1. Prediction and analysis of cricket batsman performance using neural networks.
- Author
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Sreenivasgoud, Pulluri, Sirajuddin, Mohammad, Sridhar, Kankanala, Sagar, Rachoori, and Venkatesh, Thudum
- Subjects
ARTIFICIAL neural networks ,MACHINE learning ,CRICKET (Sport) ,AWARENESS advertising ,RANDOM forest algorithms - Abstract
Cricket is the more popular game with uncertainty like a single ball can change the results. Players must concentrate more on t20 and One-Day matches without diverting their presence of mind. The sponsors invest more money in the game and players for their brand advertising. The match depended on various parameters such as player performance, pitch, team binding, etc. So, selecting a player and forming a team is essential for sponsors. Predicting player performance based on previous records will take a lot of work, so many researchers have taken forward steps to analyze the data. Machine learning algorithms, such as Linear Regression, SVM, Random forest, etc., have been implemented in various research studies. In our study, we have implemented the Artificial Neural Networks model to predict the player batting performance based on the koggle.com dataset. According to our model, ANN has provided 86.21% accuracy in predicting a player. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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