A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques
Previous studies have shown that variation in PlayerLoad (PL) could be used to detect fatigue in soccer players. Machine learning techniques (ML) were used to develop a new locomotor efficiency index (LEI) based on the prediction of PL. Sixty-four elite soccer players were monitored during an entire...
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/15/8808 |
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| author | Mauro Mandorino Antonio Tessitore Cédric Leduc Valerio Persichetti Manuel Morabito Mathieu Lacome |
| author_facet | Mauro Mandorino Antonio Tessitore Cédric Leduc Valerio Persichetti Manuel Morabito Mathieu Lacome |
| author_sort | Mauro Mandorino |
| collection | DOAJ |
| description | Previous studies have shown that variation in PlayerLoad (PL) could be used to detect fatigue in soccer players. Machine learning techniques (ML) were used to develop a new locomotor efficiency index (LEI) based on the prediction of PL. Sixty-four elite soccer players were monitored during an entire season. GPS systems were employed to collect external load data, which in turn were used to predict PL during training/matches. Random Forest Regression (RF) produced the best performance (mean absolute percentage error = 0.10 ± 0.01) and was included in further analyses. The difference between the PL value predicted by the ML model and the real one was calculated, individualized for each player using a z-score transformation (LEI), and interpreted as a sign of fatigue (negative LEI) or neuromuscular readiness (positive LEI). A linear mixed model was used to analyze how LEI changed according to the period of the season, day of the week, and weekly load. Regarding seasonal variation, the lowest and highest LEI values were recorded at the beginning of the season and in the middle of the season, respectively. On a weekly basis, our results showed lower values on match day − 2, while high weekly training loads were associated with a reduction in LEI. |
| first_indexed | 2024-03-11T00:31:05Z |
| format | Article |
| id | doaj.art-2a3ece3612aa4a35afe83ccffc33ed62 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:31:05Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2a3ece3612aa4a35afe83ccffc33ed622023-11-18T22:37:47ZengMDPI AGApplied Sciences2076-34172023-07-011315880810.3390/app13158808A New Approach to Quantify Soccer Players’ Readiness through Machine Learning TechniquesMauro Mandorino0Antonio Tessitore1Cédric Leduc2Valerio Persichetti3Manuel Morabito4Mathieu Lacome5Performance and Analytics Department, Parma Calcio 1913, 43121 Parma, ItalyDepartment of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, ItalyCarnegie Applied Rugby Research (CARR) Center, Institute for Sport, Physical Activity and Leisure, Carnegie School of Sport, Leeds Beckett University, Leeds LS6 3QS, UKPerformance and Analytics Department, Parma Calcio 1913, 43121 Parma, ItalyPerformance and Analytics Department, Parma Calcio 1913, 43121 Parma, ItalyPerformance and Analytics Department, Parma Calcio 1913, 43121 Parma, ItalyPrevious studies have shown that variation in PlayerLoad (PL) could be used to detect fatigue in soccer players. Machine learning techniques (ML) were used to develop a new locomotor efficiency index (LEI) based on the prediction of PL. Sixty-four elite soccer players were monitored during an entire season. GPS systems were employed to collect external load data, which in turn were used to predict PL during training/matches. Random Forest Regression (RF) produced the best performance (mean absolute percentage error = 0.10 ± 0.01) and was included in further analyses. The difference between the PL value predicted by the ML model and the real one was calculated, individualized for each player using a z-score transformation (LEI), and interpreted as a sign of fatigue (negative LEI) or neuromuscular readiness (positive LEI). A linear mixed model was used to analyze how LEI changed according to the period of the season, day of the week, and weekly load. Regarding seasonal variation, the lowest and highest LEI values were recorded at the beginning of the season and in the middle of the season, respectively. On a weekly basis, our results showed lower values on match day − 2, while high weekly training loads were associated with a reduction in LEI.https://www.mdpi.com/2076-3417/13/15/8808soccerfatiguemachine learningtraining loadPlayerLoad |
| spellingShingle | Mauro Mandorino Antonio Tessitore Cédric Leduc Valerio Persichetti Manuel Morabito Mathieu Lacome A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques Applied Sciences soccer fatigue machine learning training load PlayerLoad |
| title | A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques |
| title_full | A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques |
| title_fullStr | A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques |
| title_full_unstemmed | A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques |
| title_short | A New Approach to Quantify Soccer Players’ Readiness through Machine Learning Techniques |
| title_sort | new approach to quantify soccer players readiness through machine learning techniques |
| topic | soccer fatigue machine learning training load PlayerLoad |
| url | https://www.mdpi.com/2076-3417/13/15/8808 |
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