AI Article Synopsis
- The study investigates the effectiveness of social data in predicting team transitions for NBA and MLB players.
- Results show that social data greatly enhances prediction accuracy for NBA transitions but has minimal impact for MLB transitions.
- While performance and team fitness data slightly help in predictions, social data significantly aids in accurately inferring past transitions in both leagues.
Article 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 the NBA but only marginally in MLB. We also measure the extent to which player performance and team fitness data can be used to predict transitions between teams. This data, however, only slightly improves our predictions for players for both basketball and baseball players. We also consider whether social, performance, and team fitness data can be used to infer past transitions. Here we find that social data significantly improves our inference accuracy in both the NBA and MLB but player performance and team fitness data again does little to improve this score.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231699 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268619 | PLOS |
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