is not suitable and practical for the real events in the field. It is unrealistic to expect that the field of view of each camera on the floor will not be obstructed during the recording of the attempt since about 30 people work in the throwing area alone. Furthermore, it means that accreditations for up to five camera operators must be obtained from the IPC, whose aim is to reduce the number of people working in the area.

Consequently, it is more realistic to attempt a bi-planar analysis under these types of environments. Using two cameras appeared to be a more suitable option because it was less invasive than a three-dimensional analysis and still allowed kinematic analysis in the sagittal and frontal planes which will provide a fair representation of the main rotation between the shoulders and the hips of the athletes. Most of these athletes are seated facing the sector and their putting action occurs essentially in the sagittal plane. As no passive or active markers are allowed on the athlete, automatic tracking could not be used. Thus, the two-camera setup has the advantage of reducing the data that needed to be digitized manually or pointed frame-by-frame. In addition, this method was more cost effective as it required only accreditation for two operators and fewer airfares as well as less time for computing.

The following sections will present the key aspects of the camera setup including the type, the location and the field of view of the cameras.

Type of cameras

Two operators recorded each put using two compact (20 x 20 x 10 cm), high-speed digital video cameras (JVC, Model DVL 9800) set at a sample rate of 100 Hz.


The two cameras were operated simultaneously with one camera on the side and the other in front of the athlete as illustrated in Figure 1, for most of the classes. The camera on the side was placed either on the right or the left of the athlete depending on their throwing hand for the classes F52-F54 and F58 Women. Previous studies placed the second camera behind the thrower (Dessureault, 1978, Chow and Mindock, 1999, Chow et al., 2000). At the Sydney 2000 Paralympic Games, the rear of the plate was a designated area for athletes, assistants and referees and was therefore not accessible. For this reason, the second camera was located in front of the thrower for this study.

A slightly different setup was used for a few classes when the object of research was concerned with a specific aspect of the technique. For example, only one camera was placed in the front for the classes F58 Women, F57 Men and F57 Women since only the rotation in the frontal plane of the athletes in these classes was of interest. Also, the cameras were placed on each side for the class F56 Men in order to accurately determine the position of the upper body segments in the sagittal plane of both sides of the athletes in this class.

A customized calibration frame (2 m length x 1.5 m height x 1 m width) including 43 control points was recorded at the beginning and at the end of each event.

Field of view and position of the cameras

A few pilot studies conducted prior to the Games defined the suitable cameras’ field of view for data analysis including displacements in sagittal and frontal planes of each body segment as well as the determination of the shot’s trajectory. For both cameras, the bottom of the field of view included the full-length (2.29 m) and full-width (1.68 m) of the plate on the ground, used to secure the athlete’s seat. The field of view in the sagittal plane (camera on the side) was enlarged in the direction of the throw to secure the recording of at least the first five frames of the shot’s aerial trajectory (Figure 2). In experimental or training conditions, these fields of view can be obtained by zooming to reduce the perspective error once the cameras were placed at distance from the plate. During this study, the zoom was occasionally used to achieve the appropriate field of view, particularly for the camera in the frontal plane, which had to be placed outside of the sector. The camera on the side was placed relatively closer to the plate in order to reduce the possibilities of intrusion of TV crews, equipment and/or referees in the field of view.

The camera in front of the thrower was placed between 14 to 18 m perpendicular to the width of the plate, while the camera on the side was between 8 to 10 m perpendicular to the length of the plate. The height of both cameras was approximately 1.10 m. The angle between the optical axis of the two cameras and the ground was approximately 900. The positions of the cameras in relation to the plate are presented in Figure 1.

The pixel resolution ranged between 0.95 cm to 1.35 cm for both cameras depending on their positions, which provided sufficient accuracy for further analysis.

Duration of recording

The duration of the recording of each attempt was approximately 7 seconds. The recording started when the referee handed the shot to the athlete, and ended when the shot landed on the ground. Consequently, the recording included the back and forward thrusts of preparation as well as the delivery of the shot. An overall of approximately 100 minutes worth of data was recorded for each camera.

Results and Discussion

Table 1

Table 1 Breakdown of the success rate for systematic video recording of the shot-put event by class

Figure 2

Figure 2 Example of the field of view for the side camera and an obstruction caused by a referee.

Table 1 reports the number of useful attempts recorded, taking into consideration the effect of uncontrollable perturbing factors. This table details the number and the percentage of attempts recorded, not recorded, incomplete, obstructed and usable for both cameras in each class. The percentages are expressed with regard to the total number of attempts expected in each class. This number of attempts expected was determined by the number of athletes and the number of attempts each athlete was allowed to perform according to competition rules (three attempts in the qualification round for all athletes and three further attempts in the final round for athletes ranked in the first six from the qualification round).

Number of attempts recorded

A total of 717 attempts performed by 93 seated athletes from 10 classes was recorded resulting in an average of 88?13% of the expected attempts recorded per class.

One hundred and thirty two attempts, corresponding to an average of 12?13% of the expected attempts, were not recorded. The number of attempts not recorded is particularly important for the F52 Men and F54 Men classes for both cameras. The qualification round of these classes took place on two separate pits because the number of participants exceeded 12 (13 for F52 Men and 14 for F54 Men). This aimed at reducing the duration of the event for these classes. The operator was unable to record 30 attempts in class F56 Men as the position of a referee completely obstructed the view of the athletes as shown in the example in Figure 2. Overall, 20 attempts corresponding to 2.35% of the attempts expected were not recorded by the operator who was under the impression that the first attempt was a warm up.

Usability of the attempts recorded

Here, the term ‘usability’ refers to the potential use of the recording for a complete tracking of the body landmarks for at least five frames before the beginning of the back thrust and after the release of the put.

There were 614 attempts successfully recorded and fully useable for further analysis, corresponding to an average of 74?23% of the expected attempts. The difference between the number of attempts recorded and those useable was due to the number of attempts either incomplete or obstructed.

An attempt was defined as “incomplete” when it was only partially recorded mainly when the operators triggered the acquisition slightly after the beginning of the put. This occurred while the operators were distracted due to an unforeseen event, or when the athletes put the shot immediately without preparation. This occurred during 16 attempts corresponding to 2% of the attempts expected. The number of attempts incomplete was more important for the camera on the side because the operator was more prone to be distracted by people moving around and in front of the camera.

An attempt was defined as “obstructed” when a part of the athlete’s movement was hidden during the put. The camera in the frontal plane was only obstructed for 14 attempts mainly due to TV crews in the field of view. The camera in the sagittal plane was obstructed almost twice as often. Here, one source of obstruction was equipment such as the posts for the safety net used during the discus event, display panels, tables for referees and boxes. As illustrated in Figure 2, the other source of obstruction was the referees. Some puts were obstructed either at the beginning or at the end by the referee who was required to be perpendicular to the athlete so as to have a proper appreciation of their technique (F52-F54 Women, F54 Men).

However, the incomplete or obstructed attempts might be partially useable for further analysis. For example, the parameters of the shot’s trajectory might be determined for the attempts incomplete or obstructed at the beginning while the analysis of the segmental organisation might be impossible. Therefore, the realistic number of attempts useable for analysis was close to 85% of the expected attempts.


In this study, 15% of the attempts were not recorded, 72% were recorded and fully available for analysis, 10% were incomplete and 2% were obstructed (as a percentage of expected attempts).

While these figures may appear satisfactory in the context of training, they might not be considered acceptable for a competition event, particularly if the recording of the attempts performed by the medallists was incomplete or obstructed. For instance, this could jeopardise a study aiming at analysing the technique of the best athletes.

The measures to improve the capture rate emanating from this first experience of systematic video recording during a world-class event are linked to the number and position of the cameras as well as the number of operators.

Number and position of the cameras

It is shown that two cameras were sufficient to produce a systematic video recording for classes with fewer than 12 athletes. However, two additional cameras would be needed for a systematic video recording of classes exceeding 12 athletes as two or more separate pits might be used simultaneously.

It has been demonstrated that it is practical and efficient to place one camera in front of the thrower. The recordings of this camera were less frequently obstructed than those of the side camera. The side camera can be placed on the right of the thrower on most occasions since only four athletes representing less than 6% of all participants were left-handed.

In addition, one may need to compromise the ideal position and remote distance of the camera from the plate (proper field of view with zoom) as this space is likely to be occupied because it overlaps with other events (e.g. other throwing pits or racing track). Our experience demonstrated that the further the cameras were positioned from the plate, the higher the chances of perturbing factors, such as someone walking in front of the cameras.

Number of operators

Video recording in the conditions of world-class competition will always have unpredictable factors, involving the officials, equipment and broadcast TV crews. This is mainly because the work of the officials and broadcast TV crews traditionally and legitimately prevails over a research project. One way to significantly reduce the number of attempts incomplete or obstructed due to these unpredictable factors is to allow the operator to prevent them.

During the video recording, attempts were made to use the remote control to trigger the recording at a distance. This way the operator could start the recording and potentially prevent interferences of the environment. The operator stood nearby the camera and paid particular attention to the surrounding. However, in our experience this strategy generally failed. It did not reduce the number of obstructions and eventually made the triggering of the recording more difficult.

Our experience showed that a better way to prevent interferences of the environment during the recording is to employ an additional operator per camera, particularly on the side camera. Only one operator per camera conducted the video recording in this study. However, another operator could have prevented officials or other people from walking within the field of view. In addition, this would leave the operator behind the camera in a better position to follow the event and closely concentrate on the recording. Furthermore, the operators could take turns conducting the recording and this way the operator behind the camera might be able to maintain the necessary level of concentration required over the three to four hour duration of the event.

Additional facilitating actions

Our experience revealed that a few other actions might help to improve the video recording, such as providing the participants with an informative document and defining a protected zone.

Officials and broadcast TV crews were formally informed prior to each event. They were both most sensitive and cooperative during the video recording sessions. However, it is likely that providing more formal information in advance about the study will increase their awareness and therefore reduce the number of attempts that were incomplete or obstructed in future event. For example, a one-page flyer could be provided to them, via the IPC, prior to the event, informing them of the recording process and its benefits.

In addition, lines corresponding to the field of view of the camera on the side could be drawn on the ground and presented as a sensitive zone. Such a display might help the participants to be more aware of their position in relation to the camera. However, this required prior approval from the organising committee and officials.

Practical implication for further performance analysis during world-class event

It could be concluded from our experience that the guideline for future bi-planar systematic video recording of seated shot-putters during world-class events should include the following key points:

  • Assess thoroughly the feasibility of the cameras’ positions prior to opt for a three-dimensional video recording setup,
  • Use at least two cameras and preferably four cameras for a bi-planar analysis,
  • Place the cameras reaching the relevant field of view as close as possible to the plate,
  • Employ two operators per camera. One could be in charge of the recording while the other prevents interferences from the environment,
  • Provide formal information on the study to officials and broadcast TV crews prior to the event,
  • Draw a line on the ground corresponding to the field of view of the cameras.

It is hoped that this paper will provide valuable information to the sports scientists facing the challenge of conducting performance analysis based on systematic video recording of able-bodied or disabled athletes, such as seated shot-putters, during world-class competitive events.


The authors wish to express their gratitude to Chris Nunn (Australian Institute of Sport), Scott Goodman (Athletics Australia), Dale Hudson (Sydney Academy of Sport), Lyn Phillips (Sydney Academy of Sport), and Chris Cohen (International Paralympic Committee).

The authors wish to thank Dr James Smeathers (Human Movement Studies-QUT) in particular and Professor Mark Pearcy (School of Engineering Systems-QUT) as well as Professor John Evans (School of Engineering Systems-QUT) for their insights and suggestions, which have greatly influenced the content of this manuscript.

This study was partially founded by Athletics Australia and QUT Strategic Links with Industry Scheme.


  1. Abdel-Aziz, Y. I. and Karara, H. M. (1971). Direct linear transformation from comparator coordinates into object space coordinates in close-range photo-grammetry. In ASP Symposium on Close Range Photogrammetry. Falls Church, VA: American Society of Photogrammetry.
  2. Allard, P., Stokes, I.A.F. and Blanchi, J.P. (1995). Three-dimensional analysis of human movement, Champaign, III: Human Kinetics.
  3. Ariel, G. (1973). Computerized biomechanical analysis of world’s best. Track and field quarterly review, 73, 199-206.
  4. Ariel, G. (1979). Biomechanical analysis of shotputting. Track and field quarterly review, 79, 27-37.
  5. Bartonietz, K. and Borgström, A. (1995). The throwing events at the World Championships in Ahtletics 1995, Göteborg: Technique of the world’s best athletes. Part 1: Shot put and hammer throw. New Studies in Athletics, 10, 43-63
  6. Chow, J. W., Chae, W. and Crawford, M.J. (2000). Kinematic analysis of shot-putting performed by wheelchair athletes of different medical classes. Journal of Sports Sciences, 18, 321-330.
  7. Chow, J. W. and Mindock, L. A. (1999). Discus throwing performances and medical classification of wheelchair athletes. Medicine & Science in Sports & Exercise, 99, 1272-1279.
  8. Dessureault, J. (1978). Kinetic and kinematic factors involved in shotputting. Biomechanics, 6B, 51-60.
  9. Higgs, C., Babstock, P., Buck, J., Parsons, C. and Brewer, J. (1990). Wheelchair Classification for Track and Field Events: A performance approach. Adapted Physical Activity Quarterly, 7, 22-40.
  10. Laveborn, M. (2000). Wheelchair Sports: International Stoke Mandeville Wheelchair Sports Federation: ISMWSF track & field classification handbook,, Aylesbury: UK.
  11. Lichtenburg, D. B. and Wills, J. G. (1978). Maximizing the range of the shot put. American Journal of Physics, 46, 546-549.
  12. Linthome, N. (2001). Optimum release angle in the shotput. Journal of Sports Sciences, 19, 359-372.
  13. Luhtanen, P., Bloqvist, M. and Vänttinen, T. (1997). A comparison of two elite shot putters using the rotational shot put technique. New Studies in Athletics, 12, 25-33.
  14. Marzan, G. T. (1975). Rational design for close-range photogrammetry. University of Illinois at Urbana-Champaign. Doctoral dissertation.
  15. McCann, B. C. (1993). The medical disability-specific classification system in sports. In Vista ’93 The Outlook: Proceedings of the International Conference on High Performance Sport for Athletes with Disabilities. (Eds, Steadward, R. D., Nelson, E. R. and Wheeler, G. D.) pp. 275-288. Edmonton: Rick Hansen Centre.Vanlandewijck and Chappel.
  16. McCoy, R. W., Gregor, R.J., Whiting, W.C. and Rich, R.G. (1984). Technique analysis: Kinematic analysis of elite shot-putters. Track Technique, 2868-2871.
  17. Sušanka, P. t., J. (1990). Biomechanical Analysis of the Shot Put. In Scientific Report on the Second IAAF World Championship. pp. 1-77. International Athletic Foundation.
  18. Tsirakos, D. M., Bartlett, R.M. and Kollias, I.A. (1995). A comparitive study of the release and temporal characteristics of shot put. Journal of Human Movement Studies, 28, 227-242.
  19. Tweedy, S. M. (2002). Taxonomic theory and the ICF: Foundation for the Unified Disability Athletics Classification. Adapted Physical Activity Quarterly, 19, 221-237.
  20. Vanlandewijck, Y. C. and Chappel, R. J. (1996). Integration and classification issues in competitive sports for athletes with disabilities. Sport Science Review, 5, 65-88.
  21. Williamson, D. C. (1997). Principles of classification in competitive sport for participants with disabilities: A proposal. Palaestra, 13, 44-48.
  22. Zatsiorsky, V. M., Lanka, G. E. and Shalmanov, A. A. (1981). Biomechanical analysis of shotputting technique. Exercise and Sports Science Reviews, 9, 353-387.
2015-03-27T12:00:36+00:00June 5th, 2006|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off on Video Recording of Elite Seated Shot Putters During World Class Events

A New Market Research Approach in Sport-Data Mining


Numerous organizations in the field of business have shown that great success and lucrative outcomes can be accomplished through implementing data mining. For example, Wal-Mart used data mining and found a link between the sales of babies’ diapers and beer. Based on this result, Wal-Mart placed beer close to the babies’ diapers, which resulted in a significant increase in terms of beer sales (Saban, 2001). Another salient example is American Express. American Express built a data mining model to examine millions of data and calculated “purchase scores”—customer’s propensity to make purchases, which not only provided merchants with valuable information, but also reduced American Express’ marketing expenses (Saban, 2001). As a result, research efforts made in data mining are warranted due to numerous successes accomplished while utilizing it.

Although data mining has been widely and successfully used in the domain of business operations, data mining in sport is just in its infancy (Fielitz & Scott, 2003; Lefton, 2003). In other words, the sports industry has generally been a poor and light user of data mining (Jutkins, 1998). It turned out that few papers related to data mining in the area of sport were found in sport journals. However, Lewis (2004) pointed out that data mining will become a critical component of selling and marketing sports teams. Similarly, the concept of data mining will become main stream in sports as an effective complementary marketing tool in the future (Martin, 2005). As a result, data mining warrants sport marketing researchers’ attention and efforts.

The purpose of the present article was to advocate the data mining approach to be utilized in the sport industry in order to effectively achieve sport organizations’ marketing goals. The organization of the current article is as follows: first, definitions and benefits of data mining were discussed; second, successful cases of application of data mining in sport were illustrated; third, proposed techniques of data mining that are appropriate and potentially useful in the sport industry were described followed by discussions and conclusions.

Definitions and Benefits of Data Mining

Data mining is a process of extracting previously unknown, valid, actionable, and ultimately comprehensible information from large databases and then using the information to make crucial business decisions (Cabena et al., 1998). From a different perspective, Kotler (2003) described data mining as “involving the use of sophisticated statistical and mathematical techniques such as cluster analysis, automatic interaction detection, predictive modeling, and neural networking” (p.54). Most of the definitions of data mining fall into these two aforementioned categories. As a result, from the combination of the two definitions, data mining is the process of using sophisticated mathematical or statistical models to extract valuable, valid, and actionable information from a database to accomplish an organization’s goals. (For similar definitions, also see Berry & Linoff, 2004; Hair, Anderson, Tatham, & Black, 1998).

The benefits of executing data mining are as follows: implementing up-selling, increasing season-ticket sales, monitoring season-ticket usage, raising transplanted-fan ticket sales, and executing cross-selling (James, 2004). Additionally, other benefits include (a) retaining current customers, (b) determining customers’ lifetime value, (c) developing relationships with customers, (d) improving delivery of sales promotion, (e) reinforcing consumers purchase decisions, (f) customizing consumer services, (g) facilitating marketing research, (h) profiling the customers, and (i) identifying the best customers for an organization (Aaker, Kumar, & Day, 2000; “Happy Customer,” 2004; Kotler, 2003).

Cases of Executing Data Mining in Sport

Although data mining has not been as widely employed in sport as it has in business, various successful applications in sport still exist. The following observations demonstrate how effective data mining can be for sport organizations and how sport organizations benefit from implementing data mining.

Dick and Sack (2003) conducted a study about effective marketing techniques in the NBA. They contended that a more effective and efficient way to ensure that advertising messages are received by the target markets was to use data mining. Several NBA teams such as the Cleveland Cavaliers, the Seattle SuperSonics, the Portland Trail Blazers, and the Miami Heat have successfully utilized data mining. The Cleveland Cavaliers created a database that includes customers’ names, addresses, telephone numbers, and other detailed information on the products purchased. By analyzing that database, the Cleveland Cavaliers consistently gave a follow-up call to those who bought tickets from Ticketmaster to determine whether they were interested in other games or events (Bonvissuto, 2005). The Seattle SuperSonics also developed a data mining program to raise its revenues and increase its season ticket holders. Additionally, the Portland Trail Blazers analyzed their customer database to help forecast advertising revenues and spot ticket-sale trends (Whiting, 2001). Finally, Miami Heat officials contend that data mining delivers an even more effective targeted audience than traditional advertising or traditional mass-media marketing. By using data mining, the overall Miami Heat season-ticket renewal rate in 2005 was expected to be around eight-five percent (Lombardo, 2005).

Data mining can also be applied by coaches to identify player patterns that box scores do not reveal, which helps win games by extracting relevant information from the database. In a 1997 playoff series, the Orlando Magic discovered Darrell Armstrong’s talent through data mining and inserted him into the starting lineup. The coach increased Armstrong’s responsibility in this series because the data showed that if Armstrong was on the court, the probability of an Orlando Magic win increased. Finally, the Orlando Magic won two consecutive games, and Armstrong personally won the Sixth Man Award in 1999 (Restivo, 1999). In addition, Brian James, assistant coach of the Toronto Raptors, employed a data mining application to know what kinds of plays opponents will use. Utilizing data mining in this way makes it easier for coaches to make decisions about when and how to position their players for maximum effect (Baltazar, 2000). Francett (1997) and Hudgins-Bonafield (1997) stated that data mining applications help analyze a huge amount of data to reveal winning player combinations for coaches. Moreover, the data mining approach to postgame analysis and improvement takes much less time than the traditional approach—forever rewinding the videotape. Namely, data mining makes analysis more efficient.

In summary, not only have the major league teams adopted data mining to increase ticket sale revenues and season-ticket holders’ renewal rates, but also sport coaches have utilized data mining to achieve their goals and objectives. Information extracted from records about players’ performance enables coaches to position and direct their players in a game. Consequently, data mining is a powerful technique with flexible applicability in sport.

Proposed Techniques for Data Mining in Sport

This section briefly presents an overview of the frequently used statistical models or techniques for data mining in terms of marketing, sales, and customer relationship management. The tasks that have been performed in the area of data mining are as follows: classification, estimation, prediction, and profiling (Berry & Linoff, 2004). The overview will include the definitions and the properties of the models along with the circumstance under which a model should be used. These models include (a) Discriminant Analysis, (b) Logistic Regression, (c) Decision Trees, (d) Artificial Neural Networks, (e) Collaborative Filtering, (f) Market Basket Analysis, and (g) Survival Analysis.

Discriminant Analysis

Discriminant analysis is a statistical method using linear functions to distinguish groups based on the independent variables. Discriminant analysis is the appropriate statistical technique when the dependent variable is categorical and the independent variable is continuous (Hair, Jr., Anderson, Tatham, & Black, 1998; Tabachnick & Fidell, 2001). It is an old and extensively used parametric statistical approach in classification. It works by comparing a weighted sum of the input variables to a constant value in the weights, and the constants are determined in such a way that the least square error of misclassification is minimized (Tabachnick & Fidell, 2001). Sport organizations can use it, for example, to classify customers as high-, medium-, or low-value customers in terms of their monetary contribution to the sport organization. This enables a sport organization to allocate marketing resources more effectively.

Logistic Regression

Logistic regression is a widely used technique for classifying subjects into two mutually exclusive exhaustive categories (Ratner, 2003). In logistic regression, the maximum likelihood estimation is employed to estimate the probability of classifying a subject into a group. The logistic regression is often used as a benchmark in the field of data mining when comparing the accuracy of model prediction. Professional sport teams can employ it to investigate the characteristics of the season ticket holders who end up terminating season ticket purchases and predict the probability of terminating season ticket purchases.

Decision Trees

Decision trees are one of the most popular methods in data mining and are frequently used for data exploration (Borisov, Chikalov, Eruhimov, & Tuv, 2005). Decision trees are a data mining technique that can be used to divide or partition a large collection of heterogeneous data into successively smaller sets of homogeneous data by using a sequence of simple decision rules with respect to a selected target variable (Berry & Linoff, 2004). In essence, decision trees are utilized to partition the data by employing independent variables to identify the subgroups that contribute most to the dependent variable (Chakrapani, 2004). This technique can also be used to classify and/or predict in the sport settings.

Artificial Neural Networks

Artificial neural networks (ANNs) are computer-intensive computational techniques that simulate the function of neural activity in a human brain (Chakrapani, 2004). To put it differently, ANNs are the computational tools for data exploration and model development to help identify patterns or structures in the data (Smith & Gupta, 2002). ANNs consist of three layers of processing units: input layer, hidden layer, and output layer. Since the final decision is binary (0 or 1), the value for the output layer is the predicted value of the decision. If the output value is 0.5 or above, then the decision is assumed to be an acceptance, while if it is 0.5 or below, then the decision is a rejection (Kumar & Olmeda, 1999). Compared to the traditional statistical methods, which are usually linear-based, ANNs use the non-linear approach (Cho & Ngai, 2003) and do not depend on a set of specified procedures. ANNs have been widely used in recent years as a classification technique and have been applied to a variety of business fields including bond rating, bankruptcy prediction, and stock market prediction. ANNs are superior over the regression-type models because of their ability to detect non-linear relationships and to adapt to changing input (Chakrapani, 2004). Sport organizations can use it to predict and classify their customers to better allocate marketing resources, i.e., more accurately segmenting the market and targeting custoemrs.

Collaborative Filtering

Collaborative filtering (CF) is a new technique in the area of data mining, assisting people to make choices based on other people’s choices. Similarly, Berry and Linoff (2004) described collaborative filtering as an approach to making and providing personalized recommendations. The collaborative filtering approach starts with evaluating a history of customer product preferences as well as demographics and ends up with determining similarities so that people who may like the same products will be put together (Berry & Linoff, 2004). Namely, this approach employs the reactions or preference of others within the database as well as their similarity to generating recommendations. Professional sport teams can utilize it to make recommendations or promote sporting events/sport merchandise to their customers based on what other customers purchased or consumed.

Market Basket Analysis

Market basket analysis is a data mining technique aiming to understand point-of-sale transaction data (Berry & Linoff, 2004). In other words, market basket analysis deals with such business problems as which products tend to be purchased together as well as which are most appropriate to promotion (Berry & Linoff, 2004). To perform basket analysis, three levels of market basket data are required: customers, orders, and items. Customer data refers to customer information including customer’s IDs, names, addresses and so on. Order data represents a single purchase event by a customer including the total amount of the purchase, payment type and whatever other data is related to and relevant to this transaction. Item data contains the price paid for the purchased item and the number of items (Berry & Linoff, 2004). Sport organizations can acquire benefits, such as deciding which product should have a promotion, the segmentation of customers, and the identification of the relationships among product items by using this technique.

Survival Analysis

Survival analysis method, also known as Event History Analysis, Reliability Analysis, Time to Failure and Duration Analysis, is developed mainly to deal with the probability that a certain event will occur but also deals with when it will occur (Harrison & Ansell, 2002). Namely, it deals with the time between events (Drye, Wetherill, & Pinnock, 2001). For example, survival analysis can identify when existing customers will re-attend professional sporting events based on their past game attendance records, which provides valuable information for professional sport teams in terms of promotional decision-making.

Discussions and Conclusions

Both advances in information technology and organizations’ needs have facilitated the upsurge of data mining. Even though a data mining approach has been successfully adopted to accomplish a number of organizations’ marketing goals and objectives in business, it is still in the infancy stage in the domain of sport. However, lack of use of data mining in the sport business does not mean that it is not applicable or important in the sport business. Instead, it is a great opportunity for sport businesses to adapt data mining and benefit from implementing it. With correct and appropriate use of data mining, sports organizations can benefit from the strategies and tactics developed from analyzing customer databases.

The development of models or algorithms in the area of data mining is upsurging to fulfill a variety of problems in practice. Various models have been commonly and successfully employed to solve real world problems. Tasks that are performed vary from model to model. Consequently, no rule of thumb exists that explains which model is the best model in solving a practical problem. In other words, the selection of the model depends heavily on the type of problems, the data structure an organization possesses, and the objective of an organization. Therefore, it is critical to have a thorough examination of organizational goals and data structure before choosing data mining techniques.


  1. Aaker, D. A., Kumar., V., & Day, G. S. (2000). Marketing research (7th ed.). NY: John Wiley & Sons, Inc.
  2. Baltazar, H. (2000). NBA coaches’ latest weapon: Data mining. PC Week, 17(10), 69.
  3. Berry, M. J. A., & Linoff, G. S. (2004). Data mining techniques: For marketing, sales, and customer relationship management (2nd ed.). Indiana: Wiley Publishing, Inc.
  4. Bonvissuto, K. (2005). Cavaliers forge fan friendships with strategic database use. Crain’s Cleveland Business, 26(8).
  5. Borisov, A., Chikalov, I., Eruhimov, V., & Tuv, E. (2005). Performance and scalability analysis of tree-based models in Large-Scale Data-Mining Problems. International Technology Journal, 9(2), 143-151.
  6. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., & Zanasi, A. (1998). Discovering data mining: From concept to implementation. NJ: Prentice Hall.
  7. Chakrapani, C. (2004). Statistics in market research. New York: Oxford University Press Inc.
  8. Cho, V., & Nagi, E. (2003). Data mining for selection of insurance sales agents. Expert Systems, 20(3), 123-132.
  9. Dick, R., & Sack, A.L. (2003). NBA marketing directors’ perceptions of effective marketing techniques: A longitude perspective. International Sports Journal, 7(1), 88-99.
  10. Drye, T., Wetherill, G., & Pinnock, A. (2001). When are customers in the market? Applying survival analysis to marketing challenges. Journal of Targeting, Measurement, and Analysis for Marketing, 10(2), 179-188.
  11. Fielitz, L., & Scott, D. (2003). Prediction of physical performance using data mining. Research Quarterly for Exercise and Sport, 74(1), 25.
  12. Francett, B. (1997). The NBA gets a jump on data mining. Software Magazine, 17(9).
  13. Hair, Jr., J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). New Jersey: Upper Saddle River: Prentice Hall.
  14. Happy Customer. (2004, April 1). Happy customer: Creative ways to keep your season ticket holders happy throughout the year. The Magala Report. Retrived May 17, 2005, from
  15. Harrison, T., & Ansell, J. (2002). Customer retention in the insurance industry: Using survival analysis to predict cross-selling opportunities. Journal of Financial Service Marketing, 6(3), 229-239.
  16. Hudgins-Bonafield, C. (1997). Data mining software scores high with the NBA. Network Computing, 8(11).
  17. James, V.L. (2004). Build fan base from your database. Street & Smith’s SportsBusiness Journal. Retrieved June 24, 2005, from SportsBusiness Journal archive database.
  18. Jutkins, R. (1998). Direct marketing: A new strategy in the sports industry. Direct Marketing, 60(10), 34-35.
  19. Kotler, P. (2003). Marketing management. (11th ed.). New Jersey: Upper Saddle River Pearson, Education, Inc.
  20. Kumar, A., & Olmeda, I. (1999). A study of composite or hybrid classifiers for knowledge discovery. Journal on Computing, 11(3), 267-277.
  21. Lefton, T. (2003). NFL flexes database marketing muscle to sell tix, boost viewership. Street & Smith’s SportsBusiness Journal, Retrieved June 24, 2005, from SportsBusiness Journal archive database.
  22. Lewis, J. (2004). Career spotlight: Tom Glick. Street & Smith’s SportsBusiness Journal. Retrieved September 24, 2005, from SportsBusiness Journal archive database.
  23. Lombardo, J. (2005). A new ball game in South Florida: Heat puts twist on marketing. Street & Smith’s SportsBusiness Journal, Retrieved June 24, 2005, from SportsBusiness Journal archive database.
  24. Martin, L. (2005). Customer relations: An overview of how customer data can drive incremental sales for your organization. The Magala Report. Retrived November 3, 2005, from
  25. Ratner, B. (2003). Statistical modeling and analysis for database marketing: Effective techniques for mining big data. New York: Chapman & Hall/CRC.
  26. Restivo, K. (1999). Professional sports teams turn to resellers for an edge. Computer Dealer News, 15(28).
  27. Saban, K. A. (2001). The data mining process: At a critical crossroads in development. Journal of Database Marketing, 8(2), 157-167.
  28. Smith, K., & Gupta, J. (2002). Neural networks in business: Techniques and applications. PA: Idea Group Publishing.
  29. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). MA : Allyn and Bacon.
  30. Whiting, R. (2001). Customers come into focus with combination software. Retrieved June 11, 2005, from the LexisNexis database.
2015-03-27T11:53:07+00:00June 4th, 2006|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on A New Market Research Approach in Sport-Data Mining

Gender, Skill, and Performance in Amateur Golf: An Examination of NCAA Division I Golfers


In a previous study, it was found that male amateur golfers must possess a variety of shot-making skills to be successful and that relative to driving ability, putting skills and reaching greens in regulation contribute more to explaining tournament success. This present research extends these findings by expanding the investigation to analyze the performance determinants of both female and male amateur golfers. In so doing, we are able to test for the presence of gender-based differences in skill levels and in the relationship between skills and tournament performance. Using a sample of NCAA Division I male and female golfers who participated in tournament play during 2004-2005, our research offers two interesting observations. First, on average, male and female amateur golfers possess different levels of shot-making skills. Second, these disparate skills influence tournament performance differently across genders. Although the causality of these gender-based disparities cannot be identified with certainty, several plausible explanations are considered.


In an earlier research study of amateur golfers, we empirically examine the relationship between a male golfer’s tournament performance and a set of shot-making skills (Callan and Thomas, 2004). This initial investigation was the first of its kind to focus on a sample of NCAA Division I male golfers. Statistically, those findings validate analogous research on the performance of professional golfers. What we discovered is that male amateur golfers, like their professional counterparts, must possess a wide variety of shot-making skills to be successful. Moreover, we found that, relative to driving ability, putting skills and reaching greens in regulation contribute more to explaining the variability in a player’s success. This present study extends that research to study both men and women amateur golfers and, in so doing, allows us to test for the presence of gender-based differences in skill levels or in any skill-to-performance relationship.

That gender-specific skill differences exist in the game of golf is explicitly recognized by the United States Golf Association (USGA), which is the governing body for the rules of golf. For example, in its rating system of golf courses, the USGA specifically defines a bogey golfer and a scratch golfer according to the golfer’s gender, as noted below (United States Golf Association, 2005).

Bogey Golfer:

“A male bogey golfer is a player who has a Course Handicap© of approximately 20 on a course of standard difficulty. He can hit tee shots an average of 200 yards and can reach a 370-yard hole in two shots at sea level.

A female bogey golfer is a player who has a Course Handicap© of approximately 24 on a course of standard difficulty. She can hit tee shots an average of 150 yards and can reach a 280-yard hole in two shots at sea level.”

Scratch Golfer:
“A male scratch golfer is a player who can play to a Course Handicap© of zero on any and all rated golf courses. A male scratch golfer, for rating purposes, can hit tee shots an average of 250 yards and can reach a 470-yard hole in two shots at sea level.

A female scratch golfer is a player who can play to a Course Handicap© of zero on any and all rated golf courses. A female scratch golfer, for rating purposes, can hit tee shots an average of 210 yards and can reach a 400-yard hole in two shots at sea level.”

Following these and other gender-specific distinctions made by the USGA, it is reasonable to expect that on average, male golfers are able to drive the ball longer distances off the tee, and female golfers have shorter approach shots to each green. Similar assertions, some with supporting data, are found in the literature, for example, Shmanske (2000) and Wiseman, Chatterjee, Wiseman, and Chatterjee (1994). Such observations motivate the need to learn how such gender-based skill differences translate into scoring performances under actual tournament conditions.

While no existing research papers examine this skill-to-performance relationship across male and female amateur golfers, there are studies that investigate the existence and degree of gender differences among professional golfers. Generally in such investigations, two questions are examined:

  1. Do the data support the hypothesis that there are statistically different shot-making skills across male and female golfers?
  2. How do shot-making skills influence a golfer’s tournament performance, and is this set of relationships gender specific?

To illustrate, we offer a few salient examples of this research and an overview of the approach used in each case.

Using performance measures for the 1992 season, Wiseman, Chatterjee, Wiseman, and Chatterjee (1994) investigate the influence of gender differences across golfers in the Professional Golfers’ Association (PGA), Senior PGA (SPGA), and Ladies PGA (LPGA). Overall performance is measured in their study by the average score per round of golf, and the shot-making skills considered are driving distance, driving accuracy, hitting greens in regulation, and putting. What these researchers find is that, on average, male PGA golfers drive the ball farther and hit a larger percentage of greens in regulation than female professionals. Driving accuracy was approximately the same across genders. Because the PGA and LPGA do not collect putting data in the same manner, no gender comparisons could be made about putting ability. However, using multiple regression analysis, Wiseman, et al. (1994) discover that the two most influential skills for female golfers are putting and hitting greens in regulations. These two skills explained 88 percent of the variability in an LPGA member’s average score per round. However, male PGA golfers need a more well-rounded game, as indicated by the importance of all shot-making skills in determining their tournament performance.

In a more recent study, Moy and Liaw (1998) examine golfers’ shot-making skills and tournament performance during the 1993 tournament season for the same professional tours used by Wiseman, et al. (1994). For the most part, Moy and Liaw’s findings agree with those of Wiseman, et al. (1994) regarding PGA golfers’ skills at driving the ball and reaching greens in regulation relative to LPGA golfers. However, they add a variable that captures sand saves, measured as the percentage of time a player gets out of a greenside bunker and scores par or better on the hole. They find that, on average, PGA golfers achieve a higher proportion of sand saves relative to their female counterparts in the LPGA. Given that no comparable putting statistic was available across the two tours, Moy and Liaw were unable to test for gender differences with respect to putting skills.

Shmanske (2000) statistically compares the skill-to-performance relationship for a sample of PGA golfers and LPGA golfers for the 1998 tournament season. In addition to the conventional shot-making skills, Shmanske constructed a comparable putting skill measure for each set of tour professionals. Overall, his results on gender differences are consistent with those of previous researchers. Specifically, he finds that male professional golfers drive the ball farther off the tee, have a higher sand save percentage, and demonstrate a higher putting proficiency. For the other key shot-making skills, namely driving accuracy and hitting greens in regulation, Shmanske observes no meaningful difference across genders.

While these studies have contributed to our understanding of gender differences in professional golf, no analogous investigations have been done for amateur golf. Recognizing the importance of this issue, we extend our previous study of amateur golfers (Callan and Thomas, 2004) to an analysis of skills and performance across male and female amateurs. Using the fundamental framework suggested by Wiseman, et al. (1994) and others, we use a two-pronged approach to our investigation. First, we statistically test for gender-specific skill differences at the amateur level. Second, we use regression analysis to assess the influence of a player’s shot-making skills on tournament performance and statistically determine if these skill-to-performance relationships are affected by gender.



To conduct our investigation, we use a subset of NCAA Division I male and female golfers who participated in at least one tournament during the 2004–2005 season. Data on members of all Division I teams are not available. The colleges and universities represented in this study are identified in Table 1 along with the number of players on each team, the number of tournaments played during the season, and the average length in yards of the typical course on which the teams played. Most of these data are obtained from Golfstat, Inc. (2005), which is accessible on the Internet at

Notice that the data presented in Table 1 suggest some important distinctions across genders. At a fundamental level, we observe that male golf teams, on average, comprise between 8 and 9 players, while female teams are smaller, averaging between 7 and 8 players. We also note that males play in slightly more tournaments than females, averaging 10.8 for males and 9.7 for females. Consistent with the USGA’s rating system, we also observe that the average male golfer plays courses that are almost 1,000 yards longer than those played by females, specifically 7,042 yards for men versus 6,104 for women. As a consequence, one might infer that male golfers place a higher premium on driving distance, while female golfers might focus more on developing their short game skills.


For each of the universities included in this research, Golfstat, Inc. collects and reports statistics for player skills and tournament performance. In this study, we use data for the 2004–2005 NCAA Division I tournament season for men and women teams from the same group of institutions. Just as we argue in our 2004 study, AVERAGE SCORE per round is a viable measure of tournament performance, since earnings are not relevant at the amateur level. Moreover, Wiseman et al. (1994) assert that correlation results are actually stronger when scoring average, as opposed to earnings, is used. As for the shot-making skills, we use a set of variables that collectively capture each player’s golf game from tee to green. Among these are measures of driving ability, fairways hit, greens in regulation, sand saves, and putting, which follows the approach used in Callan and Thomas (2004). We briefly discuss each measure in turn, starting with those capturing a player’s long game.

To capture each amateur’s ability to drive the golf ball, we use the variable EAGLES, defined as the cumulative number of recorded eagles (i.e., two strokes under par on any hole) a player makes each season. This variable serves as a proxy for driving distance, which is a statistic not reported by Golfstat. In support of this proxy measure, Dorsel and Rotunda (2001) report a positive correlation between a player’s driving distance and the number of eagles made. Related to driving distance is accuracy in driving the ball into the fairway. To measure this skill, we use the variable FAIRWAYS HIT, measured as the percentage of time a player drives the golf ball off the tee and into the fairway. We also define a variable called GREENS IN REGULATION (GIR) as the percentage of time a player reaches a green in the requisite number of strokes, specifically one for a par three, two for a par four, and three for a par five. This follows the work of Belkin, Gansneder, Pickens, Rotella, and Striegel (1994), who assert that GIR captures a player’s iron skill and success in reaching a green within the regulation number of strokes.

As for a player’s short game, we employ two skill variables that are commonly used in the literature. The first is SAND SAVES, which measures the percentage of time a player gets out of a greenside bunker and achieves a score of par or better. The second is a player’s ability to putt the ball into the hole once on the green. To capture this shot-making skill, we use the variable PUTTS PER ROUND, which measures the average number of putts a golfer makes per round of golf. This follows Belkin, et al. (1994).

Beyond the effect of shot-making skills, we hypothesize that a golfer’s overall performance is influenced by two other key factors – a player’s experience level and any associated team effects. Recognizing experience as a determinant of a golfer’s performance follows Shmanske (1992) and others. In the professional literature, experience is typically captured by the number of years a player has been a professional player. For this analysis of amateur golfers, we construct two experience variables. One is the variable ROUNDS, which is simply the number of tournament rounds completed by each player during the 2004–2005 season. This variable effectively measures a player’s short-term experience, because it captures the way each additional round played in a season adds to the knowledge a player can call upon in subsequent rounds. The second experience measure controls for longer-term cumulative experience and is modeled through a set of dummy variables that reflect the player’s academic age, specifically FRESHMAN, SOPHOMORE, JUNIOR, or SENIOR. The underlying expectation is that the more advanced is a player’s academic age, the more collegiate golfing experience has been gained and, therefore, the lower the expected average score.

The other theorized non-skill determinant of amateur performance is characterized as team effects. These are expected to arise from various factors, including the expertise and experience of the coach and the relative challenge of the courses played by the team. Coaches can directly affect the success of each player in myriad ways, such as through mentoring, leadership, instruction, and guidance. As a leader, the coach is responsible for setting team strategy and for determining the extent of each player’s tournament participation. As an instructor, the coach guides and motivates the development of each player’s athleticism and skills. Hence, collegiate golfers can achieve varying levels of success in the sport based in part on the expertise and experience of their coach, holding skill levels constant.

Likewise, a player’s amateur performance might be affected by the courses played by their team, because course venues, and hence their relative difficulty, vary across collegiate teams. Therefore, a member of a team that plays on relatively easy courses in a tournament season might enjoy a lower average score for that season, and, of course, the converse is true. To account for such team effects, we construct university-specific dummy variables for each player, whereby each identifies the team to which a player belongs.


For this study, two conventional statistical procedures were used to analyze the skill and non-skill determinants of amateur golf performance, controlling for gender. One is the two-sample t-test, which was used to statistically examine the difference between mean values of male and female shot-making skills. The second procedure is the use of a multiple regression model that estimates the influence of skill, experience, and team effects on a player’s tournament score, holding constant all other score determinants. Ordinary least squares (OLS) is used to derive the regression estimates.

Results and Discussion

In Table 2, we present descriptive statistics for the sample of 179 amateur golfers, comprising 94 males and 85 females. At the collegiate level, tournaments generally consist of 3 rounds of golf, and each round comprises 18 holes of play. In our sample, the average NCAA Division I male golfer had an average score per round of approximately 75 strokes during the 2004–2005 season. In comparison, the average female golfer had a higher average score per round of about 79 for the season.

Based on the two experience variables, the average male amateur has more experience than the average female. For short-term experience, we observe that males play slightly more than 24 rounds of golf in the season, while females play fewer rounds, at about 22. As for longer-term experience based on academic age, approximately 61 percent of male team members are juniors and seniors, while the comparable value for females is lower at 47 percent.

Turning our attention to shot-making skills, we observe the following distinctions across genders. The average male golfer hits approximately 64 percent of fairways and reaches greens in the regulation number of strokes 60 percent of the time. Female golfers, on the other hand, hit 70 percent of fairways and reach greens in regulation 50 percent of the time. Over the course of a round, a male golfer makes slightly less than 31 putts, while the female golfer makes slightly more than 32 putts. For sand saves, the data show that the amateur male golfer makes par or better when hitting from a bunker 39 percent of the time, which is notably higher than the amateur female golfer, who has a comparable success rate of 29 percent. Lastly, over the course of the 2004–2005 season, the average male player makes 1.8 eagles, while the average female had 0.34 eagles, suggesting superior driving distance for males.

For each variable in the table, we also find the coefficient of variation for each gender group. As a measure of dispersion, this statistic contributes useful information about performance and skills across genders. Notice that for AVERAGE SCORE, the coefficient of variation is smaller for males than females. The same is true for all shot-making skill variables with the single exception of PUTTS PER ROUND. What these results imply is that there is a greater degree of competition among amateur male golfers than among females, an interpretation that follows Moy and Liaw (1998).

By simple observation, these data suggest that there may be statistically significant differences in skill levels across genders. To formally examine this theory, we use two-sample t-tests across the gender-specific skill variables and present our findings in Table 3. Not surprisingly, there are indeed statistically significant differences across genders (i.e., p < 0.0001) for all shot-making skills. Specifically, NCAA Division I male golfers, on average, possess superior shot-making skills relative to their female counterparts for EAGLES, GIR, PUTTS PER ROUND, and SAND SAVES. These findings generally agree with those found in research studies of professional golfers (Wiseman, et al., 1994; Moy and Liaw, 1998; Shmanske, 2000). The opposite relationship holds for FAIRWAYS HIT, the measure of driving accuracy, for which female collegiate golfers are statistically superior to males, on average.

While certainly of interest, the observation of gender-specific skill differences does not ensure that they translate into comparable changes in tournament performances. Investigation of this important issue requires the use of a multiple regression model. To that end, we specify a model to estimate the relationship between an amateur golfer’s average score and each of the determinants identified previously, specifically the set of five shot-making skills, the two experience measures, and the team dummy variables. To identify whether these determinants affect average score differently across males and females, we explicitly control for gender through the use of an interactive binary variable, FEMALE. This variable equals 1 if the golfer is female and 0 if male. It enters the model by itself as well as multiplicatively with each of the other explanatory variables. That way, each score determinant enters the model directly to represent males and multiplicatively with FEMALE to represent any incremental differences for females. In so doing, the estimation results quantify not only how shot-making skills, experience, and team effects influence average score but also whether those effects vary across genders.

The results of this multiple regression analysis are given in Table 4. Based on the adjusted R2 statistic, the regression model explains approximately 95 percent of the variability in a golfer’s tournament performance. Of particular interest are the gender-specific estimates that communicate the relative importance of each shot-making skill on overall performance, holding constant all other skills, team effects, and player experience. We also can assess the influence of all non-skill factors on a player’s average score independent of skill levels, and again, we can do so by gender. The estimated values for male golfers are listed in the first two columns of the table, and the estimates of any incremental differences for females are given in the second pair of columns.

To determine if the gender-based distinctions are collectively relevant, we conducted several F-tests, the results of which are shown at the bottom of Table 4. Other than the test for academic age variables for which gender differences are only marginally significant, all other F-tests indicate that gender differences exist and are statistically significant. These include tests for the overall model, for shot-making skills, and for team effects. These are important findings, which, to the best of our knowledge, have not yet been identified in the literature. They communicate far more than differences in skill levels across males and females. Rather, these results suggest that improvements in skill levels do not translate equivalently to better performance outcomes for both gender groups.

Next consider the individual results for each of the explanatory variables, starting with the set of shot-making skills. With the exception of FAIRWAYS HIT, each shot-making skill has a statistically significant influence on a player’s tournament performance, and each bears the expected algebraic sign. We also find that for several of these shot-making skills, gender differences exist and are statistically significant. Specifically, male golfers gain more tournament success than females from improving SAND SAVES. Conversely, increasing the GIR proportion statistically improves a female golfer’s tournament performance more than it does for a male. An analogous argument is relevant to reducing PUTTS PER ROUND. There are no apparent gender-based differences for EAGLES. Perhaps this outcome is due to the USGA establishing different tee boxes for males and females, which may correctly adjust for any inherent gender-based differences in driving ability.

As for the experience measures, the results suggest that short-term experience measured through ROUNDS does improve tournament performance and does so with no difference between the genders. For cumulative experience, captured through the academic age variables, FRESHMAN, SOPHOMORE, JUNIOR, and SENIOR, only the results for females are reasonable. Specifically, we find that female sophomores achieve higher average scores relative to seniors (the suppressed academic age variable). This makes sense, suggesting that greater collegiate experience improves performance. For males, the parameter on SOPHOMORE is significant, but its algebraic sign is negative. This outcome may be an artifact of the data sample, such as an unusually talented group of male sophomores in the 2004–2005 tournament season. It might also be related to the fact that in this sample, there are about 50 percent more male seniors than male sophomores, while for females there are 12.5 percent fewer seniors than sophomores.

We further find that team effects exist for certain universities. Golfers from East Tennessee State, on average, have higher average scores for the season than those from Vanderbilt University (the suppressed variable), regardless of gender. The same is true for players at the University of Texas. This implies that Vanderbilt University may have a better coaching staff and/or the Vanderbilt teams may play on less challenging courses. Interestingly, the team effect results also suggest a gender difference for teams at Indiana University and Kent State. In both cases, female teams perform at a lower level (i.e., have higher average scores), than their male counterparts, holding constant all other score determinants, including shot-making skills.

To quantify the effect of these differences, we follow Shmanske (2000) and compare the fitted value of average score for an arbitrarily defined male (e.g., a sophomore at Kent State University), with a predicted value that uses female parameter estimates with mean values of male score determinants. What we find is that the fitted value for average score is 73.88, but the predicted value is 75.38. This helps to underscore how the skill-to-performance relationship for females causes their scores to be higher than males, holding all else constant. Using the same approach for an analogously defined female (i.e., a sophomore at Kent State University) yields a fitted value for average score of 79.51, but a predicted value of 77.35, using mean values of female score determinants with male parameter estimates. Again, the difference indicates that the skill-to-performance relationship for males contributes to their scores being lower than that of females, holding skills, experience, and team effects constant.

That these collective results provide some evidence of gender-specific differences in how various factors affect performance is an interesting set of findings. That is, we now have a better sense of why amateur golf performance varies across gender groups. The commonly discussed observation of different average scores for males versus females seems not to be solely a function of differences in skill levels or years of experience but also a function of how changes in score determinants affect golfer performance.

How might we explain these differences? Although definitive answers are beyond the scope of this research, we offer three possible explanations based on selected analyses and theories that have been explored in the literature. These are based on: (1) differences in the degree of competition; (2) varying opportunities within and across university athletic programs; and (3) dissimilar physiological and psychological factors. We present a brief overview of each, which may encourage further investigation of these and other possible explanations.

First, based on the calculations of the coefficient of variation discussed previously and presented in Table 2, there seems to be a greater degree of competition among male amateurs. This is not unlike what Moy and Liaw (1998) find in their analysis comparing professional male and female golfers. More competition among males might encourage longer practice sessions and greater concentration, which in turn should yield higher skill levels and correspondingly greater improvements in performance as those skills develop. Somewhat related to this issue is that male amateurs might also be more highly motivated to practice and may compete more aggressively because of greater earnings potential at the professional level than females. This reality is based in part on higher purses offered on the PGA tour than on the LPGA tour. In fact, some studies of professional golf suggest that golfer success depends on effort, which in turn is influenced by the skewed distribution of tournament purses, meaning that performance improves when the stakes are higher (Ehrenberg and Bognanno, 1990; Shmanske, 2000).

Second, there may be disparate expertise in the coaching staffs and/or significant differences in course difficulty for male teams relative to female teams. This might be the case across institutions or it may arise within a university’s athletic department. The source of such differences is important, since the provision of unequal opportunities based on gender is a violation of Title IX of the Educational Amendment Act of 1972, in which Section 106.41 pertains specifically to athletic programs. Part C of that section identifies several factors that are to be considered when assessing the provision of equal opportunities to both sexes. These include the provision of equipment and supplies, the scheduling of games and practice time, the opportunity to receive coaching and academic tutoring, and the provision of practice and competitive facilities (U.S. Department of Education, 1972).

Third, it is often argued in both the common press and professional journals that gender differences in golf skills and performance might be attributable to physiological or psychological distinctions between males and females. The nature and validity of such arguments are being studied and intensely debated in the literature, and hence no definitive conclusion can be offered here. However, we can identify some of the more salient elements of these arguments, and suggest that they may help to explain the gender differences observed in our sample of amateur golfers.

Some researchers focus on psychological factors that may have differing effects on the play of male and female golfers. To illustrate, consider that Hassmen, Raglin, and Lundqvist (2004) identify a significant correlation between the variability of amateur male golfers’ somatic (or physiological) anxiety levels and the variability of their golf scores. However, Krane and Williams (1992) find no such relationship for their sample of amateur female golfers.

Others ascribe gender-based performance differences to physiological attributes. A common assertion in the professional golf literature is that men’s larger physical size and greater strength explains their ability to drive the ball further than females, and this may in turn explain the lower mean golf scores achieved by males. See, for example, Moy and Liaw (1998). However, others argue that such an assertion is incorrect, because driving a golf ball requires more skill than brute strength alone would provide (Shmanske, 2000). Indeed, in a sophisticated study of the biomechanics of golf, Hume, Keogh and Reid (2005), analyze the two main movements in golf – the swing and the putt, and show that golfers must possess strength, flexibility, and timing to achieve the distance and accuracy necessary for success. Hence, observed gender differences in shot-making skills might be linked to dissimilarities in any or all of these attributes. Some evidence of this hypothesis is offered by Myers, Gebhardt, Crump, and Fleishman (1993), who find within their tests of male and female physical abilities that males scored higher than females on tests of strength and stamina, while females scored higher on tests of flexibility. These findings might explain why male golfers generally drive the ball farther than women and why females typically achieve greater driving accuracy, results found in our analysis and others.


In our previous study of NCAA Division I male golfers, we identified the relationship between an amateur golfer’s tournament performance and various shot-making skills (Callan and Thomas, 2004). This present investigation advances this work by extending the analysis to both females and males. In so doing, we are able to examine whether the skill levels of amateur female golfers differ from those identified for males. Taking this one step further, we also are able to estimate the relationship between each shot-making skill and overall performance for males and females and specifically test for any statistically significant gender differences. To our knowledge, this is the first such study to examine gender differences in amateur golf.

Using a sample of NCAA Division I male and female golfers who participated in tournament play during 2004-2005, our empirical estimation and subsequent analysis supports two important conclusions. First, male and female amateur golfers, on average, possess dissimilar levels of various shot-making skills, and these differences indeed are statistically significant. Such dissimilarities are consistent with the literature examining gender distinctions among professional golfers. In this case, we find that on average, NCAA Division I male golfers possess superior shot-making skills relative to females for all shot-making skills except FAIRWAYS HIT, for which the opposite is true. Second, the manner in which shot-making skills influence tournament performance is not independent of gender. For example, male golfers achieve greater performance improvements by improving SAND SAVES, while females gain more from increasing the GIR proportion and from reducing PUTTS PER ROUND.

Both sets of results are of interest because they improve our understanding of the complexities of amateur golf tournament play. Moreover, through statistical testing, they validate anecdotal evidence of differing skill levels and performance outcomes across male and female collegiate teams. In so doing, the findings suggest the need for further research to learn more about these distinctions and, if necessary, to suggest changes in tournament play that recognize, and perhaps correct for, these disparities.

Although the root causes of gender-based differences in NCAA Division I golf cannot be identified, we do offer several plausible explanations based on research findings from the economics, sports medicine, and physiology literatures. First, we offer the possibility that the higher degree of competition among male golfers may incite more practice and more intensity of play, which in turn may translate into superior skills and/or tournament scores. Second, we suggest that skill and performance distinctions may be related to differences in facilities, coaching, and/or varying course venues. Further study is needed to identify these differences and to determine if there are associated implications for Title IX. Lastly, we consider the role of physiological and psychological factors in explaining gender-specific skill levels and performance in sports, an area that has been, and likely will continue to be, studied in earnest.

We believe that our study and its associated findings are of interest in their own right and contribute to the literature examining both professional and amateur golf. However, it is our hope that this work will have broader implications by encouraging new research in amateur golf and other sports aimed at learning more about the skill-to-performance relationship and the influence of gender and other factors on this important connection in amateur and professional sports.


  1. Belkin, D.S., Gansneder, B., Pickens, M., Rotella, R.J., and Striegel, D. (1994) “Predictability and Stability of Professional Golf Association Tour Statistics.” Perceptual and Motor Skills, 78, 1275-1280.
  2. Callan, Scott J. and Thomas, Janet M. (2004) “Determinants of Success among Amateur Golfers: An Examination of NCAA Division I Male Golfers.” The Sport Journal, 7 (3). Available at
  3. Dorsel, T. N. and Rotunda, R. J. (2001) “Low Scores, Top 10 Finishes, and Big Money: An Analysis of Professional Golf Association Tour Statistics and How These Relate to Overall Performance.” Perceptual and Motor Skills, 92, 575-585.
  4. Ehrenberg, Ronald G. and Bognanno, Michael L. (1990) “Do Tournaments Have Incentive Effects?” Journal of Political Economy, 96, 1307-1324.
  5. Golfstat, Inc. (2005) “Customized Team Pages-Men and Women,” (accessed August 16, 2005), various teams.
  6. Hassmen, Peter; Raglin, John S.; and Lundqvist, Carolina (2004) “Intra-Individual Variability in State Anxiety and Self-Confidence in Elite Golfers.” Journal of Sport Behavior, 27 (3), 277-290.
  7. Hume, Patria A., Keogh, Justin, and Reid, Duncan. (2005) “The Role of Biomechanics in Maximizing Distance and Accuracy of Golf Shots.” Sports Medicine, 35(5), 429-49.
  8. Krane, Vikki and Williams, Jean. (1992) “Path Analysis Examining Relationships among Cognitive Anxiety, Somatic Anxiety, State Confidence, Performance Expectations, and Golf Performance. “ Journal of Sports Behavior, December, 15(4), 279-96.
  9. Myers, David C.; Gebhardt, Deborah L.; Crump, Carolyn E.; and Fleishman, Edwin A. (1993) “The Dimensions of Human Physical Performance: Factor Analysis of Strength, Stamina, Flexibility, and Body Composition Measures.” Human Performance, 6 (4), 309-44.
  10. Moy, R. L. and Liaw, T. (1998) “Determinants of Professional Golf Tournament Earnings.” The American Economist, 42, 65-70.
  11. Shmanske, Stephen (2000) “Gender, Skill, and Earnings in Professional Golf.” Journal of Sports Economics 1(4), 385-400.
  12. United States Department of Education. (1972). “Title IX of the Educational Amendment Act.”(Available at
  13. United States Golf Association (USGA) (2005), “Course Rating™ Primer.” (Available at, accessed April 25, 2006.)
  14. Wiseman, F., Chatterjee, S., Wiseman, D., and Chatterjee, N. (1994) “An Analysis of 1992 Performance Statistics for Players on the U.S. PGA, Senior PGA, and LPGA Tours.” In A. J. Cochran and M. R. Farrally (Eds.), Science and Golf: II. Proceedings of the World Scientific Congress of Golf. London: E & FN Spon. 199-204.
Coastal Carolina University 10 11 6971
5 10 5994
Ea. Tenn. State University 9 10 7029
7 9 5978
Fresno State University 8 14 6924
8 11 6080
9 11 7035
8 10 6120
Kent State University 8 10 7017
7 11 6111
University of Kentucky 7 10 7091
11 11 6162
University of New Mexico 8 11 7128
6 8 6090
University of So. California 9 11 6934
10 9 6115
Texas A & M University 10 11 7066
9 10 6187
University of
8 10 7218
8 9 6169
Vanderbilt University 8 10 7045
6 9 6139
(std. deviation)

Source: Golfstat, Inc. (2005) and individual team Web pages.

Score 74.97 79.23 2.22 3.53 69.95 73.50 81.33 94.45 0.030 0.045
Eagles 1.80 0.34 2.23 0.61 0.00 0.00 9.00 2.00 1.239 1.794
Fairways Hit 0.64 0.70 0.08 0.09 0.36 0.47 0.86 0.88 0.125 0.129
Greens in Regulation 0.60 0.50 0.08 0.10 0.33 0.16 0.81 0.65 0.133 0.200
Putts per Round 30.83 32.31 1.42 1.29 23.00 29.84 35.33 35.71 0.046 0.040
Sand Saves 0.39 0.29 0.15 0.13 0.00 0.06 1.00 1.00 0.385 0.448
Rounds 24.11 22.31 12.83 8.89 3.00 3.00 43.00 36.00 0.532 0.398
Freshman 0.17 0.29 0.38 0.46 0.00 0.00 1.00 1.00 2.235 1.586
Sophomore 0.22 0.24 0.42 0.43 0.00 0.00 1.00 1.00 1.909 1.792
Junior 0.28 0.26 0.45 0.44 0.00 0.00 1.00 1.00 1.607 1.692
Senior 0.33 0.21 0.47 0.41 0.00 0.00 1.00 1.00 1.424 1.952

NOTE: Basic statistics for each university dummy variable are available from the authors upon request.

Variable Mean Difference
(Male – Female)
Standard Error* t-statistic p-value
Eagles 1.4567 0.2501 5.82 <.0001
Fairways Hit –0.0540 0.0128 –4.24 <.0001
Greens in Regulation 0.0982 0.0132 7.46 <.0001
Putts per Round –1.4810 0.2036 –7.27 <.0001
Sand Saves 0.0984 0.0216 4.56 <.0001

*Standard error calculation assumes male and female populations have equal variances.



Intercept 69.40 *** Female Intercept –15.24 ***
Shot-Making Skill Variables Shot-Making Skill Variables
Eagles –0.10 ** (Female)(Eagles) 0.22
Fairways Hit 0.05 (Female)(Fairways Hit) –0.40
Greens in Regulation (GIR) –21.86 *** (Female)(GIR) –4.08 *
Putts per round 0.64 *** (Female)(Putts per Round) 0.53 ***
Sand Saves –1.32 ** (Female)(Sand Saves) 1.68 **
Experience Variables Academic Age Variables
Rounds –0.03 *** (Female)(Rounds) 0.01
Junior 0.05 (female)(Junior) –0.23
Sophomore –0.41 * (Female)(Sophomore) 0.68 *
Freshman 0.08 (Female)(Freshman) 0.02
Team Variables Team Variables
Coastal Carolina 0.44 (Female)(Coastal Carolina) 0.95
East Tennessee State 1.11 *** (Female)(East Tennessee) 0.49
Fresno State 0.58 (Female)(Fresno State) –0.14
Indiana University –0.21 (Female)(Indiana University) 1.77 **
Kent State –0.34 (Female)(Kent State) 1.13 *
Univ. of Kentucky 0.56 (Female)(Univ. of Kentucky) 0.35
Univ. of New Mexico 0.32 (Female)(Univ. of New Mexico) 0.07
Univ. of Southern California 0.44 (Female)(Univ. of Southern California) –0.55
Texas A&M University 0.06 (Female)(Texas A&M University) 1.01
University of Texas 0.99 ** (Female)(University of Texas) –0.56
F-Statistic 82.75
(p-value< 0.001)
R-Squared 95.87
Adjusted R-Squared 94.71
F-Statistic (no gender differences overall) 2.83
(p-value < 0.001)
F-Statistic (no gender differences with respect to shot-making skills) 2.84
(p-value = 0.012)
F-Statistic (no gender differences with respect to academic age) 2.10
(p-value = 0.103)
F-Statistic (no gender differences with respect to team variables) 2.39
(p-value = 0.012)

* significant at the 0.10 level, assuming a one-tailed test of hypothesis for skills and two-tailed test elsewhere
** significant at the 0.05 level, assuming a one-tailed test of hypothesis for skills and two-tailed test elsewhere
*** significant at the 0.01 level, assuming a one-tailed test of hypothesis for skills and two-tailed test elsewhere

2015-03-27T11:50:33+00:00June 3rd, 2006|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology, Women and Sports|Comments Off on Gender, Skill, and Performance in Amateur Golf: An Examination of NCAA Division I Golfers