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Probabilistic match importance in professional sports De Lorenzo, M 2018, Probabilistic match importance in professional sports, Doctor of Philosophy (PhD), Science, RMIT University. Document type: Thesis Collection: Theses Attached Files Name Description MIMEType Size de_Lorenzo.pdf Thesis application/pdf 3.65MB Title Probabilistic match importance in professional sports Author(s) De Lorenzo, M Year 2018 Abstract Quantifying the importance of a match in professional sports can be beneficial in a variety of circumstances, including in the statistical modelling of match outcomes and attendance figures. Current literature on probabilistic match importance measures have overlooked key information, including the significance of a draw outcome in football (soccer), and the multiple end-of-season outcomes that a team can achieve in a season. The aim of this research was to develop a probabilistic measure of match importance that accounts for different end-of-season outcomes and is adaptable to both a two-result (win/loss) and a three-result (win/draw/loss) sport. By first furthering an existing probabilistic measure of match importance, a new model was developed that calculates the importance of achieving in different end-of-season outcomes using a Markov Chain model for both NBA basketball and Bundesliga football. Furthermore, the new model allows for the significance of a draw outcome to be quantified in the latter; which has not been fully achieved in past literature. The new model was compared to a complete Monte Carlo simulation model, where it was observed that the results between the two modelling techniques were similar. The results from the new model were then applied to an Elo ratings system and a stepwise regression model, where an effect on each model’s predictive performance was observed when matches were classified by their level of importance to the competing teams. The completion of this research helps further the knowledge on calculating and applying match importance within sports modelling. Degree Doctor of Philosophy (PhD) Institution RMIT University School, Department or Centre Science Subjects Statistics not elsewhere classified Stochastic Analysis and Modelling Applied Statistics Keyword(s) match importance sports statistics Markov chain prediction football basketball Versions Version Filter Type Fri, 13 Apr 2018, 13:55:29 EST Fri, 13 Apr 2018, 14:00:08 EST Fri, 13 Apr 2018, 14:01:33 EST Fri, 13 Apr 2018, 14:02:51 EST Fri, 16 Nov 2018, 10:55:31 EST Fri, 16 Nov 2018, 10:56:53 EST Fri, 16 Nov 2018, 11:01:17 EST Filtered Full Access Statistics: 107 Abstract Views, 133 File Downloads  -  Detailed Statistics Created: Fri, 13 Apr 2018, 13:55:26 EST by Denise Paciocco