Abstract:
Sport activity is a competitive activity in which individuals or teams aim to use, maintain,
or improve physical ability and skills providing entertainment to participants and
spectators. Cricket is the most famous game in South Asia and the second most famous
game in the world.
The main problem this research address is how to predict the final score of a cricket
match as early as possible. The identification is based on factors such as team,
opposition, venue, players' performance etc. Since lot of statics needs to be done
manually for each player and team, it is very time consuming. Existing systems lack in
producing score predictions and predicting the scores before the matches. Therefore, to
predict the scores before the match an approach was proposed which considers player
performance and team related factors.
The problem was divided into two parts as identifying factors that affect match score and
determining the suitable algorithm for the prediction. Identification of the factors was
done using existing systems, surveys, and feature selection techniques. Ensemble
Learning was selected as the machine learning approach to address this problem.
This research identified team, opposition team, player batting index, player bowling
index, ground, and run rate as the factors which will impact on the final scores. The
regression ensemble model was created using a weight average technique for this
purpose. The model gave an accuracy of 82%. The overall feedback of the experts'
evaluation was positive and few suggested having a better ensemble approach as future
enhancements
