Related Experiment Video
Updated: Jun 24, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
Game-related statistics that discriminate winning from losing in NCAA Division-I men's basketball
Dimitrije Cabarkapa1, Damjana V Cabarkapa1, Andrew C Fry1
1Jayhawk Athletic Performance Laboratory-Wu Tsai Human Performance Alliance, Department of Health, Sport and Exercise Sciences, University of Kansas, Lawrence, KS, United States.
Abstract:
The purpose of the present study was to examine differences in game-related statistics between winning and losing game outcomes and determine which performance parameters have the greatest impact in classifying winning from losing game outcomes at the National Collegiate Athletic Association (NCAA) Division-I men's basketball level of competition. The data scraping technique was used to obtain publicly available data over a 2018-2019 season span. The total number of games examined was 5,147. Independent t-tests were used to examine statistically significant differences between winning and losing game outcomes, while a full model discriminant function analysis was used to determine the relative contribution of each game-related statistic and its ability to classify winning from losing game outcomes (p < 0.05). Alongside scoring a greater number of points at the end of the game, the findings of the present study indicate that winning teams: (a) attempted and made more field goals, three-point, and free-throw shots, (b) accumulated more defensive and total rebounds, assists, steals, and blocks, (c) had fewer turnovers and personal fouls, and (d) secured greater field goal, three-point, and free-throw shooting percentage. Moreover, the top three performance parameters discriminating winning from losing game outcomes were field goal percentage, defensive rebounds, and assists, accounting for 16.8%, 12.2%, and 12.0% of the total percentage of explained variance, respectively (i.e., 41.0% combined). Overall, these findings support the expected roles of offensive and defensive game-related statistics and provide further insight into how they work together to optimize the chances of securing the desired game outcome.
More Related Videos
Related Concept Videos
Social Facilitation
Standard Deviation
Weighted Mean
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Statistical Significance
Regression Toward the Mean

