Towards probabilistic footy tipping: a hybrid approach utilising genetically defined neural networks and linear programming

Towards probabilistic footy tipping: a hybrid approach utilising genetically defined neural networks and linear programming

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Article ID: iaor20071963
Country: United Kingdom
Volume: 33
Issue: 7
Start Page Number: 2003
End Page Number: 2022
Publication Date: Jul 2006
Journal: Computers and Operations Research
Authors:
Keywords: forecasting: applications, gaming, neural networks, programming: linear
Abstract:

Using readily available data from the 1992–1995 Australian Football League season, we have developed a model that will readily predict the winner of a game, together with the probability of that win. This model has been developed using a genetically modified neural network to calculate the likely winner, combined with a linear program optimisation to determine the probability of that occurring in the context of the tipping competition scoring regime. This model has then been tested against 484 tippers in a probabilistic tipping competition for the 2002 season. We have found that the performance of the combined neural network, linear program model compared most favorably with other model based tipping programs and human tippers.

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