The Value of Graph-based Encoding in NBA Salary Prediction

2026-03-05

AI Summary

This study addresses the limitations of conventional tabular data approaches in accurately predicting salaries for veteran and high-earning NBA players by systematically introducing graph embedding techniques. The authors construct an NBA knowledge graph that integrates on-court performance metrics with off-court contextual information. Player embeddings are generated using algorithms such as Node2Vec and GraphSAGE, then combined with structured features as input to supervised learning models, thereby effectively capturing complex relational patterns and contextual semantics among players. Experimental results demonstrate that this approach significantly improves overall salary prediction accuracy, with particularly pronounced gains for high-salary and veteran players, thus overcoming key shortcomings of purely tabular modeling paradigms.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• NBA salary prediction
• market valuation
• player performance
• graph-based encoding
• supervised learning

Innovation

• graph-based encoding
• knowledge graph
• graph embedding
• NBA salary prediction
• supervised machine learning