基于机器学习的NBA球员工资分摊模型
Estimating NBA players salary share according to their performance on court: A machine learning approach
摘要：
研究者和实践者习惯于拟合线性模型，以便根据球员在球场上的表现来预测NBA球员的工资。相反，我们首先选择最重要的决定因素或统计数据（在联赛中的经验，比赛等）来关注球员的工资份额（关于球队的工资）。然后利用它们通过采用非线性随机森林机器学习算法来预测球员薪水。我们从外部评估我们的工资预测，从而避免了大多数论文中观察到的过度拟合现象。总体而言，使用2017-2019年三个不同时期的数据，我们确定了实现非常令人满意的工资预测的重要因素，并得出了有用的结论。
英文摘要：
It is customary for researchers and practitioners to fit linear models in order to predict NBA player's salary based on the players' performance on court. On the contrary, we focus on the players salary share (with regards to the team payroll) by first selecting the most important determinants or statistics (years of experience in the league, games played, etc.) and then utilise them to predict the player salaries by employing a non linear Random Forest machine learning algorithm. We externally evaluate our salary predictions, thus we avoid the phenomenon of over-fitting observed in most papers. Overall, using data from three distinct periods, 2017-2019 we identify the important factors that achieve very satisfactory salary predictions and we draw useful conclusions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
