Machine Learning Applications in Baseball: A Systematic Literature Review
Statistical analysis of baseball has long been popular, albeit only in limited capacity until relatively recently. In particular, analysts can now apply machine learning algorithms to large baseball data sets to derive meaningful insights into player and team performance. In the interest of stimulat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-11-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2018.1442991 |
| _version_ | 1797684894079385600 |
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| author | Kaan Koseler Matthew Stephan |
| author_facet | Kaan Koseler Matthew Stephan |
| author_sort | Kaan Koseler |
| collection | DOAJ |
| description | Statistical analysis of baseball has long been popular, albeit only in limited capacity until relatively recently. In particular, analysts can now apply machine learning algorithms to large baseball data sets to derive meaningful insights into player and team performance. In the interest of stimulating new research and serving as a go-to resource for academic and industrial analysts, we perform a systematic literature review of machine learning applications in baseball analytics. The approaches employed in literature fall mainly under three problem class umbrellas: Regression, Binary Classification, and Multiclass Classification. We categorize these approaches, provide our insights on possible future applications, and conclude with a summary of our findings. We find two algorithms dominate the literature: (1) Support Vector Machines for classification problems and (2) k-nearest neighbors for both classification and Regression problems. We postulate that recent proliferation of neural networks in general machine learning research will soon carry over into baseball analytics. |
| first_indexed | 2024-03-12T00:37:19Z |
| format | Article |
| id | doaj.art-98f2c7684918468b964164dae7783772 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:37:19Z |
| publishDate | 2017-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-98f2c7684918468b964164dae77837722023-09-15T09:33:56ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452017-11-01319-1074576310.1080/08839514.2018.14429911442991Machine Learning Applications in Baseball: A Systematic Literature ReviewKaan Koseler0Matthew Stephan1Miami UniversityMiami UniversityStatistical analysis of baseball has long been popular, albeit only in limited capacity until relatively recently. In particular, analysts can now apply machine learning algorithms to large baseball data sets to derive meaningful insights into player and team performance. In the interest of stimulating new research and serving as a go-to resource for academic and industrial analysts, we perform a systematic literature review of machine learning applications in baseball analytics. The approaches employed in literature fall mainly under three problem class umbrellas: Regression, Binary Classification, and Multiclass Classification. We categorize these approaches, provide our insights on possible future applications, and conclude with a summary of our findings. We find two algorithms dominate the literature: (1) Support Vector Machines for classification problems and (2) k-nearest neighbors for both classification and Regression problems. We postulate that recent proliferation of neural networks in general machine learning research will soon carry over into baseball analytics.http://dx.doi.org/10.1080/08839514.2018.1442991 |
| spellingShingle | Kaan Koseler Matthew Stephan Machine Learning Applications in Baseball: A Systematic Literature Review Applied Artificial Intelligence |
| title | Machine Learning Applications in Baseball: A Systematic Literature Review |
| title_full | Machine Learning Applications in Baseball: A Systematic Literature Review |
| title_fullStr | Machine Learning Applications in Baseball: A Systematic Literature Review |
| title_full_unstemmed | Machine Learning Applications in Baseball: A Systematic Literature Review |
| title_short | Machine Learning Applications in Baseball: A Systematic Literature Review |
| title_sort | machine learning applications in baseball a systematic literature review |
| url | http://dx.doi.org/10.1080/08839514.2018.1442991 |
| work_keys_str_mv | AT kaankoseler machinelearningapplicationsinbaseballasystematicliteraturereview AT matthewstephan machinelearningapplicationsinbaseballasystematicliteraturereview |