Forecasting the opening goal in second-half of a football match: Bayesian and frequentist perspectives

Anirban Dutta (), Hemanta Saikia (), Jonali Gogoi () and Dibyojyoti Bhattacharjee ()
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Anirban Dutta: Assam University
Hemanta Saikia: College of Sericulture
Jonali Gogoi: Assam University
Dibyojyoti Bhattacharjee: Assam University

Computational Statistics, 2025, vol. 40, issue 4, No 14, 1959-1984

Abstract: Abstract Predicting the timing of the first goal in the second half of a football match can offer valuable insights and strategic implications, as it can inform tactical adjustments at halftime based on first-half performance. This particular issue has not received much attention in sports analytics despite its significance. This study utilises survival analysis techniques to model the time-to-event of the opening goal after halftime using data from the Indian Super League (ISL), focusing on examining how a team’s first-half performance statistics impact their scoring timing in the second half. The extended Cox model revealed that the number of completed passes in the first half is a statistically significant factor that affects the timing of the initial second-half goal. In an effort to enhance the comprehensiveness of the analysis, the study then transits to the Bayesian proportional hazards model and the Bayesian Accelerated Failure Time (AFT) models. It was found that the number of corners and goal saves by the teams were significant indicators of the timing of the opening second-half goal. The unique aspect of this study lies in its innovative application of Bayesian survival modelling techniques to football-related data. A comparison of the models indicates that the Bayesian viewpoint exhibits superiority in this evaluation. Through the quantification of crucial in-game metrics on scoring trends post-halftime, this framework presents a valuable tool for sports analysts and coaches to assess strategic choices during crucial intervals of a match. Furthermore, the methodologies investigated have the potential for extension to other top-tier leagues and sports, paving the way for improved data-informed decision-making and intra-match analysis in professional sports.

Keywords: Bayesian; Data analytics; Football; Sports; Survival analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01558-2

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