Forecasting extremes of football players' performance in matches.

Forecasting extremes of football players' performance in matches. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Forecasting extremes of football players' performance in matches. Michal Nowak, Bartosz Bok, Artur Wilczek, Mariusz Kamola, Łukasz Oleksy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4071433/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Nov, 2024 Read the published version in Scientific Reports → Version 2 posted 8 You are reading this latest preprint version Show more versions Abstract Based on training history, accurate predictive modelling of an athlete's performance in competition can be a cornerstone for prior optimal planning of exercise mix and intensity. While universal in many sports, such a goal is challenging in football due to the complexity of factors leading to the final score and the complexity of the preceding training process. We developed and tested a range of models, the best of which were to forecast selected play performance indices with an accuracy of 10-20%. Such score applies to models run on raw player location data and aggregating performance indices developed with expert knowledge in the football training domain. Results show that individual player models perform better than collective ones and that more recent training data are better predictors. While we consider the accuracy of the models still of limited reliability, their transparency and present quality make them useful in the daily planning of training activities that impact player performance in the coming match. Additionally, observations of training parameters generated in short-term intervals are more effective and correlated with extreme match results than long-term dates. Specific training parameters may be key in predicting exceptional football player performance, but they may also vary from person to person. Health sciences/Health care/Quality of life Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Nov, 2024 Read the published version in Scientific Reports → Version 2 posted Editorial decision: Revision requested 11 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviews received at journal 01 Oct, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers invited by journal 26 Sep, 2024 Submission checks completed at journal 25 Sep, 2024 First submitted to journal 23 Sep, 2024 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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