Using Social Networks to Improve Group Transition Prediction in Professional Sports

TL;DR

This paper demonstrates that social network data significantly enhances the prediction of player team transitions in MLB and NBA, outperforming models based solely on performance and team fitness.

Contribution

It introduces a novel approach integrating social data into machine learning models to improve transition prediction accuracy in professional sports.

Findings

Social data improves transition prediction accuracy

Player performance and team fitness contribute to predictions

Social relationships have a large impact on player transitions

Abstract

We examine whether social data can be used to predict how members of Major League Baseball (MLB) and members of the National Basketball Association (NBA) transition between teams during their career. We find that incorporating social data into various machine learning algorithms substantially improves the algorithms' ability to correctly determine these transitions. In particular, we measure how player performance, team fitness, and social data individually and collectively contribute to predicting these transitions. Incorporating individual performance and team fitness both improve the predictive accuracy of our algorithms. However, this improvement is dwarfed by the improvement seen when we include social data suggesting that social relationships have a comparatively large effect on player transitions in both MLB and in the NBA.