  Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20,
is a short form of cricket. In a Twenty20 game the two teams of 11 players have
a single innings each, which is restricted to a maximum of 20 overs. This
version of cricket is especially unpredictable and is one of the reasons it has
gained popularity over recent times. However, in this paper we try four
different machine learning approaches for predicting the results of T20 Cricket
Matches. Specifically we take in to account: previous performance statistics of
the players involved in the competing teams, ratings of players obtained from
reputed cricket statistics websites, clustering the players' with similar
performance statistics and propose a novel method using an ELO based approach
to rate players. We compare the performances of each of these feature
engineering approaches by using different ML algorithms, including logistic
regression, support vector machines, bayes network, decision tree, random
forest.
