Title: Increased Prediction Accuracy in the Game of Cricket using Machine Learning,
Abstract: Player selection is one the most important tasks for any sport and cricket is
no exception. The performance of the players depends on various factors such as
the opposition team, the venue, his current form etc. The team management, the
coach and the captain select 11 players for each match from a squad of 15 to 20
players. They analyze different characteristics and the statistics of the
players to select the best playing 11 for each match. Each batsman contributes
by scoring maximum runs possible and each bowler contributes by taking maximum
wickets and conceding minimum runs. This paper attempts to predict the
performance of players as how many runs will each batsman score and how many
wickets will each bowler take for both the teams. Both the problems are
targeted as classification problems where number of runs and number of wickets
are classified in different ranges. We used naïve bayes, random forest,
multiclass SVM and decision tree classifiers to generate the prediction models
for both the problems. Random Forest classifier was found to be the most
accurate for both the problems.