Browsing by Author "Wang, Wenting"

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    Bracketing the NCAA Women's Basketball Tournament
    (North Dakota State University, 2014) Wang, Wenting
    This paper presents a bracketing method for all the 63 games in NCAA Division I Women's basketball tournament. Least squares models and logistic regression models for Round 1, Round 2 and Rounds 3-6 were developed, to predict winners of basketball games in each of those rounds for the NCAA Women's Basketball tournament. For the first round, three-point goals, free throws, blocks and seed were found to be significant; For the second round, field goals and average points were found to be significant; For the third and higher rounds, assists, steals and seed were found to be significant. A complete bracket was filled out in 2014 before any game was played. When the differences of the seasonal averages for both teams for all previously mentioned variables were considered for entry in the least squares models, the models had approximately a 76% chance of correctly predicting the winner of a basketball game.
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    Predicting the Outcomes of NCAA Women’s Sports
    (North Dakota State University, 2017) Wang, Wenting
    Sports competitions provide excellent opportunities for model building and using basic statistical methodology in an interesting way. More attention has been paid to and more research has been conducted pertaining to men’s sports as opposed to women’s sports. This paper will focus on three kinds of women’s sports, i.e. NCAA women’s basketball, volleyball and soccer. Several ordinary least squares models were developed that help explain the variation in point spread of a women’s basketball game, volleyball game and soccer game based on in-game statistics. Several logistic models were also developed that help estimate the probability that a particular team will win the game for women’s basketball, volleyball and soccer tournaments. Ordinary least squares models for Round 1, Round 2 and Rounds 3-6 with point spread being the dependent variable by using differences in ranks of seasonal averages and differences of seasonal averages were developed to predict winners of games in each of those rounds for the women’s basketball, volleyball and soccer tournament. Logistic models for Round 1, Round 2 and Rounds 3-6 that estimate the probability of a team winning the game by using differences in ranks of seasonal averages and differences of seasonal averages were developed to predict winners of games in each of those rounds for the basketball, volleyball and soccer tournaments. The prediction models were validated before doing the prediction. For basketball, the least squares model developed by using differences in ranks of seasonal averages with a double scoring system variable predicted the results of a 76.2% of the games for the entire tournament with all the predictions made before the start of the tournament. For volleyball, the logistic model developed by using differences of seasonal averages predicted 65.1% of the games for the entire tournament. For soccer, the logistic regression model developed by using differences of seasonal averages predicted 45% of all games in the tournament. Correctly when all 6 rounds were predicted before the tournament began. In this case, team predicted to win in the second round or higher might not have even made it to this round since prediction was done ahead of time.