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Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study
Positional tracking data allows football practitioners to derive features that describe patterns of player behavior and quantify performance. Existing research using tracking data has mostly focused on what occurred on the pitch, such as the determinants of effective passing. There have yet to be st...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597494/ https://www.ncbi.nlm.nih.gov/pubmed/36311212 http://dx.doi.org/10.3389/fspor.2022.1019990 |
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author | Herold, Mat Kempe, Matthias Ruf, Ludwig Guevara, Luis Meyer, Tim |
author_facet | Herold, Mat Kempe, Matthias Ruf, Ludwig Guevara, Luis Meyer, Tim |
author_sort | Herold, Mat |
collection | PubMed |
description | Positional tracking data allows football practitioners to derive features that describe patterns of player behavior and quantify performance. Existing research using tracking data has mostly focused on what occurred on the pitch, such as the determinants of effective passing. There have yet to be studies attempting to use findings from data science to improve performance. Therefore, 24 professional players (mean age = 21.6 years, SD = 5.7) were divided into a control team and an intervention team which competed against each other in a pre-test match. Metrics were gathered via notational analysis (number of passes, penalty box entries, shots on goal), and positional tracking data including pass length, pass velocity, defensive disruption (D-Def), and the number of outplayed opponents (NOO). D-Def and NOO were used to extract video clips from the pre-test that were shown to the intervention team as a teaching tool for 2 weeks prior to the post-test match. The results in the post-test showed no significant improvements from the pre-test between the Intervention Team and the Control Team for D-Def (F = 1.100, p = 0.308, η(2) = 0.058) or NOO (F = 0.347, p = 0.563, η(2) = 0.019). However, the Intervention Team made greater numerical increases for number of passes, penalty box entries, and shots on goal in the post-test match. Despite a positive tendency from the intervention, results indicate the transfer of knowledge from data science to performance was lacking. Future studies should aim to include coaches' input and use the metrics to design training exercises that encourage the desired behavior. |
format | Online Article Text |
id | pubmed-9597494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95974942022-10-27 Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study Herold, Mat Kempe, Matthias Ruf, Ludwig Guevara, Luis Meyer, Tim Front Sports Act Living Sports and Active Living Positional tracking data allows football practitioners to derive features that describe patterns of player behavior and quantify performance. Existing research using tracking data has mostly focused on what occurred on the pitch, such as the determinants of effective passing. There have yet to be studies attempting to use findings from data science to improve performance. Therefore, 24 professional players (mean age = 21.6 years, SD = 5.7) were divided into a control team and an intervention team which competed against each other in a pre-test match. Metrics were gathered via notational analysis (number of passes, penalty box entries, shots on goal), and positional tracking data including pass length, pass velocity, defensive disruption (D-Def), and the number of outplayed opponents (NOO). D-Def and NOO were used to extract video clips from the pre-test that were shown to the intervention team as a teaching tool for 2 weeks prior to the post-test match. The results in the post-test showed no significant improvements from the pre-test between the Intervention Team and the Control Team for D-Def (F = 1.100, p = 0.308, η(2) = 0.058) or NOO (F = 0.347, p = 0.563, η(2) = 0.019). However, the Intervention Team made greater numerical increases for number of passes, penalty box entries, and shots on goal in the post-test match. Despite a positive tendency from the intervention, results indicate the transfer of knowledge from data science to performance was lacking. Future studies should aim to include coaches' input and use the metrics to design training exercises that encourage the desired behavior. Frontiers Media S.A. 2022-10-12 /pmc/articles/PMC9597494/ /pubmed/36311212 http://dx.doi.org/10.3389/fspor.2022.1019990 Text en Copyright © 2022 Herold, Kempe, Ruf, Guevara and Meyer. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Sports and Active Living Herold, Mat Kempe, Matthias Ruf, Ludwig Guevara, Luis Meyer, Tim Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study |
title | Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study |
title_full | Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study |
title_fullStr | Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study |
title_full_unstemmed | Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study |
title_short | Shortcomings of applying data science to improve professional football performance: Takeaways from a pilot intervention study |
title_sort | shortcomings of applying data science to improve professional football performance: takeaways from a pilot intervention study |
topic | Sports and Active Living |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597494/ https://www.ncbi.nlm.nih.gov/pubmed/36311212 http://dx.doi.org/10.3389/fspor.2022.1019990 |
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