Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football
Abstract Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player trac...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85993-1 |
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| _version_ | 1832594768297721856 |
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| author | Quang Nguyen Ruitong Jiang Meg Ellingwood Ronald Yurko |
| author_facet | Quang Nguyen Ruitong Jiang Meg Ellingwood Ronald Yurko |
| author_sort | Quang Nguyen |
| collection | DOAJ |
| description | Abstract Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player tracking data, we present a novel framework for assessing tackling contribution in a continuous and objective manner. Our approach first identifies when a defender is in a “contact window” of the ball-carrier during a play, before assigning value to each window and the players involved. This enables us to devise a new metric called fractional tackles, which credits defenders for halting the ball-carrier’s forward motion toward the end zone. We demonstrate that fractional tackles overcome the shortcomings of traditional metrics such as tackles and assists, by providing greater variation and measurable information for players lacking recorded statistics like defensive linemen. We view our contribution as a significant step forward in measuring defensive performance in American football and a clear demonstration of the capabilities of player tracking data. |
| format | Article |
| id | doaj-art-714b3656be4c4ac2a47811501914e299 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-714b3656be4c4ac2a47811501914e2992025-01-19T12:19:51ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-85993-1Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American footballQuang Nguyen0Ruitong Jiang1Meg Ellingwood2Ronald Yurko3Department of Statistics & Data Science, Carnegie Mellon UniversityNeuroscience Institute and Center for the Neural Basis of Cognition, Carnegie Mellon UniversityDepartment of Statistics & Data Science, Carnegie Mellon UniversityDepartment of Statistics & Data Science, Carnegie Mellon UniversityAbstract Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player tracking data, we present a novel framework for assessing tackling contribution in a continuous and objective manner. Our approach first identifies when a defender is in a “contact window” of the ball-carrier during a play, before assigning value to each window and the players involved. This enables us to devise a new metric called fractional tackles, which credits defenders for halting the ball-carrier’s forward motion toward the end zone. We demonstrate that fractional tackles overcome the shortcomings of traditional metrics such as tackles and assists, by providing greater variation and measurable information for players lacking recorded statistics like defensive linemen. We view our contribution as a significant step forward in measuring defensive performance in American football and a clear demonstration of the capabilities of player tracking data.https://doi.org/10.1038/s41598-025-85993-1American footballCorrelationPerformance metricTracking data |
| spellingShingle | Quang Nguyen Ruitong Jiang Meg Ellingwood Ronald Yurko Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football Scientific Reports American football Correlation Performance metric Tracking data |
| title | Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football |
| title_full | Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football |
| title_fullStr | Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football |
| title_full_unstemmed | Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football |
| title_short | Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football |
| title_sort | fractional tackles leveraging player tracking data for within play tackling evaluation in american football |
| topic | American football Correlation Performance metric Tracking data |
| url | https://doi.org/10.1038/s41598-025-85993-1 |
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