IIM Bangalore - Indian Institute of Management

Abstract: Penalty shootouts in association football are sometimes criticized by fans and pundits as an imperfect tie-breaking procedure. In this study, we analyze through a Bayesian model if shootouts are governed more by skill or by chance. Using a representative dataset from twelve recent European seasons, we fit a hierarchical logistic model with appropriate random effects and a within–shootout latent autoregressive state to capture evolving pressure. The model is implemented through Hamiltonian Monte Carlo approach. Our proposed framework allows us to quantify the amount of skill involved in shootouts by the proportion of logit-scale variance attributable to persistent heterogeneity versus idiosyncratic and state noise. We also compare the full specification to nested alternatives via PSIS-LOO, stacking, and decision-oriented scores computed from leave-one-shootout-out predictive distributions. Empirically, persistent individual effects are found to be small: the posterior SkillShare is near zero in the full model, suggesting that shootouts are primarily chance dominated. As a by-product of our approach, we show how the proposed model can also be utilized to rank the shooters as well as to optimize penalty-taking orders. We also discuss a few alternative tie-breaking procedures as future recommendations which can be evaluated in a similar modeling framework.

Authors’ Names: Soudeep Deb Journal Name: Journal of Sports Analytics URL: https://journals.sagepub.com/doi/10.1177/22150218261444633

Abstract: Penalty shootouts in association football are sometimes criticized by fans and pundits as an imperfect tie-breaking procedure. In this study, we analyze through a Bayesian model if shootouts are governed more by skill or by chance. Using a representative dataset from twelve recent European seasons, we fit a hierarchical logistic model with appropriate random effects and a within–shootout latent autoregressive state to capture evolving pressure. The model is implemented through Hamiltonian Monte Carlo approach. Our proposed framework allows us to quantify the amount of skill involved in shootouts by the proportion of logit-scale variance attributable to persistent heterogeneity versus idiosyncratic and state noise. We also compare the full specification to nested alternatives via PSIS-LOO, stacking, and decision-oriented scores computed from leave-one-shootout-out predictive distributions. Empirically, persistent individual effects are found to be small: the posterior SkillShare is near zero in the full model, suggesting that shootouts are primarily chance dominated. As a by-product of our approach, we show how the proposed model can also be utilized to rank the shooters as well as to optimize penalty-taking orders. We also discuss a few alternative tie-breaking procedures as future recommendations which can be evaluated in a similar modeling framework.

Authors’ Names: Soudeep Deb Journal Name: Journal of Sports Analytics URL: https://journals.sagepub.com/doi/10.1177/22150218261444633