Dynamic modelling and prediction of English Football League matches for betting

Abstract

We focus on modelling the 92 soccer teams in the English Football Association League over the years 1992-1997 using refinements of the independent Poisson model of Dixon and Coles. Our framework assumes that each team has attack and defence strengths that evolve through time (rather than remaining constant) according to some unobserved bivariate stochastic process. Estimation of the teams' attack and defence capabilities is undertaken via a novel approach involving an approximation that is computationally convenient and fast. The results of this approximation compare very favourably with results obtained through the Dixon and Coles approach. We note that the full model (i.e. the model before the above approximation is made) may be implemented using Markov chain Monte Carlo procedures, and that this approach is vastly more computationally expensive. We focus on the probabilities of home win, draw or away win because these outcomes constitute the primary betting market. These probabilities are estimated for games played between any two of the 92 teams and the predictions are compared with the actual results.

Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

“…Applications of time-series models to football were published by Knorr-Held (2000), Crowder et al (2002) and Owen (2011). Glickman and Stern (1998), Stefani (2009) and Percy (2011a) considered American football, rugby and Alpine skiing, respectively, while Urban (2012) and Cattelan et al (2013) considered basketball.…”

“…This sequential updating algorithm is particularly useful for interactive dynamic learning while sporting competitions are in progress. Its use for 'within-game' analysis is relatively new, though Glickman and Stern (1998), Knorr-Held (2000), Crowder et al (2002) and Owen (2011) considered applications to 'between-game' situations. Furthermore, Congdon (2003) presented an accessible account of Bayesian inference for several dynamic models, including the time-series models that we review and apply here.…”

A measure of competitiveness in leagues: a network approach

“…In the NFL football league in the US the order in which teams select incoming players for a new season -the draft -is determined by league position, the bottom team having first pick.) In trying to estimate the outcomes of particular matches it is sometimes assumed that betting odds contain useful information about these various factors Thomas, 1992, 1997;Forrest and Simmons, 2002) although a Poisson model has been used to simulate match results (Crowder et al, 2002;Croucher, 2004). A regression model using several variables has also been used for this purpose (Dobson and Goddard, 2004).…”

A generative model for predicting outcomes in college basketball

“…In the literature, we can find several variants of a simple model for soccer that identifies each team by its attack and defense coefficients (Baio and Blangiardo 2010;Crowder et al 2002;Dixon and Coles 1997;Heuer, Muller, and Rubner 2010;Maher 1982). In all these works, the score for the home team is drawn from a Poisson distribution, whose mean is the multiplicative contribution of the home team attack coefficient and the away team defense coefficient.…”