Football scores, the Poisson distribution and 30 years of final year projects in Mathematics, Statistics and Operational Research
Abstract
The development of the Poisson match as a model used in the prediction of the outcome of football matches is described. In this context, many interesting modelling projects arise that are suitable for undergraduate, final year students. In a narrative that discusses the authors engagement with this model and other related models, the paper presents a number of these projects, their attractions and their pitfalls, and poses a number of questions that are suitable for investigation. The answers to some of these questions would be worthy of the attention of the administrators of their respective sports.
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BBC Sport, 2017. Premier League: Chelsea to win by seven points? Arsenal outside top four? Available at: http://www.bbc.co.uk/sport/football/38524102. [Accessed 12 January 2017].
Bradley, R.A. and Terry, M.E., 1952. The rank analysis of incomplete block designs 1. The method of paired comparisons. Biometrika, 39, pp.324-345.
Brady, C., Forde, M. and Chadwick, C., 2017. Why your company needs data translators. MIT Sloan Management Review, Winter 2017 issue.
Buraimo, B. and Simmons, R., 2015. Uncertainty of outcome or star quality? Television audience demand for English premier league football. International Journal of the Economics of Business, 22, pp. 449-469.
Dixon, M. and Coles, S., 1997. Modelling association football scores and inefficiencies in the football betting market. Applied Statistics, 46, pp. 265–280.
Forrest, D. and Simmons, R., 2002. Outcome uncertainty and attendance demand in sport: The case of English soccer. Journal of the Royal Statistical Society: Series D, 51, pp.229–241.
Karlis, D. and Ntzoufras, I., 2003. Analysis of sports data using bivariate Poisson models. Journal of the Royal Statistical Society: Series D, 52, pp.381–393.
Koopman, S. and Lit, R., 2015. A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League. Journal of the Royal Statistical Society: Series A, 178, pp.167–186.
Maher, M., 1982. Modelling association football scores. Statistica Neerlandica, 36, pp.109–118.
Lewis, M., 2003. Moneyball: The Art of Winning an Unfair Game. New York: Norton.
McHale, I. and Scarf, P., 2007. Modelling soccer matches using bivariate discrete distributions with general dependence structure. Statistica Neerlandica, 61, pp.432-445.
McHale, I. and Scarf, P., 2011. Modelling the dependence of goals scored by opposing teams in international soccer matches. Statistical Modelling, 11, pp.219–236.
McHale, I., Scarf, P. and Folker, D., 2012. On the development of a soccer player performance rating system for the English Premier League. Interfaces, 42, pp.339-351.
Owen, A., 2011. Dynamic Bayesian forecasting models of football match outcomes with estimation of the evolution variance parameter. IMA Journal of Management Mathematics, 22, pp.99–113.
Percy, D., 2009. A mathematical analysis of badminton scoring systems. Journal of the Operational Research Society, 60, pp.63-71.
Porter, R. and Bartholomew, H., 2016. When will I ever use that? Giving students opportunity to see the direct application of modelling techniques in the real world. MSOR Connections, 14, pp.45-49.
Scarf, P., 2007. Route choice in mountain navigation, Naismith’s rule and the equivalence of distance and climb. Journal of Sports Sciences, 25, pp.719-726.
Scarf, P., Mat Yusof, M. and Bilbao, M., 2009. A numerical study of designs for sporting contests. European Journal of Operational Research, 198, pp.190-198.
Scarf, P., Shi, X. and Akhtar, S., 2011. The distribution of runs scored and batting strategy in test cricket. Journal of the Royal Statistical Society, Series A, 174, pp.471-497.
Utt, J. and Fort, R., 2002. Pitfalls to measuring competitive balance with Gini coefficients. Journal of Sports Economics, 3, pp.367-373.
DOI: https://doi.org/10.21100/msor.v15i3.491
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