Academic journal article Journal of Sport Behavior

Utility of Team Indices for Predicting End of Season Ranking in Two National Polls

Academic journal article Journal of Sport Behavior

Utility of Team Indices for Predicting End of Season Ranking in Two National Polls

Article excerpt

The number of college basketball games aired on network television has risen dramatically in recent years. According to recent National Collegiate Athletic Association (NCAA) estimates, one-half million paid applicants competed through a lottery format for the privilege of being among the 22,500 people in attendance at the 1994 NCAA Final Four tournament in Charlotte, NC. As recently as 1991, the final tournament alone was estimated to generate in excess of one billion dollars from existing television contracts (Deckard, 1991). It appears that college basketball has indeed succeeded in capturing the hearts and minds of the American public.

A team's success during any given year may be evaluated using a number of different measures. Many teams consider a number one ranking in post-season polls to be the only hallmark of a successful season. For others, just being among the "Top 25" is enough to create a sense of validity for the season's efforts. For many teams as well as fans, coaches' polls provide the sport and its faithful with a sense of empirical credibility (Thomas, 1991). Since the Associated Press introduced national polls in 1949, 28 number one ranked teams have either won the national championship or finished as a runner-up (Vitale & Douchant, 1994). Yet, national polls represent only one of many benchmarks used by teams to measure success. Many Division 1 coaches even consider polls to be more of an anathema than an actual measure of achievement. For these coaches and programs, other Indicators such as team statistics (e.g., field goal percentage, free-throw percentage, number of rebounds) offer a much more valid and reliable means of monitoring progress throughout the competitive season.

Several aspects of the game have been offered as predictors of a team's performance (Cooper, DeNeve, & Mosteller, 1992; Ittenbach, Kloos, & Etheridge, 1992; Merskey, 1987; Pim, 1986; Wood, 1992). However, apart from a few studies utilizing probability models in football (Stern, 1991; Thompson, 1975), basketball (Schwertman, McCready, & Howard, 1991), or baseball (Albright, 1993), a general lack of statistical scrutiny of these statistics and polls exists in the literature (Ittenbach et al., 1992; Stefani, 1977). Given the widespread appeal of college basketball today, the acceptance of media polls as a criteria for success, and the perceived importance of team statistics for most Division 1 basketball programs, a systematic analysis of team statistics as they relate to final season rankings seems long overdue. The purpose of the present study, then, is twofold: first, to determine if team statistics can be used to predict final season rankings in two major media polls using the population of teams participating in the 1991 NCAA Division 1 Men's Basketball Tournament and, second, to identify variables most predictive of those rankings.


Statistics for the 64 teams competing in the 1991 NCAA Division 1 Men's Basketball Tournament were gathered and analyzed. Indices of team performance, as reported in the NCAA Tournament section of the March 11 and March 14 issues of USA Today newspaper ("Men's Division," 1991; "NCAA by the Numbers," 1991) composed the data used in the secondary data analysis. Six predictor variables were used in the analyses: points per game, points allowed, field-goal percentage, number of free-throws, three point field-goal percentage, and number of rebounds. Percentages were converted to whole number values (x 100) for use in the parametric analyses. USA Today/CNN and United Press International (UPI) final season cumulative point total rankings served as the two criterion variables. These two polls were selected for this study because all rankings are based on the perceptions of college basketball coaches.

Three different sets of analyses were conducted in this study. First descriptive statistics were computed for each of the six dependent variables. Second, two full-model regression analyses were computed and tested for significance. …

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