A Neural Network Model of the NBA Most Valued Player Selection Prediction

Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence Pub Date : 2019-08-26 DOI:10.1145/3357777.3357786

Yuefei Chen, Junyan Dai, Changjiang Zhang

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引用次数: 5

Abstract

This study analyzed all the performance of the players in the National Basketball Association (NBA) during a particular season and predicted the most valued players (MVP) of that season. The NBA game is the most popular basketball game all over the world. Every game attracted hundreds of and thousands of audiences and fans. Some fans supported the specific teams and many of fans supported some specific players in these teams. When they want to observe the performance of their preferred basketball stars and determine whether they can be awarded as the most valued player in the current season. Our study can help answer this question. We developed a novel NBA MVP prediction system with the neural network. We trained and tested this neural network using each season performances of NBA players from 1997 to 2019. These features of inputs are specific and optimized with training results. Based on our model, we randomly chose testing dataset from season 2009-2010 and season 2016-2017, and successfully predicted that the most valued players of the chosen seasons are LeBron James(season 2009-2010) and Russell Westbrook(season 2016-2017).

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NBA最有价值球员评选预测的神经网络模型

本研究分析了NBA球员在特定赛季的所有表现，并预测了该赛季最有价值球员(MVP)。NBA比赛是世界上最受欢迎的篮球比赛。每场比赛都吸引了成千上万的观众和球迷。一些球迷支持特定的球队，许多球迷支持这些球队中的某些特定球员。当他们想要观察他们喜欢的篮球明星的表现，并决定他们是否可以被授予本赛季最有价值的球员。我们的研究可以帮助回答这个问题。我们开发了一种基于神经网络的NBA MVP预测系统。我们使用NBA球员从1997年到2019年每个赛季的表现来训练和测试这个神经网络。输入的这些特征是特定的，并根据训练结果进行优化。基于我们的模型，我们随机选择了2009-2010赛季和2016-2017赛季的测试数据集，并成功预测了所选赛季最有价值的球员是勒布朗·詹姆斯(2009-2010赛季)和拉塞尔·威斯布鲁克(2016-2017赛季)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence

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