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</script>In any game, selection of best players in a team plays vital role in overall team performance. The team selection in any sport is the key task to ensure good performance of the team. Players are selected based on different criteria. In game of cricket selection of players should consider parameters like players own performance, ground condition, weather forecasting, opposition strength and weakness etc. Machine learning can play vital role in players’ performance prediction. Machine learning uses historical data of team performance and past performance of individual players to predict overall performance of team. Prediction of individual player performance helps in team building process. Recently many researchers proposed model for prediction of player’s performance for a game of cricket. Researchers’ uses machine learning approach for prediction. However existing studies omits some vital features related to ground and weather in their study which have potential to make huge impact on player’s performance. We performed detailed study and literature survey to propose efficient performance prediction of players for game of cricket. Our model will help in best team selection and thus improves overall team performance.
Cricket, Performance Prediction, Machine Learning, Decision Tree, Random Forest
Cricket, Performance Prediction, Machine Learning, Decision Tree, Random Forest
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
| views | 2 | |
| downloads | 9 |

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