Machine Learning for Understanding and Predicting Injuries in Football
Authors: Majumdar, A., Bakirov, R., Hodges, D., Scott, S. and Rees, T.
Journal: Sports Medicine - Open
Volume: 8
Issue: 1
eISSN: 2198-9761
ISSN: 2199-1170
DOI: 10.1186/s40798-022-00465-4
Abstract:Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment—such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.
https://eprints.bournemouth.ac.uk/36943/
Source: Scopus
Machine Learning for Understanding and Predicting Injuries in Football.
Authors: Majumdar, A., Bakirov, R., Hodges, D., Scott, S. and Rees, T.
Journal: Sports Med Open
Volume: 8
Issue: 1
Pages: 73
ISSN: 2199-1170
DOI: 10.1186/s40798-022-00465-4
Abstract:Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment-such as response to data imbalance, model fitting, and a lack of multi-season data-limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.
https://eprints.bournemouth.ac.uk/36943/
Source: PubMed
Machine Learning for Understanding and Predicting Injuries in Football
Authors: Majumdar, A., Bakirov, R., Hodges, D., Scott, S. and Rees, T.
Journal: SPORTS MEDICINE-OPEN
Volume: 8
Issue: 1
eISSN: 2198-9761
ISSN: 2199-1170
DOI: 10.1186/s40798-022-00465-4
https://eprints.bournemouth.ac.uk/36943/
Source: Web of Science (Lite)
Machine learning for understanding and predicting injuries in soccer
Authors: Majumdar, A., Bakirov, R., Hodges, D., Scott, S. and Rees, T.
Journal: Sports Medicine - Open
Publisher: Springer (part of Springer Nature)
ISSN: 2198-9761
https://eprints.bournemouth.ac.uk/36943/
Source: Manual
Machine Learning for Understanding and Predicting Injuries in Football.
Authors: Majumdar, A., Bakirov, R., Hodges, D., Scott, S. and Rees, T.
Journal: Sports medicine - open
Volume: 8
Issue: 1
Pages: 73
eISSN: 2198-9761
ISSN: 2199-1170
DOI: 10.1186/s40798-022-00465-4
Abstract:Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment-such as response to data imbalance, model fitting, and a lack of multi-season data-limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.
https://eprints.bournemouth.ac.uk/36943/
Source: Europe PubMed Central
Machine learning for understanding and predicting injuries in soccer
Authors: Majumdar, A., Bakirov, R., Hodges, D., Scott, S. and Rees, T.
Journal: Sports Medicine - Open
Volume: 8
Issue: 1
Publisher: Springer (part of Springer Nature)
ISSN: 2198-9761
Abstract:Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimizing the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship.
In this article, we review this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment— such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.
https://eprints.bournemouth.ac.uk/36943/
Source: BURO EPrints