What question did this study set out to answer?

This research aims to improve injury forecasting in professional soccer by using training data and machine learning. It seeks to uncover factors that influence injury risk.

July 25, 2018SHILAP Revista de lepidopterologíaOpen Access

Effective injury forecasting in soccer with GPS training data and machine learning

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Authors

ARAlessio RossiUniversity of Pisa LPLuca PappalardoIstituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo"PCPaolo CintiaUniversity of Pisa

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Implication

Randomized trial identifies injury risk factors in professional soccer, suggesting improved prevention strategies.

Key Points

This research aims to improve injury forecasting in professional soccer by using training data and machine learning. It seeks to uncover factors that influence injury risk.
Collected GPS training data from players in a professional soccer club over one season.
Developed a multi-dimensional injury forecaster using machine learning techniques.
Provided case studies illustrating the accuracy and interpretability of the forecaster.
The injury forecaster accurately predicts injury risks based on training workload.
Demonstrated strong correlations between specific training metrics and injury occurrences.
Provided actionable rules for coaches to assess training performance in relation to injury prevention.

Cite This Study

Rossi et al. (2018) studied this question.

synapsesocial.com/papers/69d837e9f4e559c61eae2f4c https://doi.org/10.1371/journal.pone.0201264

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Effective injury forecasting in soccer with GPS training data and machine learning

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Key Points

Cite This Study

Effective injury forecasting in soccer with GPS training data and machine learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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