Abstract:
This paper proposes two novel approaches for
predicting the outcome of cricket matches by modelling the
team performance based on the performances of it’s players
in other matches. Our first approach is based on feature
encoding, which assumes that there are different categories
of players exist and models each team as a composition of
player–category relationships. The second approach is based on
a shallow Convolutional Neural Network (CNN) architecture,
which contains only four layers to learn an end-to-end mapping
between the performance of the players and the outcome of
matches. Both of our approaches give considerable improvement
over the baseline approaches we consider, and our shallow CNN
architecture performs better than our proposed feature encodingbased
approach. We show that the outcome of a match can be
predicted with over 70% of accuracy.
