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Predictive Modelling of Team Success in the t20 Big Bash League
Conference paper   Peer reviewed

Predictive Modelling of Team Success in the t20 Big Bash League

Aaron Corris, Anthony Bedford and Ian Grundy
Proceedings of the 5th International Conference on Mathematics in Sport, pp.19-24
MathSport International Conference, 2015 (Longhborough, United Kingdom, 29-Jun-2015–01-Jul-2015)
MathSport International
2015
url
http://www.mathsportinternational.com/MathSport2015Proceedings.pdfView
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Abstract

Human Movement and Sports Science
Due to the shortened nature of a T20 cricket match compared to Test or 50 over cricket, many commentators believe the matches to be little more than luck. This paper looks at the Australian domestic t20 competition, known as the Big Bash League, to look at whether its possible to predict matches with a better than 50-50 chance at a team versus team level. An Elo model was built to predict the results of matches and compared against the results of predicting the market favourite to win each match. The Elo model predicted more matches correctly than picking the favourite according to the market, but picking the market favourite had the best result in any one season. Home Ground Advantage, winning the toss and which team batted first were factors investigated, however none of those factors had a significant impact. Ultimately, the Elo model appears to be a reasonably good indicator for team strength in the Big Bash League.

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