A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives
Abstract Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-lev...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-51658-8 |
| _version_ | 1797355848354234368 |
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| author | Christopher B. Taber Srishti Sharma Mehul S. Raval Samah Senbel Allison Keefe Jui Shah Emma Patterson Julie Nolan N. Sertac Artan Tolga Kaya |
| author_facet | Christopher B. Taber Srishti Sharma Mehul S. Raval Samah Senbel Allison Keefe Jui Shah Emma Patterson Julie Nolan N. Sertac Artan Tolga Kaya |
| author_sort | Christopher B. Taber |
| collection | DOAJ |
| description | Abstract Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-level performances in Division-1 Women's basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score (GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep, and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. We used data balancing techniques and an Extreme Gradient Boosting (XGB) classifier to predict RSI and GS with greater than 90% accuracy and a 0.9 F1 score. The XGB regressor predicted PER with an MSE of 0.026 and an R2 of 0.680. Ensemble of Random Forest, XGB, and correlation finds feature importance at all levels. We used Partial Dependence Plots to understand the impact of each feature on the target variable. Quantifying and predicting performance at all levels will allow coaches to monitor athlete readiness and help improve training. |
| first_indexed | 2024-03-08T14:17:04Z |
| format | Article |
| id | doaj.art-47ff56f8f99c4c968348445eef903a6c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T14:17:04Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-47ff56f8f99c4c968348445eef903a6c2024-01-14T12:18:09ZengNature PortfolioScientific Reports2045-23222024-01-0114111010.1038/s41598-024-51658-8A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectivesChristopher B. Taber0Srishti Sharma1Mehul S. Raval2Samah Senbel3Allison Keefe4Jui Shah5Emma Patterson6Julie Nolan7N. Sertac Artan8Tolga Kaya9Department of Physical Therapy and Human Movement Science, Sacred Heart UniversitySchool of Engineering and Applied Science, Ahmedabad UniversitySchool of Engineering and Applied Science, Ahmedabad UniversitySchool of Computer Science and Engineering, Sacred Heart UniversityDepartment of Physical Therapy and Human Movement Science, Sacred Heart UniversityDepartment of Physical Therapy and Human Movement Science, Sacred Heart UniversityDepartment of Physical Therapy and Human Movement Science, Sacred Heart UniversityDepartment of Physical Therapy and Human Movement Science, Sacred Heart UniversityCollege of Engineering and Computing Sciences, New York Institute of TechnologySchool of Computer Science and Engineering, Sacred Heart UniversityAbstract Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-level performances in Division-1 Women's basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score (GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep, and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. We used data balancing techniques and an Extreme Gradient Boosting (XGB) classifier to predict RSI and GS with greater than 90% accuracy and a 0.9 F1 score. The XGB regressor predicted PER with an MSE of 0.026 and an R2 of 0.680. Ensemble of Random Forest, XGB, and correlation finds feature importance at all levels. We used Partial Dependence Plots to understand the impact of each feature on the target variable. Quantifying and predicting performance at all levels will allow coaches to monitor athlete readiness and help improve training.https://doi.org/10.1038/s41598-024-51658-8 |
| spellingShingle | Christopher B. Taber Srishti Sharma Mehul S. Raval Samah Senbel Allison Keefe Jui Shah Emma Patterson Julie Nolan N. Sertac Artan Tolga Kaya A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives Scientific Reports |
| title | A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives |
| title_full | A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives |
| title_fullStr | A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives |
| title_full_unstemmed | A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives |
| title_short | A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives |
| title_sort | holistic approach to performance prediction in collegiate athletics player team and conference perspectives |
| url | https://doi.org/10.1038/s41598-024-51658-8 |
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