Can Referee Psychological Factors Predict Performance? A Machine Learning Approach to Basketball Classification Referees

Can Referee Psychological Factors Predict Performance? A Machine Learning Approach to Basketball Classification Referees | 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 Article Can Referee Psychological Factors Predict Performance? A Machine Learning Approach to Basketball Classification Referees Aydın Karaçam, Ahmet Kurtoğlu, Bekir Erhan Orhan, Bekir Çar, Niyazi Sıdkı Adıgüzel, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6597081/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Referees are important in ensuring justice, continuity, and integrity in sporting events, with their performance shaped by both technical and physical skills as well as psychological characteristics. This research examines how self-efficacy, emotion management, decision-making style, and physical self-esteem impact performance among top basketball referees. Data were gathered from 58 Class-B referees within the Turkish Basketball Federation during the 2024–2025 season via standardized psychometric instruments in conjunction with official end-of-season performance metrics. A systematic machine learning methodology was used, including three experimental stages: feature selection using p-values, correlation analysis, and dimensionality reduction using Principal Component Analysis (PCA). The model, including characteristics selected through correlation analysis, had superior performance, achieving a Mean Squared Error (MSE) of 3.930 and an R² value of 0.879, indicating a robust predictive link between psychological factors and referee performance. These results highlight the predictive significance of psychological preparedness, namely self-efficacy and emotion management, and illustrate the utility of data-driven feature selection in enhancing model accuracy. This study proposes the incorporation of psychological evaluation in referee development initiatives and underscores the promise of machine learning in improving talent discovery and performance assessment in sports officiating. Biological sciences/Psychology Health sciences/Health care Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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This research examines how self-efficacy, emotion management, decision-making style, and physical self-esteem impact performance among top basketball referees. Data were gathered from 58 Class-B referees within the Turkish Basketball Federation during the 2024\u0026ndash;2025 season via standardized psychometric instruments in conjunction with official end-of-season performance metrics. A systematic machine learning methodology was used, including three experimental stages: feature selection using p-values, correlation analysis, and dimensionality reduction using Principal Component Analysis (PCA). The model, including characteristics selected through correlation analysis, had superior performance, achieving a Mean Squared Error (MSE) of 3.930 and an R\u0026sup2; value of 0.879, indicating a robust predictive link between psychological factors and referee performance. 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