Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line

Song, Kai; Gao, Yiran; Shi, Jian

doi:10.1016/j.physa.2020.124411

Cited by 10 publications

(10 citation statements)

References 21 publications

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“…For example, the author of [ 7 ] built a simple, weighted and penalized regression model using the match-up, date and final score records to predict baseball, basketball, American football and hockey outcomes. However, most of the existing studies in this regard are focused on basketball, in particular, on the National Basketball Association (NBA) games, since NBA is the most popular basketball league in the world [ 4 , 6 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The author of [ 13 ] built a model based on regression tree, linear regression and support vector regression to predict the final score of the Golden State Warriors (an NBA team) in the 2017–2018 season. The author of [ 14 ] proposed a model based on the gamma process to predict the total points of NBA games, predicting the final total score of both teams.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A total of 14 variables were collected and used in this study. One is the final score of a team; the remaining 13 are the most commonly used statistics of a game, such as the team’s defensive performance and game-related offenses [ 4 , 6 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Table 1 shows variable definitions; variable

is the i -th variable at the t -th game and variable

is the final score at the t -th game, which is used as the target variable of this study.…”

Section: Proposed Basketball Game Score Prediction Schemementioning

confidence: 99%

“…Sports outcomes prediction has attracted attention in different sports [ 1 , 2 , 3 , 4 , 5 , 6 ]. However, most of the existing studies in this regard are focused on basketball, in particular, on the National Basketball Association (NBA) games, since the NBA is the most popular basketball league in the world [ 4 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Most existing research related to basketball games is aimed at predicting the win/lose (W/L) outcomes of a game [ 3 , 4 , 6 , 8 , 9 , 10 , 12 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Most existing research related to basketball games is aimed at predicting the win/lose (W/L) outcomes of a game [ 3 , 4 , 6 , 8 , 9 , 10 , 12 , 15 ]. Only a few studies have focused on the prediction of basketball game scores [ 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association

Chen

Jhou

Lee

et al. 2021

Entropy

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The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018–2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.

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