Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club Academy
Background: Global positioning system (GPS) based player movement tracking data are widely used by professional football (soccer) clubs and academies to provide insight into activity demands during training and competitive matches. However, the use of movement tracking data to inform the design of t...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Sports |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4663/6/4/130 |
| _version_ | 1798040426351951872 |
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| author | Varuna De Silva Mike Caine James Skinner Safak Dogan Ahmet Kondoz Tilson Peter Elliott Axtell Matt Birnie Ben Smith |
| author_facet | Varuna De Silva Mike Caine James Skinner Safak Dogan Ahmet Kondoz Tilson Peter Elliott Axtell Matt Birnie Ben Smith |
| author_sort | Varuna De Silva |
| collection | DOAJ |
| description | Background: Global positioning system (GPS) based player movement tracking data are widely used by professional football (soccer) clubs and academies to provide insight into activity demands during training and competitive matches. However, the use of movement tracking data to inform the design of training programmes is still an open research question. Objectives: The objective of this study is to analyse player tracking data to understand activity level differences between training and match sessions, with respect to different playing positions. Methods: This study analyses the per-session summary of historical movement data collected through GPS tracking to profile high-speed running activity as well as distance covered during training sessions as a whole and competitive matches. We utilise 20,913 data points collected from 53 football players aged between 18 and 23 at an elite football academy across four full seasons (2014⁻2018). Through ANOVA analysis and probability distribution analysis, we compare the activity demands, measured by the number of high-speed runs, the amount of high-speed distance, and distance covered by players in key playing positions, such as Central Midfielders, Full Backs, and Centre Forwards. Results and Implications: While there are significant positional differences in physical activity demands during competitive matches, the physical activity levels during training sessions do not show positional variations. In matches, the Centre Forwards face the highest demand for High Speed Runs (HSRs), compared to Central Midfielders and Full Backs. However, on average the Central Midfielders tend to cover more distance than Centre Forwards and Full Backs. An increase in high-speed work demand in matches and training over the past four seasons, also shown by a gradual change in the extreme values of high-speed running activity, was also found. This large-scale, longitudinal study makes an important contribution to the literature, providing novel insights from an elite performance environment about the relationship between player activity levels during training and match play, and how these vary by playing position. |
| first_indexed | 2024-04-11T22:07:27Z |
| format | Article |
| id | doaj.art-cf2cf1d5a9f14a138b666879d47a0d2f |
| institution | Directory Open Access Journal |
| issn | 2075-4663 |
| language | English |
| last_indexed | 2024-04-11T22:07:27Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sports |
| spelling | doaj.art-cf2cf1d5a9f14a138b666879d47a0d2f2022-12-22T04:00:39ZengMDPI AGSports2075-46632018-10-016413010.3390/sports6040130sports6040130Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club AcademyVaruna De Silva0Mike Caine1James Skinner2Safak Dogan3Ahmet Kondoz4Tilson Peter5Elliott Axtell6Matt Birnie7Ben Smith8Loughborough University London, Loughborough University, London E15 2GZ, UKLoughborough University London, Loughborough University, London E15 2GZ, UKLoughborough University London, Loughborough University, London E15 2GZ, UKLoughborough University London, Loughborough University, London E15 2GZ, UKLoughborough University London, Loughborough University, London E15 2GZ, UKChelsea Football Club Academy, Cobham KT11 3PT, UKChelsea Football Club Academy, Cobham KT11 3PT, UKChelsea Football Club Academy, Cobham KT11 3PT, UKChelsea Football Club Academy, Cobham KT11 3PT, UKBackground: Global positioning system (GPS) based player movement tracking data are widely used by professional football (soccer) clubs and academies to provide insight into activity demands during training and competitive matches. However, the use of movement tracking data to inform the design of training programmes is still an open research question. Objectives: The objective of this study is to analyse player tracking data to understand activity level differences between training and match sessions, with respect to different playing positions. Methods: This study analyses the per-session summary of historical movement data collected through GPS tracking to profile high-speed running activity as well as distance covered during training sessions as a whole and competitive matches. We utilise 20,913 data points collected from 53 football players aged between 18 and 23 at an elite football academy across four full seasons (2014⁻2018). Through ANOVA analysis and probability distribution analysis, we compare the activity demands, measured by the number of high-speed runs, the amount of high-speed distance, and distance covered by players in key playing positions, such as Central Midfielders, Full Backs, and Centre Forwards. Results and Implications: While there are significant positional differences in physical activity demands during competitive matches, the physical activity levels during training sessions do not show positional variations. In matches, the Centre Forwards face the highest demand for High Speed Runs (HSRs), compared to Central Midfielders and Full Backs. However, on average the Central Midfielders tend to cover more distance than Centre Forwards and Full Backs. An increase in high-speed work demand in matches and training over the past four seasons, also shown by a gradual change in the extreme values of high-speed running activity, was also found. This large-scale, longitudinal study makes an important contribution to the literature, providing novel insights from an elite performance environment about the relationship between player activity levels during training and match play, and how these vary by playing position.https://www.mdpi.com/2075-4663/6/4/130sports analyticsplayer trackingfootball (soccer) |
| spellingShingle | Varuna De Silva Mike Caine James Skinner Safak Dogan Ahmet Kondoz Tilson Peter Elliott Axtell Matt Birnie Ben Smith Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club Academy Sports sports analytics player tracking football (soccer) |
| title | Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club Academy |
| title_full | Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club Academy |
| title_fullStr | Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club Academy |
| title_full_unstemmed | Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club Academy |
| title_short | Player Tracking Data Analytics as a Tool for Physical Performance Management in Football: A Case Study from Chelsea Football Club Academy |
| title_sort | player tracking data analytics as a tool for physical performance management in football a case study from chelsea football club academy |
| topic | sports analytics player tracking football (soccer) |
| url | https://www.mdpi.com/2075-4663/6/4/130 |
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