A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance
Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player’s performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relate...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Public Library of Science
2015
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| Online Access: | http://hdl.handle.net/1721.1/99883 https://orcid.org/0000-0003-0774-3563 |
| _version_ | 1826216772284448768 |
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| author | Skinner, Brian J Guy, Stephen J. |
| author2 | Massachusetts Institute of Technology. Research Laboratory of Electronics |
| author_facet | Massachusetts Institute of Technology. Research Laboratory of Electronics Skinner, Brian J Guy, Stephen J. |
| author_sort | Skinner, Brian J |
| collection | MIT |
| description | Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player’s performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players’ skills to the team’s success at running different plays, can be used to automatically learn players’ skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players’ respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player’s interactions with a given lineup based only on his performance with a different lineup. |
| first_indexed | 2024-09-23T16:52:52Z |
| format | Article |
| id | mit-1721.1/99883 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:52:52Z |
| publishDate | 2015 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/998832022-09-29T22:07:55Z A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance Skinner, Brian J Guy, Stephen J. Massachusetts Institute of Technology. Research Laboratory of Electronics Skinner, Brian J Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player’s performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players’ skills to the team’s success at running different plays, can be used to automatically learn players’ skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players’ respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player’s interactions with a given lineup based only on his performance with a different lineup. 2015-11-10T17:35:19Z 2015-11-10T17:35:19Z 2015-09 2013-09 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/99883 Skinner, Brian, and Stephen J. Guy. “A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.” Edited by Frank Emmert-Streib. PLoS ONE 10, no. 9 (September 9, 2015): e0136393. https://orcid.org/0000-0003-0774-3563 en_US http://dx.doi.org/10.1371/journal.pone.0136393 PLOS ONE Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science Public Library of Science |
| spellingShingle | Skinner, Brian J Guy, Stephen J. A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance |
| title | A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance |
| title_full | A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance |
| title_fullStr | A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance |
| title_full_unstemmed | A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance |
| title_short | A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance |
| title_sort | method for using player tracking data in basketball to learn player skills and predict team performance |
| url | http://hdl.handle.net/1721.1/99883 https://orcid.org/0000-0003-0774-3563 |
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