@article{ref1,
title="Machine learning for understanding and predicting injuries in football",
journal="Sports medicine open",
year="2022",
author="Majumdar, Aritra and Bakirov, Rashid and Hodges, Dan and Scott, Suzanne and Rees, Tim",
volume="8",
number="1",
pages="e73-e73",
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.  Key Points Football injuries can lead to extended periods of absence from competition, with associated impacts on team performance, as well as financial implications. The relationship between training load and injuries is now a key research and applied focus, but current models and statistical approaches to data analysis fail to sufficiently capture the nuances of this relationship.  The application of machine learning to the training load and injury relationship is a new but fast growing research area, but there is a lack of consensus regarding which variables to consider for analysis, let alone those subsequently proving to be key in predicting players’ injuries, making it difficult at this time to draw on those studies when choosing which training load variables upon which to focus.  Although questions remain as to the current utility of machine learning for real-world application, the use of machine learning has great potential to unearth new insights into the workload and injury relationship, if research is expanded to examine multiple seasons’ data, accounts for data imbalance, and uses explainable artificial intelligence.  Keywords: Soccer <p /> <p>Language: en</p>",
language="en",
issn="2199-1170",
doi="10.1186/s40798-022-00465-4",
url="http://dx.doi.org/10.1186/s40798-022-00465-4"
}