2005 – Page 7 – The Sport Journal

A Composite Softball Bat Revolution: Why the Pitcher has Little Time to React to a Batted-Ball

Submitted by: Mark McDowell, Ph. D., Michael V. Ciocco, Ph.D. & Bryan Morreale

Abstract

In the past few years, there has been a composite bat construction revolution in the softball bat industry. While composite material bats have enabled softball bat performance to increase much to the delight of hitters, they can pose a significant safety risk to defensive players and especially the pitcher. A controlled experiment using two composite softball and two wooden softball bat models has been conducted in order to present experimental data on the field performance of various bat/ball combinations. Three types of softballs were used in this study; low-compression, cork-centered softballs, mid-compression polyurethane softballs and high-compression polyurethane softballs ranging from 1134 to 2389 N/0.64 cm (255 to 537 lbs/0.25 in) compression. Three experienced softball players were chosen as test subjects for this study. Initial batted-ball velocities ranged from 126.2 to 164.3 km/h (78.4 to 102.1 mph), corresponding to an available pitcher reaction time ranging from 0.456 to 0.350 seconds. The goal of this study is to present the potential safety and injury risks associated with using composite softball bats in the sport of softball.

Introduction

Over the past few years, the sport of softball has undergone a revolution in bat construction. When the sport was originated, wood was the major construction material of softball bats. In the 1980s, softball bats constructed from aluminum-based materials were introduced followed by titanium-based bats in the mid-1990s. However, the excessive batted-ball velocities (BBV) resulting from the use of titanium-based bats led to them being declared illegal and unsafe by all of the major governing bodies of softball due to their increased injury potential. From the late 1990s to the present, bats constructed of composite materials are the most commonly used bats and have been responsible for countless injures and even fatalities in the sport (Dawson, R., 2003). Composite softball bats are currently being used at all levels of softball from fast pitch softball in the Olympics, collegiate and high school levels to the recreational weekday slow-pitch softball league. Although there are over 19 million softball players in the U.S. (Ramsey J. and Smith G., 2003), there is no national database on injuries and fatalities in the sport and this complicates the issue of establishing a national safety standard for the sport of softball.

The authors have conducted a batted-ball field test study using three different compression ranges of softballs in order to show the potential risks involved when using wood and composite softball bats by measuring the BBV for each of the bat/ball combinations and using these speeds to calculate the time a pitcher has to react to a batted-ball, which the authors call available pitcher reaction time (APRT).

Method

A field study to analyze composite and wood softball bat performance using three different softball compression values was conducted. Three experienced softball players with an average height of 1.85 m (6 ft, 1 in) and weight of 99.8 kg (220 lbs) were chosen as subjects for this study and were required to record over twenty-five BBV measurements with each bat-ball combination over five test rounds. In each round, at least five recorded hits out of a maximum of seven from each tester was recorded in order to minimize fatigue. In addition, there was a mandatory minimum five-minute rest period between each round. In order to be considered a valid test, the standard deviation of the top five (20%) BBV readings from each bat-ball combination had to be within 3.9 km/h (2.4 mph) based on a statistical margin of error at the 95% confidence level.

Softballs

All softballs were compression-tested according to the ASTM standard for ball testing (ASTM F 1888-02, 2002). Ball compression is defined as the amount of force necessary to compress a softball 0.64 cm (0.25 in) and is measured in N/0.64 cm (lbs/0.25 in). Three different softball models with compression values ranging from 1134 to 2389 N/0.64 cm (255 to 537lbs/0.25 in) were chosen for this study (sixty balls per model). All softballs were purchased from retail sporting goods stores and are listed in Table 1.

Softball Bats

The composite bats chosen for the study were an 850 g (30 oz) EastonÒ Synergy+ and a 794 g (28 oz) MikenÒ Freak model. The wooden bats chosen were an 850 g (30 oz) TPSÒ and an 850 g (30 oz) BWPÒ Maple model. These bats were selected due to their popularity amongst recreational softball players and were purchased from retail sporting goods stores.

Batted-ball velocity measurements

A Jugs® professional radar gun having an accuracy to within 0.8 km/h (0.5 mph) was used to measure BBV. Before each round, the Jugs® radar gun was calibrated using a certified 98.2 km/h (61 mph) K-band tuning fork. Readings were taken approximately halfway between the batter’s impact point and the pitcher’s mound.

Pitching

A Jugs® Professional softball pitching machine capable of accurately and reliably pitching a softball in the 26 to 40 km/h (16 to 25 mph) range through the hitting zone was used. This range was used in order to simulate the actual pitch speeds that occur in a slow-pitch softball game.

Available Pitcher Reaction Time: Using the fact the pitcher’s mound in slow-pitch softball is 15.24 m (50 ft) away from home-plate; the available pitcher reaction time (APRT) for a particular bat-ball combination can be calculated once the BBV is recorded. The APRT is a safety metric that can be used to determine if a bat-ball combination is considered safe or unsafe. As a general rule, the higher the BBV’s, the lower the APRT’s, and thus, the higher the risk of injury.

Results

The initial BBV and APRT data is listed in Tables 2 through Table 6. The initial BBV using cork-centered, mid- and high-compression softballs using the wooden softball bats ranged from 126.0 to 136.8 km/h (78.3 to 85.0 mph), which translates into an available pitcher reaction time ranging from 0.456 to 0.421 seconds. The initial BBV using cork-centered, mid- and high-compression softballs using the composite softball bats ranged from 143.5 to 164.3 km/h (89.2 to 102.1 mph), which translates into an available pitcher reaction time ranging from 0.401 to 0.350 seconds. It should be noted that at the time of this study, the two largest softball associations, the United States Specialty Sports Association (USSSA) and the Amateur Softball Association (ASA) of America mandated that any bat achieving an initial BBV of 137.2 km/h (85.2 mph), which translates into an APRT of 0.420 seconds (USSSA, 2003), (ASA, 2003) is considered unsafe. Based on our data, the composite bats would be considered unsafe for all balls tested while the wooden softball bats would be considered safe for all balls tested.

Discussion and Implications

The main finding of this study is that unsafe batted-ball velocities were achieved using composite softballs bats. The initial BBV of the composite bats were as much as 27.2 km/h (16.9 mph) higher than the softball association’s goal of 137.2 km/h (85.2 mph), which translates into an available pitcher reaction time of 0.420 seconds. However, very little research has been done with respect to quantifying how much time a pitcher, or any player for that matter, needs to safely defend themselves from a batted ball. To date, there have been three published studies in the sport of baseball using ball exit speeds and APRT.

A recent study [Nicholls, et al., 2003] investigated player safety concerning ball exit speed for the sport of baseball and concluded that the maximum safe initial batted-ball velocity that a pitcher can react to is approximately 148 km/h (92 mph), which translates into an APRT of 0.425 seconds. They also concluded that a “certified” metal bat swung by an experienced hitter may produce ball exit velocities exceeding that demonstrated by a robotic hitting machine, which is currently used for establishing safe batted-ball velocities. A second study [Owings, et al., 2003] was conducted investigating the available pitcher reaction time as a consideration in design constraints for baseballs and baseball bats for various age groups. They found that for the 16-year age group, a minimum reaction time of 0.409sec. is necessary to reduce the potential for serious or catastrophic injury. A third study conducted by the NCAA tested a variety of bats on a specially designed batting machine located at the University of Massachusetts in Lowell, [Mississippi State University, 2002]. The recommendation from this study was to drop the exit speed to no more than 149.7 km/h (93 mph) in collegiate baseball, which translates into an APRT of 0.420 seconds.

Two recent studies [Hultsch, et al., 2002] and [Luchies, et al., 2002] address the differences in reaction time between younger and older adults and conclude that reaction time is considerably reduced or varied as a person gets older. Using this research on reaction time and the results of Nicholls and Owings, it can be theorized that when composite bats are in use and a ball is hit directly back at the pitcher or infielder, this can pose a significant risk for injury to older adults which comprise the majority of the millions of recreational softball players.

The results from the composite bat data show that none of the average pitcher response times are slower than the published results for the sport of baseball. In fact the worst case, composite/high-compression ball/BT3, has an APRT of 0.357 sec., which is over 18% lower than the suggested value of 0.425 sec.

For the wooden bat, for all the balls, the APRTs are very close to or better than those suggested by published baseball studies. The lowest APRT is 0.421 sec. and the highest is 0.457 sec. When compared to the wooden softball bats, the composite bats outperform them by as much as 30% which should at least lead to a meaningful discussion on the safety of the sport.

The implications of this study are that the use of composite softball bats is creating an unnecessary safety risk for players and that many players, coaches, league directors and parents are probably not aware of the risk associated with the use of composite softball bats.

Conclusions

A controlled field-test study analyzing the performance of composite and wooden softball bats along with using low-, mid- and high-compression softballs was conducted in order to analyze the various bat/ball combinations. Batted-ball velocity was measured and available pitcher response time calculated and compared to recommended safety limits imposed on the sport as well as published baseball safety studies. The results from this study concluded that using composite softball bats, batted-ball velocities exceeded the recommended safety limits by as much as 27.2 km/h (16.9 mph) or 0.070 seconds. It can be concluded that regardless of the ball used, composite bats may pose an increased safety risk to defensive players, especially the pitcher, in the path of a batted-ball hit.

References

Amateur Softball Association of America (2003). ASA Bat Testing Procedure (ASA 2000). URL: http://www.softball.org
ASTM F 1888-02 (2002). Standard Test Method for Compression-Displacement of baseballs and Softballs. ASTM International.
Dawson, R. (2003). Blinding Speed. WISH TV-8, Indianapolis.
URL:http://www.wishtv.com/Global/story.asp?s=%20%201274912.
Hultsch, D. F., S. W. MacDonald and R. A. Dixon. 2002. Variability in reaction time performance of younger and older adults. The Journals of Gerontology, Series B 57(2): 101.
Luchies, C. W., J. Schiffman, L. G. Richards, M. R. Thompson, D. Bazuin, and A. J. DeYoung. 2002. Effects of age, step direction, and reaction condition on the ability to step quickly. The Journals of Gerontology, Series A 57(4): M246.
Mississippi State University (2002). Study helps give pitchers more time to react. University Relations News Bureau. URL: http://msuinfo.ur.msstate.edu/msu_memo/1999/02-08-99/bats.htm
Nicholls, R.L., Elliot, B.C., Miller, K. and Koh, M. (2003). Bat Kinematics in Baseball: Implications for Ball Exit Velocity and Player Safety. Journal of Applied Biomechanics, 19, 283-294.
Owings, T. M., Lancianese, S. L., Lampe, E. M., and Grabiner, M. D. Influence of Ball Velocity, Attention, and Age on Response Time for a Simulated Catch. Med. Sci. Sports Exerc., 2003, Vol. 35, No. 8, pp. 1397-1405.
Ramsey J. and Smith G. (2003). Serious Slow-pitch Softball. Florida: Softball Magazine
United States Specialty Sports Association (2003). Bat Performance Factor Test. URL: http://www.usssa.com

U.S. Sports Academy2015-03-20T10:57:27-05:00January 4th, 2005|Contemporary Sports Issues, Sports Management|Comments Off

Mass Media and Gender Identity in High Performance Canadian Figure Skating

Submitted by: Karen McGarry

Introduction

This paper is based upon qualitative, anthropological fieldwork conducted between 2000 and 2002 among Canadian journalists, high performance (ie. National, World, and Olympic level) figure skaters, coaches, sponsors, and others involved in the production of mediated representations of figure skating for mainstream Canadian television networks and other print media. With the permission of various organizations, I conducted participant observation and fieldwork in skating arenas and in the media centers of major competitions to understand the role of figure skating in shaping a sense of Canadian national identity. As standard practice in anthropology, the names of those interviewed are withheld to protect their anonymity. The goal of this paper is to highlight the role of the media in promoting particular gendered images of figure skaters for public consumption, thereby drawing attention to two issues: 1) the socially constructed nature of various representations of men and women in the sport, and; 2) the ways in which particular gendered images of sports figures are promoted in the interests of nationalism.

Figure Skating the Media, and Canadian Culture

Figure skating is viewed by many Canadians as an integral part of Canadian culture. Records of ice skating date back to at least the 1700s, and figure skating, along with hockey, receives prime-time television coverage on major Canadian television networks. Figure skating, in fact, is ranked second only to hockey in terms of television spectatorship, government funding, and corporate sponsorship (Skate Canada 2002). Its popularity among fans is heightened by the fact that since the 1940s, Canadians have won more than 500 international medals, making it one of the nation’s most competitively successful amateur sports. Skaters such as Elvis Stojko, Kurt Browning, Elizabeth Manley, Barbara Ann Scott, Toller Cranston, and Jamie Sale and David Pelletier, to name a few, have become household names and national icons in Canada.

Given figure skating’s high spectatorship levels, it is not surprising that Skate Canada, the sport’s amateur governing body, and CTV, Canada’s self-declared “official figure skating network” have opportunistically marketed figure skating as a distinctly “Canadian” sport and a sport of national significance:

Figure Skating is a sport of national significance to Canadians and is part of our heritage. Canadians have excelled in figure skating, achieving international success and celebrity status (Skate Canada Fact Sheet 2002).

It’s part of our Canadian heritage. That’s why CTV – ‘Canada’s Figure Skating Network’ – is committed to bringing you the best figure skating in the world. CTV has been partnered with the sport since 1961, and makes figure skating its core sports property (CTV website, July 16, 2001).

For Skate Canada and its top skaters, sponsorships are relatively easy to obtain (in comparison with other amateur sports). In fact, Skate Canada has become a primarily self-sustaining organization over the years, thanks mainly to lucrative sponsorship opportunities. While the organization continues to receive government funding, this represents a small portion (7%) of its annual operating budget (Skate Canada 2002). This means that the task of promoting a sense of national identity among Canadians has, in the case of figure skating, moved increasingly into the hands of non-state actors, and particularly the mass media and its sponsors and advertisers. Throughout my research, I learned that there exist powerful alliances of interest between skating sponsors and various Canadian media outlets, who have a vested economic interest in promoting various national representations that appeal to entertainment spectacles. These alliances, in turn, indirectly influence the gendered representations upheld for Canadian audiences. For example, at one event, I talked with a television sports network executive from a major media outlet. Very excited to be there, she had this to say about her company’s agreement with Skate Canada:

When they [Skate Canada] approached us, we were excited to be a part of it all. We were looking for the best sports ambassadors for Canada to support. What’s better than figure skating? They’re good, clean-cut kids. We’re proud too of our association with skating and our advertisers have really supported Elvis Stojko over the years.

Karen: What was it about Elvis that makes him appealing?

Executive: He’s successful and a champion. And he’s such a strong, masculine presence. And he’s a great public figure for the sport and for the country. People just love him. I remember when a commentator at the X network said something bad about one of his skates a few years back and people were upset. So were we. And we made that quite clear. There’s no reason to be bad-mouthing such great skaters on live broadcasts.

In this example, we see how the media and their sponsors can influence various representations on ice, and the alliances of interest at stake. Top skaters recognize the importance of their sport to the country, and they rarely doubt that their image will not be promoted favorably. Their bodies have become commodified to the point where, as one skater confidently told me, “I’m not trying to be arrogant here, but they [the network] would have my people [his sponsors] down their throats if I got criticized. I’m worth a lot to them.” In many ways then, figure skating represents an opportunity for understanding how the Canadian media and television, in conjunction with advertisers, influence the production of gendered, commodified identities on ice, and in doing so, produce highly specific gendered bodily representations of the nation.

Gender and Figure Skating

The gendered categories of “masculinity” and “femininity” are socially constructed concepts and societal ideals about an appropriate masculine or feminine behavior vary spatially, from culture to culture, and temporally, depending upon various socio-historical circumstances (eg. Butler 1990). In other words, what is considered “socially appropriate” behavior for a female or male (today) is not the same as a century ago. Given the lucrative sponsorship potential of the sport, it is not surprising that the Canadian mainstream media and its sponsors and advertisers have sought to endorse “clean cut” mainstream, heterosexual images of masculinity and femininity for public consumption.

While figure skating is rumored to have the highest proportion of homosexual men of any amateur competitive sport (Pronger 1999), it is ironically a sport in which men must exhibit the most blatantly heterosexual signs to be successful and to receive commercial endorsements. Since the late 1980s, Skate Canada, the media, and its sponsors have made a concerted effort to de-emphasize figure skating’s balletic heritage, a tradition strengthened in the 1970s by skaters such as Canadian Olympian Toller Cranston, an openly homosexual competitor who introduced flamboyant costumes, thai cosmetics, ballet, and choreography into men’s skating. Male homosexuality, it seems, is considered a financial liability to the sport. In 1998, for example, the two-time Olympic silver medalist, Brian Orser, was “outed” in the media during a palimony lawsuit. Orser was devastated by the media exposure and claimed that such allegations of homosexuality would threaten his economic livelihood. Similarly, at the 2001 World Championships in Vancouver when a well-known male Canadian skater was contacted by a gay magazine about the possibility of doing a feature story on him, he was told by Skate Canada that he must decline the request. As one coach said to me, “that is not the sort of picture that Skate Canada wants to paint for the country, especially in an international forum.” Every effort is made to construct such skaters as heterosexual.

The heterosexual masculine images of World Champion skaters like Kurt Browning and Elvis Stojko were endorsed enthusiastically in the media throughout the 1990s, a point also noted by Adams (1997). Usually skating to rock and roll music, or adopting the traditionally masculine personas of characters like ninjas, Scottish warriors, or karate experts, Stojko was constructed in the media as “Canada’s Terminator.” At one competition I attended, I watched Stojko warm up backstage with balletic exercises, but the camera technician, who was instructed to film Stojko’s pre-competitive routine, waited until he decided to jog to film him. As he said to me, “Elvis is such a macho guy; we want to show that side of him.” By emphasizing male skaters’ athletic abilities in media portrayals, links are made between the supposed “strength” of the male figure skating body, and the “strength” of the nation. As one sponsor said to me, “we want to promote strong images of our male skaters to show the Americans and other nations that we are an important force to contend with.” The bottom line is that images of male heterosexuality sell to a broader, and hence more lucrative, spectator demographic.

Similar sorts of gender expectations and pressures exist for Canada’s female skaters. Women are expected to emulate a soft, delicate femininity reminiscent of, as one coach told me, “an Audrey Hepburn or Grace Kelly era.” The competitive future of female skaters who fail to project such images may, in some cases, be threatened. One female skater I spoke with, for example, told me that, subsequent to a competition, she was informed by a judge that her earring (she had three piercings in each ear), her nose ring, and her weight, were “unfeminine” and that she might fare better in the future should she comport herself “more accordingly and ladylike.” She also suggested that the skater should lose five pounds, as this would make her more attractive to the media.

Clearly then, the gendered images performed in figure skating are culturally constructed images, oftentimes produced in conjunction with the media. Some skaters I spoke with, for instance, informed me that they consult their agents and media organizations for input before deciding upon their annual costume and program themes.

Social Implications/Conclusion

The gendered images favored in figure skating are important to discuss here because they have a variety of negative consequences for the sport. First, the persistence of idealized representations of gender can lead to the onset of debilitating emotional and physical disabilities among skaters. For women in particular, figure skating is notorious for the existence of eating disorders and a variety of other physical and psychological ailments (eg. Davis 1997; Ryan 1995) as a result of the desire to achieve an “idealized femininity.” The narrow range of opportunities for gendered identities also hampers the individual creative talents and artistic abilities of skaters, coaches, and choreographers, many of whom feel limited in the range of options available. Also, it is somewhat surprising, in the supposedly tolerant, multicultural, and inclusive environment of a nation like Canada, that such a rigid and narrow definition of gendered identities is accepted and promoted (oftentimes unconsciously) for public consumption. Clearly, this is a growing concern within the sport due to the increasing power and hegemony of the mainstream media and its powerful position in shaping modern identities. As nation-states gradually begin to lose control over the production of national identities in increasingly globalized contexts, non-state entities like the mass media are taking over or supplanting the state’s role in nation-building. As this article has suggested, this has important connotations for the future of increasingly mediated sports like figure skating, where the bodies of skaters are heavily commodified and mainstream representations of national bodies dominate.

Bibliography

Adams, M. (1997). To be an Ordinary Hero: Male Figure Skaters and the Ideology of Gender. Avante 3(3):93-110.
Butler, J. (1990). Gender Trouble: Feminism and the Subversion of Identity. New York: Routledge.
CTV (2001). CTV and Figure Skating. http: www.ctv.ca/sport, July 16, 2001.
Davis, C. (1997). Eating Disorders and Hyperactivity: A Psycho-biological Perspective. Canadian Journal of Psychiatry 42:168-175.
Pronger, B. (1999). The Arena of Masculinity: Sport, Homosexuality, and the Meaning of Sex. New York: St. Martin’s Press.
Ryan, J. (1995). Little Girls in Pretty Boxes: The Making and Breaking of Elite Gymnasts and Figure Skaters. New York: Doubleday.
Skate Canada (2002). Skate Canada Fact Sheet. Ottawa: Skate Canada.

U.S. Sports Academy2017-04-18T08:56:27-05:00January 3rd, 2005|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology, Women and Sports|Comments Off

An Examination of the Moneyball Theory: A Baseball Statistical Analysis

Submitted by: Ehren Wassermann, Daniel R. Czech, Matthew J. Wilson & A Barry Joyner

INTRODUCTION

Money is a very important aspect in almost every professional sport. In professional baseball, there are large (New York Yankees) and small (Oakland Athletics) market organizations that make important decisions based on their economic status. For example, many smaller city market teams, must spend their money wisely to ensure the best outcome; whereas, a larger city market team has more income that is expendable (Lewis, 2003). This money spending process originates during the Major League Baseball player draft held each June. The draft process involves fifty rounds of selections by all thirty teams. Each team gathers their general managers, scouts, and professional consultants to decide which players should be drafted. The higher the draftee the more valuable he is believed to the team. Therefore, the procedure to decide which players should be selected earliest is very important (Lewis, 2003). According to Lewis (2003) there are two main theories that are being used to narrow the selection process.

The first theory is generally considered the “old” scouting theory. Scouts venture out and evaluate players all over the country. They do not pay particular attention to statistics, but rather base decisions on the five tools: speed, quickness, arm strength, hitting ability and mental toughness (Lewis, 2003). Each scout goes through “scout school” and is given a pamphlet on what should be looked for in certain aspects of baseball, such as arm strength, fielding, running, and the most important hitting. For arm, strength evaluation, scouts are instructed to look for players exhibiting a “fluid arm action and easy release” (Major League Baseball, 2001 p. 10). Furthermore arm strength evaluation is conducted with the assistance of a radar gun. In the fielding category, “a strong arm and defensive skills can and do carry a player to the major leagues” (MLB, 2001 p. 10). Also, “a live, active lower body, quick feet, agility, instinct, . . . alertness, are some of the qualities that go into the rankings of a major league infielder” (MLB, 2001 p. 10). Running is commonly judged through a timed 60 yard sprint (Baechle & Earle, 2003). Hitting is the “most difficult of all scouting categories of judgment” (MLB, 2001 p. 11). A general list of guidelines that scouts look for is: (1). Strength, (2). Starting the bat, generating bat speed, (3). Full arm extension and follow through after making contact, (4). Head stays on ball, (5). Lack of fear, butt stays up at plate, (6). Short stride, (7). Top hand is evident upon making contact and follow through, (8). Head of bat does not lag, (9). Aggressive, hits first good pitch, (10). Short strokes, yet ball jumps off bat, (11). Bat goes to ball (Not a swing through a certain arc area and the ball happens to be in that zone) (MLB, 2001 p. 11). Scouts are instructed not to scout performance but to “watch for things that are done mechanically that will eventually bring results and success” (MLB, 2001 p. 13). When a scout sees a player he then gives the player a certain grade. “The evaluated grade of five (5) in any respective category portrays the player as having, or will have, an average skill of major league standards, currently or once he reaches major league competition” (MLB, 2001 p. 14)

The second theory is based on the Oakland A’s general manager Billy Beane and is illustrated in a novel by Micheal Lewis entitled Moneyball. The Moneyball theory places no emphasis on the body of the athlete or the physical tools that the athlete possess’ (Lewis, 2003). This theory illustrates the simplicity of baseball by asking two questions: Does this player get on base? and Can he hit? According to Lewis (2003), Billy Beane (the inspiration of Moneyball) decided to base his drafting of position players/hitters on certain statistics. His main two statistics included on-base percentage (OBP) and slugging percentage. These two stats combined to form a new statistic called on-base plus slugging (OPS). Another differing aspect in Beane’s approach was his lack of emphasis on power (Lewis, 2003). Therefore, Beane believed that power could be developed, but patience at the plate and the ability to get on base could not. Moreover, Beane believed in the notion to select college players who are experienced on a different level than the high school “phenom” who needs to be developed into a player. Beane’s theory was created based on the works of a sabermetrician named Bill James. “Sabermetrics is the mathematical and statistical analysis of baseball records” (James, 1982 p. 3). James spent years trying to decipher numbers via the Bill James Baseball Abstract, which in turn, resulted in a specific philosophy on hitters.

James’ idea on hitters differs from the draft process of Billy Beane, but Beane adopted his views from James’ ideology. When putting together a lineup, managers must decide the best order in which the team has the best chance of winning. To win the game one must score more runs than the opposing team. This thought provokes the question as to why such great importance is placed on batting averages? “People are in the habit of listing their teams offensive statistics according to batting averages rather than in order of runs scored” (James, 1984 p.10). James believes that “a hitter’s job is not to compile a high batting average, maintain a high on-base percentage, create a high slugging percentage, get 200 hits, or hit home runs” (James, 2001 p. 329). However, part of a hitter’s job from a coach’s perspective, is to hit homeruns, singles, doubles, get on base, drive in runs, and steal bases (James, 2001). James believes the job of a hitter is to create runs. “The essential measure of a hitter’s success is how many runs he has created” (James, 2001 p. 330). James then developed a formula that allows one to establish created runs:

(Hits + Walks) x Total Bases
At-bats + Walks

This formula works 90 % of the time and gives a total of the team’s actual scored runs within 5 % (James, 2001). From this philosophy, Beane developed his theory. The only way to score runs is to get on base and since walks are such a vital part of the created runs formula, on-base percentage should be closely monitored. Even though this formula is very accurate, additional steps can be taken to improve the accuracy. This new formula accounts for the more minute aspects of meaningful baseball statistics. It works off the simple formula:
(A x B)/ C
The A variable adjusts the “on-base” aspect of baseball.

A = hits + walks + hit batsmen – caught stealing – ground into double play (H + W + HBP – CS – GIDP)
The B variable takes into account the advancement of the player.
B = total bases plus .26 times hit batsmen and non-intentional walks, plus .52 times stolen bases, sacrifice hits, and flies (TB + .26(TBB – IBB + HBP) + .52(SB + SH + SF)

The C variable accounts for opportunity.

C = at-bats + total walks + sacrifice hits and flies + hit batsmen (AB + TBB + SF + HBP) (James, 1984 p. 14)

James believed that “figuring the number of runs created is a great tool to evaluate hitters since a hitter’s job is to create runs” (James, 1983 p. 5). Therefore, Beane also placed a major emphasis on what had to be done to create runs and drafted players accordingly.

The difference between these two theories leads to the following questions, what are the optimal attributes of the ideal draft pick? Are young high school prospects with the ideal 5 physical tools more advantageous to draft than the seasoned college player with high offensive Moneyball statistics?

The purpose of this investigation was to answer the question of whether there is a significant difference in on base percentage, slugging percentage and on base + slugging percentage (OPS) between high school and college drafted position players performing at the professional level? It is hypothesized that because of more experience, more rich statistical data, and better competition at the college level, the college baseball players will have better offensive Moneyball statistics than the high school players.

METHODS

Participants

The participants in this study were 60 professional baseball players. More specifically, thirty high school and thirty college players from the 1997 major league professional amateur draft were selected for participation in this study. The age range of the participants was 18 to 23 years of age. The mean age of the high school players was x=18.3 and the mean age of the college players is x=20.9. The mean age for the entire participant sample is 19.6 years of age.

Procedure

A comprehensive internet search was conducted to locate the high school and college players from the 1997 amateur draft. The authors felt that four years was enough time to examine a drafted player’s moneyball statistics, as four years is the time when many players move to their highest level of play. By use of the following website (www.sports-wired.com), draft information i.e. the top thirty drafted position players from high school and college Moneyball statistics were obtained. Each player’s professional (Major and Minor League) Moneyball statistics (slugging percentage, on-base percentage, and on-base plus slugging) from their rookie year to their 4th year of playing professionally were utilized. Slugging percentage was calculated as (Total Bases divided by At Bats). On base Percentage was calculated as (Hits + Base on Ball + Hit By Pitch) divided by (At Bats + Base on Balls + Hit by Pitch + Sacrifice Flies)

Results

Descriptive statistics included the means and standard deviation ranges overall and as a function of both major league and minor league slugging percentage, on base percentage, and OPS. A score was calculated, comparing college and high school players, for each variable using the SPSS 12.0 statistical package. An independent samples T-test was utilized to compare differences between collegiate and high school players. An alpha level of .05 was used for all statistical tests.

The mean and standard deviation for the college and high school player’s performances in the major and minor leagues is illustrated in Table 1. An independent T-test revealed a significant difference between college and high school minor league slugging percentage. No significant differences were found when comparing college and high school on base percentage and OPS.

DISCUSSION

The purpose of this study was to compare the top collegiate and high school drafted baseball player’s professional offensive Moneyball statistics- slugging percentage, on base percentage, and on base plus slugging (OPS) over a four year period. It was hypothesized that college drafted players would have significantly higher Moneyball related offensive statistics than the high school players. The results did not support the hypothesis in that the only significant difference was between college and high school minor league slugging percentage. These results may contradict some of Beane’s Moneyball theory (Lewis, 2003).

Beane postulated in Lewis’ (2003) that college players would perform better than high school players. This hypothesis is due to several factors. First, college players are more mature physically, mentally, and emotionally than high school players. This maturity would enable them to handle the stresses that are involved in minor league baseball such as, long bus rides, the occasional slump, and unfamiliarity with surroundings. Secondly, college players play against stronger and more advanced competition more often than high school players. This allows for more experience which may provide a better preparation for professional play. Finally, college players play a longer schedule and usually practice year round. This consistent playing allows for skills to be refined and mastered. Using these facts, Beane decided that college players are a better investment than high school players (Lewis, 2003).

The results may not have supported the hypothesis because both groups of athletes had to make adjustments to professional baseball. The high school players may adapt more easily to new changes because they are younger and may have had less influence from other less experienced coaches; however, college players may have developed a certain approach to hitting from college that contradicts a new approach at the professional level. Therefore, the college players may take a longer time to alter their approach to hitting and thus hindering their productivity at the plate. Another factor may be due to the notion that high school players are usually placed in lower levels of professional baseball than college players, which in turn may even the offensive statistics. Lastly, college baseball players may have the opportunity to gain more experience with the wooden bat when competing in collegiate summer leagues.

The rest of baseball has seemed to take notice of the Billy Beane philosophy of drafting. In the 2003 First-Year Player Draft, more than 70 % of the players drafted through the first twenty rounds were from a four-year college or a junior college (Mayo/MLB.com, 2003). This percentage was “a marked increase compared to the last three years” (Mayo/MLB.com, 2003, p.1). Even though this significant increase in drafting college players seems to be the trend, “there [has been] little statistical data to support doing that” (Newman/MLB.com, 2003, p. 2). Baseball America researched the 1990s draft and announced that 2,115 players signed in the first ten rounds between 1990-97 (Newman/MLB.com, 2003). “The group includes 1,024 collegians, 398 of whom (38.9 %) reached the Majors” and “920 prepsters, 259 (28.2 %) did the same” (Newman/Mlb.com, 2003, p 2-3). It was noted that most of the differences amounts to only limited time in “The Show”. However, “further research noted that 90 college players (8.8 %) and 77 high school players (8.4 %) became Major League regulars for at least a few seasons” (Newman/MLB.com, 2003, p. 3). These last numbers correlate with the findings of this study illustrating little difference between the productivity of college players versus high school players.

It is important to note that there were limitations to this study. For example, one relevant limitation was the number of participants used in the study. A more significant result could have been established utilizing the entire draft. With more participants and more statistical data, a better idea of the purpose could have been allocated. Another limitation that needs to be noted is the speed at which certain players are promoted. Some high draft picks (top ten rounds) are quickly promoted to a higher level, regardless of their success at the current level. This is due to the amount of money invested in the athlete. For example, a fourth round shortstop may get a signing bonus of 450,000 dollars while the 38th round shortstop may only get 1,000 dollars.

Consequently, the organization has a tremendous amount of money invested in the fourth rounder and they need him to develop faster (Lewis, 2003). Hence, even though this player may not be physically and mentally ready, the organization wants to see a quick return on its investment. Finally, a major limitation is the amount of playing the athlete does. Each year when the regular season ends, many players face the decision of playing winter ball (Lewis, 2003). Many believe that rest is needed to help the body recover from a long, strenuous season; however, others believe that winter ball allows them to gain an extra advantage over their competition. No matter the limitations there is significant evidence against the Billy Beane philosophy.

What this study attempted to illustrate was how an organization with a low budget produces quality baseball players using a new philosophy unorthodox to the norm of baseball (Lewis, 2003). From a financial standpoint, the authors believe there are two mindsets regarding the lack of significance. Because of the minimal significant differences between college and high school players’ “moneyball” statistics, many MLB teams might want to disregard the notion that cheaper “moneyball” college drafted players are better investments because they do not do as well as their high school drafted counterparts. However, even though the comparison is not significant statistically, the statistics may be significant to an organization/coach, which is playing the Moneyball way of baseball. A small market organization may want to pay less for college players who average .432 (slugging percentage), .344 (on base percentage) and .776 (OPS) than pay more for high school players who average .396 (slugging percentage), .332 (on base percentage), .728 (OPS) over a four year time period. Even though slugging percentage is the only significant difference, the college players have better statistics from a baseball playing perspective. This difference may be the rationale as to draft cheaper players based on the Moneyball statistics and play the Moneyball way of baseball, especially for small market teams. More research, both qualitative and quantitative needs to be completed before making a conclusion regarding the Moneyball way of drafting and playing professional baseball. If the Moneyball method is proven as significant, it could revolutionize the baseball industry. The importance of this theory is not only relevant monetarily, but it could institute a new theory to the selection of baseball players. Future research should examine if other organizations are using Beane’s philosophy and if they are how this will affect the Oakland organization. Moreover, future research should analyze OPS and Runs Created.

REFERENCES

1. Baechle, T.R., & Earle, R.W. (2000). Essentials of Strength Training and
Conditioning. Human Kinetics: Champaign, Il.
2. James, B. (1982). The Bill James Baseball Abstract 1982. New York: Ballantine
Books.
3. James, B. (1983). The Bill James Baseball Abstract 1983. New York: Ballantine
Books.
4. James, B. (1984). The Bill James Baseball Abstract 1984. New York: Ballantine
Books.
5. James, B. (2001). The New Bill James Historical Baseball Abstract. New York:
The Free Press.
6. Lewis, M. (2003). Moneyball: The Art of Winning the Unfair Game. New York:
W.W. Norton and Company.
7. Major League Baseball. (2001). Major League Baseball Scouting Pamphlet.
8. Mayo, J. (2003). A Strong Lean Toward Collegians: Trend Away from High
Schoolers Continues in Draft. November 24, 2003, http://mlb.mlb.com/NASApp/mlb/mlb/news/mlb_news.jsp?ymd=20030603&content_id=353523&vkey=draft2003&fext=.jsp&c_id=mlb.
9. Mayo, J. (2003). High School Players Fall in Draft. November 24, 2003,
http://mlb.mlb.com/NASApp/mlb/mlb/news/mlb_news.jsp?ymd=20030604&content_id=355074&vkey=draft2003&fext=.jsp.
10. Newman, M. (2003). High School vs. College: Does Either Provide a Better
Shot at a “Sure Thing?”. November 24, 2003,
http://mlb.mlb.com/NASApp/mlb/mlb/news/mlb_news.jsp?ymd=20030520&content_id=328934&vkey=news_mlb&fext=.jsp&c_id=mlb

U.S. Sports Academy2015-03-20T10:41:26-05:00January 2nd, 2005|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Predicting Fund Raising Revenues in NCAA Division I-A Intercollegiate Athletics

Submitted by: Chad McEvoy

Introduction

According to the NCAA (Fulks, 2001), contributions from alumni and others, or fund raising, is the second-largest revenue source for Division I-A athletic programs, trailing only ticket sales. Fund raising accounts for nearly five million dollars of the typical Division I-A athletic programs’ $25 million of total revenue, and, as such, is clearly a vital source of funding for intercollegiate athletic programs. Therefore, the ability to forecast fund raising revenues is crucial for college athletic departments. This study will create a model to predict annual fund raising revenues in NCAA Division I-A intercollegiate athletics, thus aiding practitioners in predicting these revenues on their own respective campuses.

Related Literature

Numerous authors have examined the relationships between intercollegiate athletic programs and higher education. These studies have focused on the relationship between college sports performance and variables such as applicants to universities (Allen & Peters, 1982; Chressanthis & Grimes, 1993; Murphy & Trandel, 1994; Toma & Cross, 1998; Zimbalist, 2001), SAT scores of incoming students (Bremmer & Kesserling, 1993; McCormick & Tinsley, 1987; Mixon, 1995; Tucker & Amato, 1993), and university fund raising (Baade & Sundberg, 1996; Brooker & Klastorin, 1982; Budig, 1976; Gaski & Etzel, 1984; Grimes & Chressanthis, 1994; McCormick & Tinsley, 1990; Sack & Watkins, 1985; Sigelman & Carter, 1979). Few studies, however, have investigated athletic fund raising in this regard.

Sigelman and Brookheimer (1983) examined the relationship between 11 predictor independent variables and contributions to both athletics and university fund raising programs at 57 NCAA Division I-A institutions in major athletic conferences using a multiple regression analysis. Football success (r = .335) and traditionalism (r = .242), a scaled measurement of the social and political culture towards civic responsibility and philanthropy, were determined to be significant predictors of giving to intercollegiate athletics annual fund raising programs, albeit not overly strong predictors given their Pearson’s coefficient values.

Coughlin and Erekson (1984, 1985) utilized multiple linear regression analysis to model contributions to athletic fund raising program using 16 independent variables. Coughlin and Erekson utilized 1980-1981 athletics fund raising data published in the Omaha World-Herald as their measurement of the contributions dependent variable, as Sigelman and Brookheimer (1983) had done previously. Coughlin and Erekson’s final regression model accounted for 58% of the variance in predicting athletic contributions. The authors identified football attendance, conference affiliation, bowl participation, state population, men’s basketball winning percentage, and professional competition to be significant determinants of athletic contributions.

Daughtrey and Stotlar (2000) investigated the relationship between contributions to both athletics and the university and winning a national championship in football at the Division I-AA, Division II, and Division III levels. Daughtrey and Stotlar found significant relationships between football championships and increased contributions to athletics with Division II and Division III schools and between football championships and increased contributions to the university with Division III institutions. The authors’ delimitations of studying only national champions and not examining Division I-A institutions prevent useful comparisons between their work and the study conducted here.

No other published studies were identified that attempted to use a variety of variables to predict fund raising contributions to NCAA Division I-A intercollegiate athletics programs. As such, the only published works investigating the ability to predict athletic fund raising contributions are currently 20 years old and each relies upon contributions data collected in 1980-1981. Obviously, much has changed in intercollegiate athletics since then. If an athletic fund raising practitioner today tried to understand and predict fund raising contributions based upon the existing body of literature, they would be relying upon considerably outdated research. Clearly then, there is a need to re-examine the prediction of athletic fund raising contributions, as is the purpose of this study.

Methods

Subjects

The population for this study was defined as all 119 NCAA Division I-A athletic programs and their athletic fund raising contributions for each of the five-year span from 1998-1999 to 2002-2003. Questionnaires were sent to the athletic fund raising director at each of the 119 institutions in performing a census of the population. Thirty-five questionnaires were returned, representing 171 usable subjects, for a usable response rate of 28.7%.

Variables

Based on the work of Coughlin and Erekson (1984, 1985) and Sigelman and Brookheimer (1983), 13 predictor variables were selected to use in explaining the variation in annual athletic fund raising contributions: football and men’s basketball winning percentages for the year examined, the change in football and men’s basketball winning percentages from the previous year, average home attendance for football and men’s basketball in the year examined, whether the school is a member of a “major” athletic conference, whether the school is a public or private institution, state population, and four categorical variables to control for fixed-effects in the time-series regression analysis. Each of these variables is described further in Table 1.

Procedures

Questionnaires were sent to athletic fund raising directors at all 119 NCAA Division I-A athletic programs to collect dependent variable data. Data collection on each of the predictor variables was performed as discussed in Table 1. A fixed-effects ordinary least squares (OLS) multiple regression equation was developed to empirically explain annual athletic fund raising contributions. The fixed-effects model is used to control for changes over time due to the use of panel data. Four indicator variables were used to represent the five years of data from 1998-1999 to 2002-2003. A significance level of .01 was established a priori to reduce the risk of Type 1 error common with time-series regression analyses and large sample sizes.

Results

Table 2 provides descriptive data for the continuous variables included in the regression equation. The results show that the average annual athletic fund raising contributions total was $4,065,616. Additionally, the average home football game attendance was 43,119 and the average men’s basketball game attendance was 8,749.

Five of the 13 independent variables were found to be significantly related to athletic fund raising contributions at the .01 level, including football home attendance (r=.721), conference affiliation (r=-.621), football winning percentage (r=.322), type of institution (r=-.302), and men’s basketball home attendance (r=.237). In examining the correlation coefficients between the independent variables, only the relationship between football attendance and conference affiliation was above .500 or below -.500 (r=-.651), thus providing evidence that multicollinearity was not problematic.

Table 3 summarizes the multiple regression results. The model was a statistically significant estimator of annual athletic fund raising contributions. The model F-statistic equaled 18.647 and was significant at the .01 level. In addition, the model explained 60.7% of the variation in spectator attendance and the adjusted R2 was .574. The R2 and adjusted R2 findings were similar to those found in Coughlin and Erekson (1984, 1985). Additionally, this type of regression analysis allows for an estimation of the magnitude of change in annual contributions based upon a change in values of the independent variables. For example, the results suggest that membership in one of the six conferences with automatic bids to the Bowl Championship Series in football is worth more than $2.5 million per year in athletic fund raising contributions to conference members. Also, the data suggests that annual athletic fund raising contributions would increase by $70 for each average attendee increase at home football games.

Discussion

The purpose of this study was to predict annual athletic fund raising contributions in NCAA Division I-A intercollegiate athletics, providing a needed re-examination of this issue given the dated works in this area of the literature. Despite the passing of two decades and major changes in intercollegiate athletics since the studies of Sigelman and Brookheimer (1983) and Coughlin and Erekson (1984, 1985), this study supports the findings of those previous works, particularly Coughlin and Erekson. As with their work, this investigation found both home football attendance and conference affiliation to be statistically significant predictors of annual athletic fund raising contributions. Additionally, the amount of variance explained in annual athletic fund raising contributions in this study (R2=.607) was extremely close to that of Coughlin and Erekson (R2=.58). None of the similarities between the findings of these studies are, in and of themselves, overly surprising; however, these similarities are somewhat surprising given the radical changes in intercollegiate athletics since the early 1980’s. These changes include a dramatic increase in media/television coverage, rapid increases in revenues and expenses among athletic programs, the creation of the Bowl Championship Series, conference realignment, and progress towards gender equity. It is in the context of all of these major changes that the similarities between this study and previous dated works are noteworthy.

These results indicate that, assuming conference affiliation does not change, an athletic fund raising practitioner should carefully track home football attendance as an indicator of fund raising contributions. A fairly strong positive relationship (r=.721) was found between these two variables. Other changeable variables of interest to practitioners in this regard are football winning percentage (r=.322) and men’s basketball home attendance (r=.237), however, neither approaches home football attendance in the ability to predict athletic fund raising contributions.

References

Allen, B. H., & Peters, J.I. (1982). The influence of a winning basketball program upon undergraduate student enrollment decisions at DePaul University. In M.J. Etzel & J.F. Gaski (Eds.), Applying marketing technology to spectator sports (pp. 136-148). Notre Dame, IN: University of Notre Dame.
Baade, R. A., & Sundberg, J. O. (1996). Fourth down and gold to go? Assessing the link between athletics and alumni giving. Social Science Quarterly, 77, 789-803.
Bremmer, D. S., & Kesserling, R. G. (1993). Advertising effects of university athletic success. Quarterly Review of Economics and Business, 33, 409-421.
Brooker, G., & Klastorin, T. D. (1981). To the victors belong the spoils? College athletics and alumni giving. Social Science Quarterly, 62, 744-750.
Budig, J. E. (1976). The relationships among intercollegiate athletics, enrollment, and voluntary support for public higher education (Doctoral dissertation, Illinois State University, 1976). Dissertation Abstracts International, 37, 4006A.
Chressanthis, G. A., & Grimes, P. W. (1993). Intercollegiate sports success and first-year student enrollment demand. Sociology of Sport Journal, 10, 286-300.
College Football Data Warehouse (2004). Retrieved from cfbdatawarehouse.com.
Coughlin, C. C., & Erekson, H.O. (1984). An examination of contributions to support intercollegiate athletics. Southern Economic Journal, 66, 180-195.
Coughlin, C. C., & Erekson, H.O. (1985). Contributions to intercollegiate athletic programs: Further evidence. Social Science Quarterly, 66, 194-202.
Fulks, D. L. (2002). Revenues and expenses of Division I and II intercollegiate athletics programs: Financial trends and relationships-2001. Indianapolis: National Collegiate Athletic Association. Retrieved from http://www.ncaa.org.
Gaski, J. F., & Etzel, M. J. (1984). Collegiate athletic success and alumni generosity: Dispelling the myth. Social Behavior and Personality, 12(1), 29-38.
Grimes, P. W., & Chressanthis, G. A. (1994). Alumni contributions to academics: The role of intercollegiate sports and NCAA sanctions. American Journal of Economics and Sociology, 53, 27-40.
McCormick, R. E., & Tinsley, M. (1987). Athletics versus academics: Evidence from SAT scores. Journal of Political Economy, 95, 1103-1116.
McCormick, R. E., & Tinsley, M. (1990). Athletics and academics: A model of university contributions. In B. L. Goff & R. D. Tollison (Eds.), Sportometrics (pp. 193-204). College Station, TX: Texas A&M University Press.
Mixon, F. G. (1995). Athletics v. academics: Rejoining evidence from SAT scores. Education Economics, 3, 277-283.
Murphy, R. G., & Trandel, G. A. (1994). The relation between a university’s football record and the size of its applicant pool. Economics of Education Review, 13, 265-270.
National Collegiate Athletic Association. (2004). Attendance data. Retrieved from http://www.ncaa.org.
Sack, A. L., & Watkins, C. (1985). Winning and giving. In D. Chu, J. O. Segrave, & B. J. Becker (Eds.), Sport and Higher Education (pp. 299-306). Champaign, IL: Human Kinetics.
Sigelman, L., & Bookheimer, S. (1985). Is it whether you win or lose? Monetary contributions to big-time college athletic programs. Social Science Quarterly, 64, 347-359.
Sigelman, L., & Carter, R., (September, 1979). Win one for the giver? Alumni giving and big-time college sports. Social Science Quarterly, 60, 284-293.
Toma, J. D., & Cross, M. E. (1998). Intercollegiate athletics and student college choice: Exploring the impact of championship seasons on undergraduate applications. Research in Higher Education, 39, 633-661.
Tucker, I. B., & Amato, L. (1993). Does big-time success in football or basketball affect SAT scores? Economics of Education Review, 12, 177-181.
Zimbalist, A. (2001). Unpaid professionals: Commercialism and conflict in big-time sport. Princeton, NJ: Princeton University Press.

Table 1

<th”>Variable <th”>Description and Sources

Variable Descriptions and Data Sources
CONTRIB	Dependent variable representing one year’s annual athletic fund raising contributions, not including capital campaigns. Collected via questionnaire self-reporting from each institution’s athletic fund raising director.
FBWINPCT	Represents a school’s football winning percentage for that respective year. Collected from ncaa.org.
BBWINPCT	Represents a school’s men’s basketball winning percentage for that respective year. Collected from ncaa.org.
FBWINCH	Represents the change in a school’s football winning percentage from the previous year. Collected from ncaa.org.
BBWINCH	Represents the change in a school’s football winning percentage from the previous year. Collected from ncaa.org.
FBATTEN	Represents a school’s average home football attendance for that respective year. Collected from ncaa.org.
BBATTEN	Represents a school’s average home men’s basketball attendance for that respective year. Collected from ncaa.org.
CONFERNC	Represents whether or not a school is a member of one of the six major Division I-A conferences that receives an automatic bid to the Bowl Championship Series in football. Coded as a “0” for BCS conference, “1” for non-BCS conference.
INSTTYPE	Represents whether the school is a public or private institution. Coded as a “0” for public institution, “1” for private institution.

Table 2

<th”>Variable <th”>Mean <th”>Standard Deviation <th”>Minimum <th”>Maximum

Descriptive Statistics
CONTRIB	$4,065,615.92	$3,502,701.71	$201,791	$14,363,913
FBWINPCT	.513	.212	.000	1.000
BBWINPCT	.575	.155	.222	.892
FBWINCH	-.007	.206	-.727	.492
BBWINCH	-.005	.151	-.419	.398
FBATTEN	43,118.94	25,737.64	6,595	111,175
BBATTEN	8,748.54	4,975.17	935	22,248
CONFERNC	.40	.491	.00	1.00
INSTTYPE	.16	.371	.00	1.00
POPULATE	8,596,946.04	8,142,659.46	1,808,344	33,871,648

Note: N=171

Table 3

<th”>Variable <th”>Unstandardized Beta Coefficient <th”>Standard Error <th”>T-statistic <th”>P-value

Regression Results
FBWINPCT	934.813	1,259.942	.742	.459
BBWINPCT	1,474.402	1,631.419	.904	.368
FBWINCH	-658.047	1,064.320	-.618	.537
BBWINCH	434.665	1,386.728	.313	.754
FBATTEN	70.567	12.129	5.818	.000
BBATTEN	-137.200	53.892	-2.546	.012
CONFERNC	-2,587,336.224	529,184.751	-4.889	.000
INSTTYPE	-636,637.927	530,003.528	-1.201	.231
POPULATE	-.0253	.023	-1.105	.271
TIME0203	1,417,825.114	567,331.928	2.499	.013
TIME0102	1,207,864.865	568,200.487	2.126	.035
TIME0001	916,870.951	560,734.574	1.635	.104
TIME9900	392,457.586	565,992.692	.693	.489
Constant	1,443,097.013	1,260,510.634	1.145	.254

Note: R2=.607, Adjusted R2=.574, F-statistic=18.647, P-value=.000

U.S. Sports Academy2015-03-20T10:32:05-05:00January 1st, 2005|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off