| Millions of dollars are wagered on the outcome of one day international
(ODI) cricket matches, with a large percentage of bets occurring after
the game has commenced. Using match information gathered from all
2200 ODI matches played prior to January 2005, a range of variables
that could independently explain statistically significant proportions
of variation associated with the predicted run totals and match outcomes
were created. Such variables include home ground advantage, past performances,
match experience, performance at the specific venue, performance against
the specific opposition, experience at the specific venue and current
form. Using a multiple linear regression model, prediction variables
were numerically weighted according to statistical significance and
used to predict the match outcome. With the use of the Duckworth-Lewis
method to determine resources remaining, at the end of each completed
over, the predicted run total of the batting team could be updated
to provide a more accurate prediction of the match outcome. By applying
this prediction approach to a holdout sample of matches, the efficiency
of the "in the run" wagering market could be assessed. Preliminary
results suggest that the market is prone to overreact to events occurring
throughout the course of the match, thus creating brief inefficiencies
in the wagering market.
KEY
WORDS: Linear regression, live prediction, market efficiency,
betting.
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