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This study employs Bayesian methodologies to explore the influence of playeror positional factors in predicting the probability of a shot resulting in agoal, measured by the expected goals (xG) metric. Utilising publicly availabledata from StatsBomb, Bayesian hierarchical logistic regressions areconstructed, analysing approximately 10,000 shots from the English PremierLeague to ascertain whether positional or player-level effects impact xG. Thefindings reveal positional effects in a basic model that includes only distanceto goal and shot angle as predictors, highlighting that strikers and attackingmidfielders exhibit a higher likelihood of scoring. However, these effectsdiminish when more informative predictors are introduced. Nevertheless, evenwith additional predictors, player-level effects persist, indicating thatcertain players possess notable positive or negative xG adjustments,influencing their likelihood of scoring a given chance. The study extends itsanalysis to data from Spain's La Liga and Germany's Bundesliga, yieldingcomparable results. Additionally, the paper assesses the impact of priordistribution choices on outcomes, concluding that the priors employed in themodels provide sound results but could be refined to enhance samplingefficiency for constructing more complex and extensive models feasibly.

Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach