用知识图谱提升NBA球员薪资预测准确率
The Value of Graph-based Encoding in NBA Salary Prediction
- 构建球员场内场外数据的知识图谱并嵌入向量空间
- 加入图嵌入向量后模型对高薪球员预测更准
- 适合关注体育经济与图神经网络的研究者
职业运动员的市场估值因表现和地理位置的年度波动而复杂。在NBA中，传统方法是基于前一年表现的表格数据，用监督学习预测薪资。该方法对新秀有效，但对老将或高薪球员效果不佳。本文提出构建融合场上场下数据的知识图谱，通过图嵌入将其转化为向量，并融入表格数据。实验比较多种图嵌入算法，证明该流程显著提升薪资预测能力，尤其对高尾部薪资球员更为关键。
原文摘要 · Abstract (English)
Market valuations for professional athletes is a difficult problem, given the amount of variability in performance and location from year to year. In the National Basketball Association (NBA), a straightforward way to address this problem is to build a tabular data set and use supervised machine learning to predict a player's salary based on the player's performance in the previous year. For younger players, whose contracts are mostly built on draft position, this approach works well, however it can fail for veterans or those whose salaries are on the high tail of the distribution. In this paper, we show that building a knowledge graph with on and off court data, embedding that graph in a vector space, and including that vector in the tabular data allows the supervised learning to better understand the landscape of factors that affect salary. We compare several graph embedding algorithms and show that such a process is vital to NBA salary prediction.
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