论文标题
根据他们在法庭上的表现：一种机器学习方法，估计NBA球员的薪水份额
Estimating NBA players salary share according to their performance on court: A machine learning approach
论文作者
论文摘要
习惯研究人员和从业人员适合线性模型，以根据球员在球场上的表现来预测NBA球员的薪水。相反，我们首先选择最重要的决定因素或统计数据（在联盟中的多年经验，比赛等），然后利用它们来通过使用非线性随机森林机器学习算法来预测玩家的薪水，从而关注球员的薪水份额（关于团队薪资）。我们在外部评估我们的工资预测，因此避免了大多数论文中观察到的过度拟合的现象。总体而言，使用三个不同时期的数据，2017 - 2019年，我们确定了实现非常令人满意的工资预测的重要因素，并得出有用的结论。
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.
