The Value of Graph-based Encoding in NBA Salary Prediction

TL;DR

This paper demonstrates that incorporating graph-based embeddings of on and off-court data significantly improves NBA salary prediction accuracy over traditional tabular methods.

Contribution

It introduces a novel approach of using knowledge graph embeddings to enhance machine learning models for athlete salary prediction.

Findings

Graph embeddings improve prediction accuracy.

Different embedding algorithms were compared and evaluated.

Graph-based features outperform traditional tabular data alone.

Abstract

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.