A model for competitiveness level analysis in sports competitions: Application to basketball

The degree of overall competitiveness of a sport league is a complex phenomenon. It is difficult to assess and quantify all elements that yield the final standing. In this paper, we analyze the general behavior of the result matrices of each season and we use the corresponding results as a probably density. Thus, the results of previous seasons are a way to investigate the probability that each team has to reach a certain number of victories. We developed a model based on Shannon entropy using two extreme competitive structures (a hierarchical structure and a random structure), and applied this model to investigate the competitiveness of two of the best professional basketball leagues: the NBA (USA) and the ACB (Spain). Both leagues’ entropy levels are high (NBA mean 0.983; ACB mean 0.980), indicating high competitiveness, although the entropy of the ACB (from 0.986 to 0.972) demonstrated more seasonal variability than that of the NBA (from 0.985 to 0.990), a possible result of greater sporting gradients in the ACB. The use of this methodology has proven useful for investigating the competitiveness of sports leagues as well as their underlying variability across time.     

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

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.     

多自由度篮球机器人复杂路径跟踪控制系统设计

针对篮球机器人复杂路径识别精度偏低,运行能耗较大等问题,提出基于超声波的多自由度篮球机器人复杂复杂路径跟踪控制系统设计方法。通过MAX232芯片设计控制系统的接口电路,采用低功耗CMOS监控电路芯片MAX706构建监控电路,超声波能够精确提供机器人所遇障碍物距离信息,如有障碍则将接收到的信息进行转换,以电信号的形式反馈给主控板。软件部分利用主控板控制器的程序以及超声波测距程序的设计实现。实验表明,所设计控制系统有效提高了篮球机器人对障碍物的识别率,减小了系统运行所用能耗。