Using social networks to improve team transition prediction in professional sports.
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 improv...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0268619 |
| _version_ | 1818048352194920448 |
|---|---|
| author | Emily J Evans Rebecca Jones Joseph Leung Benjamin Z Webb |
| author_facet | Emily J Evans Rebecca Jones Joseph Leung Benjamin Z Webb |
| author_sort | Emily J Evans |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T10:20:19Z |
| format | Article |
| id | doaj.art-814ef861858b4357b2ece68ef1cfb9a2 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T10:20:19Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-814ef861858b4357b2ece68ef1cfb9a22022-12-22T01:52:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01176e026861910.1371/journal.pone.0268619Using social networks to improve team transition prediction in professional sports.Emily J EvansRebecca JonesJoseph LeungBenjamin Z WebbWe 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.https://doi.org/10.1371/journal.pone.0268619 |
| spellingShingle | Emily J Evans Rebecca Jones Joseph Leung Benjamin Z Webb Using social networks to improve team transition prediction in professional sports. PLoS ONE |
| title | Using social networks to improve team transition prediction in professional sports. |
| title_full | Using social networks to improve team transition prediction in professional sports. |
| title_fullStr | Using social networks to improve team transition prediction in professional sports. |
| title_full_unstemmed | Using social networks to improve team transition prediction in professional sports. |
| title_short | Using social networks to improve team transition prediction in professional sports. |
| title_sort | using social networks to improve team transition prediction in professional sports |
| url | https://doi.org/10.1371/journal.pone.0268619 |
| work_keys_str_mv | AT emilyjevans usingsocialnetworkstoimproveteamtransitionpredictioninprofessionalsports AT rebeccajones usingsocialnetworkstoimproveteamtransitionpredictioninprofessionalsports AT josephleung usingsocialnetworkstoimproveteamtransitionpredictioninprofessionalsports AT benjaminzwebb usingsocialnetworkstoimproveteamtransitionpredictioninprofessionalsports |