Dynamic prediction of Australian Rules football using real time performance statistics

This thesis contains detailed analysis of Australian Rules football, played in the Australian Football League (AFL). Data from 1295 matches, dating back to 1998, as collected by the League’s official information providers, Champion Data, has been used for the analysis as they were the industry partner for the reserach. The quality and detail associated with the data has enabled analysis to be performed that previously would have been impossible. Statistical distributions are fit to scoring events, both on attack and defence, for teams in the competition. It is discovered that the Poisson distribution provides a better approximation of the data than the negative binomial distribution for individual teams. Correlations between scoring events are also analysed with a view to developing a prematch prediction model. Using the results of the exploratory analysis, a static pre-match model that performs better than a model updated at half time, is presented. This model uses negative binomial regression to predict goals and behinds separately for each team and consequently a predicted score. The failure of this model to adapt to dynamic events resulted in models being pursued that could adjust for events as they happened. An eight state global Markov process model is presented that provides an adequate approximation to AFL football with no regard to location of events on the playing field. Transition probabilities are derived for each state using the transaction files collected by Champion Data for matches in 2003 and 2004. This model is then used for post-match applications, including altering play scenarios and calculating the effect of rule changes, as well as dynamically updating match predictions using live match data. It is expected that these applications will be made available to the wider football community over the next couple of seasons. The coding of events by Champion Data according to their location on the field enabled a second model to be developed that calculated transition probabilities by zone. This 18 state zone model improved upon the global model due to the inclusion of more information. The zone approach will be more informative to AFL teams as it gives a clearer indication of the functionality of the attacking, midfield and defensive units, rather than looking on these units as a whole. The zone model was used to replicate the applications of the global model and investigate whether different results were produced. Extra applications were made available with the introduction of the zone model, particularly investigating play strategy in different areas of the ground. Regression models were again developed for predicting match margin at different stages of a match using the transition probabilities up to that stage. The accuracy of these models was good with significant amounts of the variation in final margin explained and this accuracy increased noticeably as a match progressed. The models were used to test the differences in style of play for each team when compared to the competition average. Finally, playing styles of teams were compared for home state games and interstate games to test which transitions differed significantly. The models presented in this thesis provided accurate approximations of AFL football that has not been seen elsewhere. Some of the applications of these models are already being used by AFL clubs and further commercialisation of the applications will take place over the next season with a view to providing detailed mathematical analysis to the AFL industry in years to come.