Maximizing the Runs Scored by a Team in Cricket using Genetic Algorithm
Keywords:Cricket, Genetic Algorithm, Evolutionary Computing, Batting order, Optimization
Batting teams get two resources in cricket to score runs with, viz. wickets and balls. Trying to score as many runs as possible off every ball a team faces risks losing wickets. Trying to preserve wickets comes at the cost of not enough runs being scored. Thus, to maximize the runs scored, teams need to make efficient use of the balls and wickets available to them. To that end, teams primarily employ two types of batsmen-aggressive batsmen who try to score as many runs as possible off the balls they face and defensive batsmen who try to protect their wickets. Having too many aggressive batsmen helps a team score more runs off the balls they face but it stands the risk of a team losing all its wickets before they face all of their allotted balls. Having too many defensive batsmen helps a team face all the balls allotted to them but they may not end up scoring enough runs. Hence, selecting the right combination of defensive and aggressive batsmen is essential to maximize the runs a batting team scores. However, this is a computationally complex problem to solve. This study proposes the use of genetic algorithm to optimize the batting lineup of a team to help it maximize the runs scored in an innings. The test results indicate, by using the genetic algorithm, the number of runs scored batting first by the full member teams in the last five years can be improved by 5.46% on average.
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