Building reproducible expected‑goals models from public football event data: Logistic and mixed-effects analysis using StatsBomb open data

Building reproducible expected‑goals models from public football event data: Logistic and mixed-effects analysis using StatsBomb open data | 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 Building reproducible expected‑goals models from public football event data: Logistic and mixed-effects analysis using StatsBomb open data Kofi Nyantakyi Appiah, Nathanael Adu, Divyanshu Kumar Singh, Edward Edem Nartey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9022775/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 A reproducible expected-goals (xG) modelling pipeline is developed and evaluated using large public football event datasets. The analysis uses 10,709 non-penalty shots from La Liga 2015/2016 and the 2018 FIFA World Cup in StatsBomb Open Data, with predictors derived from event locations (shot distance and angle), body part (head vs. foot), and competition indicators. Logistic regression estimates goal probability from these predictors, and a generalized linear mixed-effects model adds shooter-level random intercepts to capture between-player variability. Model performance is assessed using information criteria and area under the ROC curve (AUC). Distance strongly reduces scoring probability, headers are less likely to be scored than footed shots, and World Cup shots have lower baseline conversion than La Liga attempts at comparable locations. AUC increases from 0.75 in the baseline model to 0.78 in the fixed-effects model and 0.79 in the mixed-effects model, indicating that open event data support transparent, statistically defensible, and practically useful xG pipelines for research and teaching. Analysis Applied Statistics Expected goals Football analytics Public event data Logistic regression Mixed-effects models StatsBomb Open Data Full Text Additional Declarations The authors declare no competing interests. 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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