Latest Articles

Book Review: Historical Dictionary of the Olympic Movement (3rd Edition)

March 14th, 2008|Contemporary Sports Issues, Sports Studies and Sports Psychology|

The third edition of the Historical Dictionary of the Olympic Movement
presents readers with a comprehensive reference guide covering the modern
Olympic movement, including chronologies, dictionary entries, appendixes,
and references.

The chronological timeline outlines key events related to the Olympic
movement starting with the Ancient Olympic Games and continuing through
the 21st Winter Olympics of 2010. There is also a second chronology which
provides valuable information on the Summer Olympiads and Winter Olympic
Games. The brief descriptions of each event include specifics about the
site selection, the number of participants, notable individual performances,
and other prominent happenings.

The book contains a dictionary that has hundreds of entries on the sporting
events, governing organizations, officials, participating countries, memorable
events, and many of the most decorated athletes of the Olympic Games.
Each entry provides background information on the subject and details
the related Olympic significance.

The appendixes and reference section are also full of pertinent information.
They provide tables and lists, including such items as former IOC members,
presidents, and Olympic Order and Cup recipients. The reference section
lists several scholarly works covering the Ancient and Modern Olympic
Games.

The authors were founding members of the International Society of Olympic
Historians (ISOH). Bill Mallon and Ian Buchanan each served terms as president
of the ISOH and both have been recognized for their contributions to the
Olympic movement by being awarded with the Olympic Order in Silver. The
Olympic Order is the supreme individual honor accorded by the International
Olympic Committee.

The Historical Dictionary of the Olympic Movement is a useful,
quick reference guide to the Modern Olympic Games. The dictionary provides
historical information that can meet the educational needs of a student
while also providing the historian or sport enthusiast with a relatively
easy read. The chronologies, dictionary, and appendixes are all formatted
to make accessing information quick and trouble-free.

Historical Dictionary of the Olympic Movement (3rd Edition)
By: Bill Mallon and Ian Buchanan
Scarecrow Press, Inc.
ISBN: 0-8108-5574-7

Book Review: Learning Culture through Sports: Exploring the Role of Sports in Society

March 14th, 2008|Contemporary Sports Issues, Sports Studies and Sports Psychology|

In a day and age when we are inundated with clichés and superficial
analyses about sports, Learning Culture through Sports: Exploring
the Role of Sports in Society
is a refreshing, thought-provoking
departure from the sociobabble presented by mainstream sports media. The
authors critically evaluate the role of sport in society. They present
a critical view of how several socio-cultural issues are represented in
and through sports.

The text is strategically divided into six sections. Each section offers
a broad framework from which socio-cultural topics and their relation
to sports are addressed. While the book pursues traditional topics of
analysis within sport sociology (e.g., sport and race, sport and gender),
the focus of discussion within these frequently discussed sport sociology
themes is centered on hardly explored yet current and pertinent issues
within sport. For instance, author Kyle Kusz critically reflects on the
relation between the media images of white men in and out of sport by
examining two popular motion picture movies, Jackass and 8
Mile
.

This is an uncomplicated read. It is suitable for the general public as well as sport studies scholars. It could serve as supplementary reading for a course in sport sociology. It offers multiple perspectives presented by over thirty contributors on various issues related to sports and society. All authors are accomplished sport academicians and/or practitioners. Their critical insight into the issues facing sport and society deepens our understanding of various socio-cultural issues represented in and through sports.

Learning Culture through Sports: Exploring the Role of Sports in Society
Edited by Sandra Prettyman and Brian Lampman.
Published in 2006 by Rowman & Littlefield Education, Lanham, MD 20706
(207 pages, ISBN 1-57886-380-5).

Does the Media Impact Athletic Performance?

March 14th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|

“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

Introduction

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).

Mood

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 (Sportscolumn.com, 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).

Conclusion

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.

The Idea of Peace as Coubertin’s Vision for the Modern Olympic Movement: Development and Pedagogic Consequences

March 7th, 2008|Contemporary Sports Issues, Sports Studies and Sports Psychology|

The Origin of the Idea of Peace in the Modern
Olympic Movement

The Olympic Games took place in ancient Greece 293 times from 776 B.C.
up to 393 A.D., i.e. over a period of almost 12 centuries, in contrast
to modern times without interruption.

(more…)

Do Reliable Predictors Exist for the Outcomes of NASCAR Races?

March 4th, 2008|Contemporary Sports Issues, Sports Studies and Sports Psychology|

Introduction

This research attempts to ascertain whether factors known prior to a NASCAR race can help to predict the order of finish of that race. We provide evidence in the form of correlation analysis of the order of finish with available quantitative and categorical information collected, and a simple test for the effect of teams (regressions for each races are also available from the authors). Data were collected on 14 races from the 2003 NASCAR Winston Cup (now Nextel Cup Series) schedule.

Many factors influence the outcomes of NASCAR races. The speed and handling of the car, the skill of the driver, and the performance of the pit crew are but a few of the variables that are important determinants of the finish for a particular car and driver. Many variables outside the control of a particular team, such as the behavior of other drivers, weather, cautions, and the like also influence the final order of finish in NASCAR races. A priori, then, it would be anticipated that predicting outcomes in any meaningful way would be problematical.

The goal of this project is to determine whether those objective, measurable, variables known prior to the start of a race are useful in determining the order of the outcome. To this end we have assembled full data sets for 14 different races from the 2003 NASCAR Winston Cup series. The data include the following for each race: the order of finish, pole position, qualifying speed, practice time, the number of team members of a given driver in the race, the finish position in the prior race at that particular venue, the finish position in the immediately preceding race, driver points for the previous year in Winston Cup competition, and laps completed for the previous year in Winston Cup competition. We also have dummy variables to indicate whether it is the rookie year for a driver and whether the driver changed teams for the current year.

A Simple Model

As a first approach to the problem of predicting the order of finish in particular NASCAR races, we offer a simple theoretical model. Order of finish is posited to be functionally related to variable sets reflecting car speed, driver characteristics, team characteristics, performance in related races, and other factors. In functional notation,

F = f(S, D, T, RR, O), where:

F = Order of finish for a particular race

S = Car speed

D = Driver characteristics

T = Team characteristics

RR = Performance in related races

O = Other factors

To be sure, the variable categories listed are not distinct from each other. That is, empirical measures of car speed are certainly related to other categories of variables such as driver and team characteristics. The theoretical model serves to provide a framework for the empirical specification of the model.

Car Speed, Driver Characteristics, and Related Races

The effects of car speed on race outcomes are obvious. Faster cars will, on average, finish better. Also obvious are the effects of driver racing skill and experience. If it is possible to proxy for driver racing skill and experience, such proxies should be related to finish position across races.

Car/driver combinations may also be subject to streakiness in consecutive races and they may also be more successful at particular venues. The empirical variables defined in the following section proxy for these effects.

Team Characteristics

Team characteristics, in particular team size, require additional explanation. It is an empirical fact that multi-car teams have, in recent years, dominated the NASCAR Winston Cup series, and it is commonly believed that multi-car teams have advantages over smaller teams. What particular advantages are possible for multi-car teams?

First, the marginal cost of increasing the speed of a car is likely to be very sharply upward sloping (Allmen, 2001). This is due in part to NASCAR rules regarding car shape, size, aerodynamics, weight, and engine characteristics. While these rules are in place to equalize competition, the existence of this degree of uniformity makes it very difficult and expensive to gain an advantage within the rules. As Bill Elliott, a driver and past owner observes, “It may cost you $5 million to get to the track, but it may cost you an additional $3 million for a few tenths better lap time ….” (Middleton, 2000, p. 37).

A team with more car/driver combinations can apply any found advantage to each of its cars. Such advantages then result in better performances for all cars on the team, and hence greater performance revenues. Consider Figure 1 in which marginal cost (MC) increases sharply as car speed increases and such costs are assumed to be the same for multi-car teams as for single car teams. Since newly discovered speed advantages can be applied to all cars on a multi-car team, those teams can generate greater revenues for the team (MR M = marginal revenue for multi-car teams) than any such advantage generates for a single car team (MR S = marginal revenue for single car teams). Following optimization principles then, multi-car teams would find it worthwhile to achieve a speed of S M, whereas single car teams have incentive to achieve a speed of only S S. If this analysis is correct, multi-car teams would be expected to achieve greater speed in general than single car teams.

Second, it is an empirical fact that larger teams attract greater sponsorship resources, in part because they are more successful. Then, if the sharply increasing marginal costs mean that multi-car teams are more likely to engage in expensive research for given performance benefits and sponsorship revenues depend on performance, the dominance of multi-car teams can be explained (at least in part) by this simple economic analysis.

Figure 1

Figure 1: Multi-car versus single car teams

Third, teams with more sponsorship income are able to offer greater compensation to crewmembers, hire more experienced and specialized team members, such as aerodynamicists, and can more easily afford expensive technology and testing.

Fourth, substantial barriers to success for smaller teams (especially single car teams) may also exist because of scale economies. The advanced technology machinery for making racing parts would be an example of the “lumpy inputs” explanation of scale economies thought to be the most common reason for decreasing long run average cost. Larger teams would then have an advantage since the production of such parts for the team would necessarily be larger in scale.

Other advantages also accrue to multi-car teams. Operationally, multi-car teams also have more test dates available to them at Winston Cup tracks. Hence, more data can be collected and shared among team members when it comes to setting up the cars for races at those tracks. Multi-car teams also have built-in drafting partners, although the NASCAR literature suggests that at the end of the race each driver is “on his own,” (Cotter, 1999; Dolack, 2003; Hinton, 1997; Pearce, 1996, 2003).

Empirical Specification: Races and Data

The data for this project were collected from a variety of Web sites, http://www.nascar.com (Past Race Archive, 2002, 2003), http://jayski.thatsracin.com/ index.html (Statistics Pages from Jayski. 2002), and http://www.foxsports.com/named/ FS/Auto (Nextel Cup Standings, 2002, 2003). The variable we wish to predict is the order of finish, which is of course, available for each race on the Winston Cup circuit.

Individual Races

The 14 races for which we collected data include short tracks, speedways, super speedways, and a road course. To determine if the same factors are related to order of finish of different races at the same track, we also included both races run at Daytona and both races run at Michigan in 2003. The specific races for which we collected data are: the Brickyard 400 at Indianapolis Motor Speedway, the Food City 500 at Bristol Motor Speedway, the Coca-Cola 600 at Lowe’s Motor Speedway, the Carolina Dodge Dealers 400 at Darlington Raceway, the Daytona 500 at Daytona International Speedway, the Pepsi 400 at Daytona International Speedway, the Virginia 500 at Martinsville Speedway, the Sirius 400 at Michigan International Speedway, the GFS Marketplace 400 at Michigan International Speedway, the Chevy Rock & Roll 400 at Richmond International Speedway, the Aaron’s 499 at Talladega Superspeedway, the Samsung/Radio Shack 500 at Texas Motor Speedway, the Tropicana 400 at Chicagoland Speedway, and the Sirius at the Glen at Watkins Glen International.

The potential explanatory variables for order of finish collected for each race were as follows:

ptime = the practice time closest to race time.

qspeed = the speed at which the car/driver qualified.

pole = position of the car at the start of the race.

points = points scored in the Winston Cup Series for the prior year.

laps = number of laps completed for all Winston Cup races in the prior year.

DNF = did not finish, the number of races in which the driver failed to finish, prior year.

rookie = a dummy variable equal to 1 if the driver was a rookie in 2003, and equal to

0 otherwise.

# drivers = the number of cars/drivers a multi-car owner fields (for 2003, values = 1,2,3,4).

newteam = a dummy variable equal to 1 if the driver was a member of a new team in 2003, and equal to 0 otherwise.

prev = the finish position of the driver in the previous week’s race.

lastyr = the finish position of the driver in the 2002 running of the same race.

Car Speed

The first three variables from the above list, practice time, qualifying speed, and pole position correspond to the car speed category from the model outlined in the previous section. Clearly qualifying speed and pole position are very closely related (since pole position is determined primarily by qualifying speed), however race officials, for reasons such as a rule and/or equipment violation, missing the driver’s meeting, switching to a backup car, an engine change, or a driver change, may alter pole position. For this reason we collected data for both qualifying speed and pole position in case one or the other is a better predictor of race outcomes.

Driver Characteristics

The next four variables, points, laps, DNFs, and rookie, are driver characteristics with the first three representing performance in the prior year, and the variable rookie is a proxy for lack of experience on the Winston Cup circuit. Theoretically, rookies will not have the skill level that existing Winston Cup drivers have developed over the years, nor will they have had exposure to certain tracks that more experienced Winston Cup drivers have competed on in the past.

Team Characteristics

The variables # drivers and newteam correspond to the team characteristics category in the model. The # drivers variable measures the effect of a given owner having multiple cars/drivers or a multi-car team. With respect to the new team variable (newteam), drivers joining a new team will require time to adjust to the way the crew operates, in addition to developing an effective communication style with the crew chief.

Related Race Effects

Related race effects are measured by the variables prev and lastyr. The variable prev is an attempt to proxy for possible streakiness from race to race. That is, are good finishes followed by other good finishes and poor performances followed by poor performance in the following race? The variable lastyr attempts to measure whether a certain racetrack is a better venue for certain car/driver combinations. For example, the dominance of Dale Earnhardt, Incorporated (DEI) at the superspeedways illustrates the expertise a team may develop at specific racing venues (McCarter, 2002). Since 2001, DEI has won 9 out of the 12 races at Daytona International Speedway and Talladega Superspeedway.

Methodology and Expectations

As a first attempt to determine those variables that relate to order of finish, correlation coefficients are computed between order of finish and each of the measured explanatory variables. The following signs are anticipated for the correlation coefficients:

Expected Sign

of coefficient Explanation

equation1 Faster (lower) practice time leads to better finish

equation2 Higher qualify in speed (MPH) leads to better finish

equation3 Better pole position leads to a better finish

equation4 More points from previous year leads to a better finish

equation5 More laps completed from previous year leads to better finish

equation6 More failures to finish leads to poorer finish

equation7 Rookies may be less likely to have better finishes

equation8 Multi-car teams may have better finishes

equation9 Driver on new teams less likely to have better finishes

equation10 Previous race finish positively related to current race finish

equation11 Previous finish at this track positively related to current finish

 

Note: ρ represents the population correlation coefficient, and f represents finish position, 1 = winner , 2 = second place, etc..

Results

Correlation Analysis

Table 1 in the appendix is the result of the correlation computations. The coefficients in bold are statistically significant at the α = .05 level and consistent with the predicted signs presented in the previous section.

Several results of this exercise are interesting and potentially important for predicting the outcome of NASCAR races. First, considering the columns (how the variables fared across different races), on average the signs of the variables are in accord with expectations (though some are on the whole insignificant). Several of the variables seem to be consistently correlated (linearly) with order of finish across races. For example the number of drivers variable (# drivers) is statistically significant for all races except Darlington and Watkins Glen (even then the coefficients have the predicted sign). Of course that teams with more members tend to be more successful is not a new conclusion—these results support statistically, at the individual race level, the hypothesis that multi-car teams are generally more successful (see the section on team characteristics above). Of the two tracks that did not have statistically significant results with respect to the # drivers variable, the Watkins Glen result might be due to the fact that it is a road race. Watkins Glen is one of only two road courses utilized by Winston Cup, and teams often use substitute drivers with more road racing skills than their full-time driver may possess in these races.

Indicators of drivers’ past successes also are correlated with order of finish. The variable points is statistically significant for 11 of the 14 races and all of these sample correlations have the anticipated sign. Interestingly enough, the three races that did not demonstrate significant results were run at Daytona and Talladega, the two restrictor plate tracks. Similarly laps, which might be interpreted as a measure of driver/car consistency and driver experience, is statistically significant in 7 of the 14 races. The DNF variable seems to explain little in the way of simple correlation with order of finish. In considering this variable, recognize that a driver with more DNFs may have simply competed in more races than another driver. Thus simple correlation, which does not control for levels of other variables, may not be appropriate to measure such effects.

Measures that account for car/driver speed include pole position (pole), qualifying speed (qspeed) and practice time (ptime). We recognize that pole position and qualifying speed generally measure the same effect. Both are included here to see if one or the other is more closely correlated with order of finish. Based on the sample correlations in Table 1, qspeed is significantly related to order of finish in half of the races and pole in 6 of the 14 races. Practice time (ptime) seems to fare somewhat better—it is significantly related to order of finish in 9 of the 14 races. There may be several reasons for this outcome. The practice times used for statistical analysis were collected from the practice session conducted closest to race time, if all drivers participated in that session. If all drivers did not participate in the last practice session, then practice time statistics were taken from the session run closest to race time in which all drivers practiced. This was done to ensure that the cars would be “set up” in practice as close to race set-up as possible. Since the cars are set-up for race conditions when they practice, it would be expected that the ptime would more closely relate to order of finish than qspeed because the set-up for qualifying is based on two laps at the fastest speed possible. Race day set-up is designed to accommodate consistency and longer runs on the track.

The variable that measures the finish position in the driver’s last race (prev) is statistically related to order of finish in 8 of the 14 races and has the expected sign for all races. This would suggest that driver/car combinations are subject to streakiness, that is, good finishes tend to be followed by other good finishes and vice versa. For only four races is the variable lastyr, the finish position of the driver in the prior running of the race by the same name correlated with the current finish position.

Of the two categorical variables, newteam (equals 1 if the driver joined a new race team for the 2003 season, 0 otherwise) is related to order of finish in 10 of the 14 races and in all races has the anticipated sign. Changing teams, on average, would seem to be related to poorer finishes. On the other hand, rookie status (rookie) was related to finish order only for the first Daytona race and Martinsville.

Again considering the columns in Table 1, the average of the correlation coefficients for each of the explanatory variables across the 14 races is included in the table as the bottom row. A coefficient above 0.25 is generally statistically significant for individual races (again α = .05, one-tailed test, n = 43) On that basis, eight of the variables (laps, points, newteam, pole, # drivers, prev, ptime, and qspeed) are on average statistically (linearly) related to order of finish.

It is also useful to consider the correlations for individual races, i.e., to consider Table 1 by row. For example, at Martinsville order of finish was linearly related to 9 of the 11 variables in the explanatory variable set. The first (June) Michigan race, Richmond, and Chicagoland were linearly related to eight of the explanatory variables. At the other end of the scale, for the two Daytona races, only two of the explanatory variables were correlated with order of finish. One of those variables was the same (# drivers) for both Daytona races. Interestingly, comparing the second (August) Michigan race to the first, only five variables were statistically significant for the second race, but each of those variables was also significant for the first Michigan race. However, relatively strong correlations for pole, practice time and qualifying speed for the first Michigan race were not repeated for the second Michigan race. The reader may examine Table 1 to see that the rest of the races have from three to seven explanatory variables that are statistically significant.

Additional Evidence on Team Effect

The effect of team membership (#drivers) seems to play an increasingly important role in NASCAR (Cotter, 1999; Dolack, 2003; Hinton, 1997; Pearce, 1996, 2003). In 2003, 12 organizations owned and fielded 33 of the 43 cars competing at the majority of NASCAR races. Additionally the Winston Cup Championship has been won by a multi-car team in each of the last 10 years (Pearce, 2003). Therefore, we considered another test of team membership on car/driver success. Using statistics from the entire 2002 and 2003 racing years, a table of results divided into top 10 finishes and finishes out of the top 10 and classified by number of team members was constructed. Table 2 in the appendix shows that teams with four members (the highest number of team members at the start of 2002) had 285 starts and of those, 43.16% resulted in top 10 finishes. The corresponding percentages are 15.55% for three member teams, 29.14% for two member teams, and only 8.68% for drivers without team members. The largest (four member) teams tended to dominate the top 10 finishes. Perhaps surprising is the fact that two member teams had by far the largest number of starts and the second highest rate of top 10 finishes with 29.14%. For three member teams the corresponding percentage was 15.55% and single drivers finished in the top 10 only 8.68% of the time. A simple chi-squared test of independence of the classification of top 10s by number of drivers on a team, yields a χ 2 = 123.9, which allows rejection of the null of independence at α < 0.001. This result confirms the obvious result that the proportion of top 10 finishes does depend on the number of team members.

Table 3 contains the same categories for the 2003 Winston Cup drivers. There was one team with five drivers for the 2003 season, so the table contains an additional column. For the 2003 season, the percentage of top 10 finishes is remarkably constant for the teams with five, four and two members, with 37%, 38% and 32% respectively. Again, teams with three members and especially the single drivers fared less well on the basis of top 10 finishes. Again, the null hypothesis of independence between number of team members and top 10 finishes can be rejected (χ 2 = 106.0), providing statistical confirmation of the already clear evidence that the proportion of top 10 finishes differs by number of team members.

Conclusions

The correlation analysis across 14 races for the 2003 NASCAR Winston Cup series identifies a number of variables that are associated with the order of finish of these races. On average, variables measuring car speed, including practice time, qualifying speed, and pole position are related to the order of finish of races. We also find that prior success on the part of the driver, measured by laps completed in the prior year and points accumulated are also correlated with order of finish. Whether or not the driver was a rookie was, perhaps surprisingly, not on average correlated with finish order across races. There is also some evidence that performances of driver/car combinations are subject to streaks. That is, finish positions in a given race are often correlated with finish positions in the race that follows. Of course these results could simply reflect the fact that some driver/car combinations consistently finish better than others may. Changing teams is correlated with poorer finishes and team size is correlated with better finishes.

The effect of team membership is reinforced by the data in Tables 2 and 3, which classifies top 10 finishes by number of team members. Teams with more members are more successful in terms of top 10 finishes. However, this effect is not monotonic in nature, since two member teams have a larger percentage of top 10s than do teams with three members.

Further research is indicated to test the robustness of these results. Such analysis could include races not in our data set and results from different years of NASCAR racing.


References

 

Allmen, P. von. (2001). Is the reward system in NASCAR efficient? Journal of Sports Performance, 2(1), 62-79.

Cotter, T. (1999). Say goodbye to the single-car team. Road & Track, 50(8), 142-143.

Dolack, C. (2003). One is the loneliest number. Auto Racing Digest, 31(6), 66.

Hinton, E. (1997). Strength in numbers. Sport Illustrated, 87(16), 86-87.

McCarter, M. (2002). Stepping up to the plate. The Sporting News, 226(27), 38-39.

Middleton, A. (2000, February). Racing’s biggest obstacle. Stock Car Racing, 34-37.

Past Race Archive, 2002 [Data files]. Available from NASCAR Web site, http://www.nascar.com

Past Race Archive, 2003 [Data files]. Available from NASCAR Web site, http://www.nascar.com

Nextel Cup Standings, 2002 [Data files]. Available from FOXSports Web site, http://www.foxsports.com/named/FS/Auto

Nextel Cup Standings, 2003 [Data files]. Available from FOXSports Web site, http://www.foxsports.com/named/FS/Auto

Parsons, K. (2002, August 26). Tunnel vision – NASCAR teams’ fortunes are blowing in the wind. The Commercial Appeal, Memphis, TN, p. D9.

Pearce, A. (1996). Fair and square. AutoWeek, 46(50), 40-41.

Pearce, A. (2003). Going it alone. AutoWeek, 53(14), 57-58.

Statistics Pages from Jayski. (2002) [Data files]. Available from Jayski Web site, http://jayski.thatsracin.com/index.html

 

Appendix

Table 1: Correlation coefficients between finish position and the explanatory variables

Explanatory Variables

Race Laps DNF Points newteam Pole #drivers prev lastyr ptime qspeed Rookie?
Indianapolis -0.214 0.091 -0.319 0.342 -0.106 -0.326 0.118 0.435 0.374 0.047 0.088
Bristol -0.209 0.316 -0.350 0.030 0.177 -0.417 0.117 0.407 0.392 -0.190 0.088
Lowe’s -0.209 0.030 -0.291 0.360 0.198 -0.314 0.335 0.085 0.403 -0.283 0.200
Darlington -0.373 0.154 -0.428 0.180 0.176 -0.153 0.170 -0.141 -0.239 -0.234 0.265
Daytona (Feb) -0.044 -0.111 -0.173 0.155 0.243 -0.300 0.274 0.045 -0.106 NA 0.094
Daytona (July) -0.140 0.110 -0.218 0.132 0.006 -0.258 0.167 0.300 0.164 -0.060 0.105
Martinsville -0.462 0.079 -0.540 0.030 0.433 -0.342 0.314 0.352 0.462 -0.496 0.263
Michigan (June) -0.383 0.115 -0.435 0.396 0.596 -0.550 0.478 0.125 0.549 -0.547 0.041
Michigan (Aug) -0.342 -0.297 -0.408 0.380 0.172 -0.356 0.392 0.087 0.237 -0.205 0.212
Richmond -0.367 -0.070 -0.508 0.384 0.406 -0.283 0.300 0.126 0.350 -0.403 0.228
Talladega -0.228 0.088 -0.230 0.306 0.398 -0.335 0.098 0.233 0.080 -0.361 0.170
Texas -0.200 0.085 -0.352 0.282 0.169 -0.393 0.195 -0.178 0.300 -0.159 0.146
Chicagoland -0.339 0.064 -0.413 0.260 0.422 -0.261 0.317 0.246 0.428 -0.464 0.178
Watkins Glen -0.410 -0.298 -0.472 0.406 0.297 -0.244 0.254 0.123 0.285 -0.278 0.239
Average -0.280 0.025 -0.367 0.260 0.256 -0.324 0.252 0.160 0.263 -0.279 0.166

 

Table 2: Top Ten Finishes by Number of Team Members, 2002 Season

4 member teams 3 member teams 2 member teams One member teams
Top 10 % 43.16% 15.55% 29.14% 8.68%
Total starts 285 328 525 357

Table 3: Top Ten Finishes by Number of Team Members, 2003 Season

5 member teams 4 member teams 3 member teams 2 member teams One member teams
Top 10 % 36.87% 38.19% 22.53% 31.67% 7.66%
Total starts 179 144 395 360 418

The flat MR curves are offered as an approximation. Additional speed should add increasing marginal revenue (as cars move up in finish order, added revenue increases), but since all cars are attempting to increase speed, the possible increases in revenue will be distributed among the competitors.

For example, testing for the aerodynamic properties of a car in a wind tunnel can cost more than $2000 per hour (Parsons, 2002).

T he field is generally set using a combination of timed laps and provisionals. The fastest 36 cars earn a place based on time, while positions 37-43 are determined by a process which may include last season’s final owners standings, current owners standings and former champions. The provisionals are assigned in descending order, beginning with the highest ranking owner in the standings. The lone exception is the Daytona 500, which uses two qualifying races to determine the field. (Nascar.com)

In other words, a driver with many laps completed and many DNFs would be expected to fare less well than another driver with many laps completed, but few DNFs.

While the sample size is generally 43 for individual races it is somewhat lower for some individual races, e.g., a race in which a driver/car combination did not run in the race at a particular venue it its previous iteration.

If all four members of a team start in the same race, that would equal 4 starts and if two of those four finish in the top 10, that would be 50% in the top 10.

This procedure can also be described as a test of proportions, that is, we have evidence that the proportions of top 10 finishes differs by number of team members.