Modeling and predicting success in Men's volleyball: a multiple approach analysis

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Kidd, Ian
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Volleyball has evolved considerably in its 127-year life span. New rules and strategies have been implemented, which have changed how the game is played. There are many diverse levels and speeds with which to contend. Thus, for any volleyball player, it is advantageous to understand the overall flow of the game being played, that is, to have a high “volleyball IQ.” In a professional setting, players must be able to make intelligent predictions in split-seconds, so volleyball IQ could be considered a factor in achieving a higher level in this sport. In addition to volleyball IQ, what other factors significantly contribute to a volleyball player’s success. This study aims to identify the physical and mental traits volleyball players must demonstrate for success. Several explanatory variables related to genetics, volleyball IQ, and recorded playing time of volleyball players in the National Collegiate Athletic Association (NCAA) will be considered to predict which variables are closely related to success in the game of volleyball. Two models will be formed for predicting success. The first model will be for predicting the winning percentage of a team given total team statistics, while the second model will be for predicting the amount of playing time a volleyball player is on the court. Data collection will consist of team and individual statistics from the top 51 teams in the NCAA men's volleyball league. The first data set, which has as its response team winning percentage, will be modeled with different regression models, including linear, logistic, beta, and regression with transformations. The second, larger data set, which consists of individual player statistics, and whose response is sets played per match, was modeled through neural networks. Comparisons and discussions of drawbacks and benefits of each type of model for analyzing volleyball success data will be included