Abstract
Data mining is becoming increasingly used in sports. Sport data analyses help fans to understand games and teams’ results. Information provided by such analyses is useful for game lovers. Specifically, the information can help fans to predict which team will win a game. Many scholars have devoted attention to predicting the results of various sporting events. In addition to predicting wins and losses, scholars have explored team scores. Most studies on score prediction have used linear regression models to predict the scores of ball games; nevertheless, studies have yet to use regression tree models to predict basketball scores. Therefore, the present study analyzed game data of the Golden State Warriors and their opponents in the 2017–2018 season of the National Basketball Association (NBA). Strong and weak symmetry requirements were identified for each team. We developed a regression tree model for score prediction. After predicting the scores of each player on two teams, we summed and compared the predicted total scores to obtain the predicted results (lose or win) of the team of interest. The results of this study revealed that the regression tree model can effectively predict the score of each player and the total score of the team. The model achieved a predictive accuracy of 87.5%.
Highlights
Advanced statistical methods were commonly used in various studies
The results revealed that the linear regression and Bayesian linear regression models were superior to the other models
We considered the Golden State Warriors (GSW), one of the 30 National Basketball Association (NBA) teams, for analysis
Summary
Advanced statistical methods were commonly used in various studies. A soft computing model used a learning approach for addressing data management over social networks [1]. Dulebenets et al [2]. Applied regression models to estimate the effects of various factors on the driving ability of individuals. Andrée et al [3] estimated a penalized non-parametric model of environmental output across economic development. A multivariate random parameter Tobit model was utilized to determine the factors that drive both the crash occurrence probability and the crash rate of 65+ roadway users [4]. Narasingam et al [5] applied sparse regression to determine the structure of reduced-order model on a hydraulic fracturing process
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