An efficient team prediction for one day international matches using a hybrid approach of CS-PSO and machine learning algorithms

Ishi, Manoj S.; Patil, J. B.; Patil, Vaishali M.

doi:10.1016/j.array.2022.100144

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…The top players on the chart are chosen for teams by this study, which raises the probability of winning games. By grouping players into five categories, the study [62] seeks to precisely determine cricket teams in the ODI format. The players are ranked as excellent, very good, good, satisfactory, or poor depending on their past and present performances.…”

Section: Literature Reviewmentioning

confidence: 99%

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

Md. Robel,

Md. Ashikur Rahman Khan,

Ishtiaq Ahammad

et al. 2023

Cloud Computing and Data Science

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Cricket player selection is a crucial task for both national teams and franchise leagues. Traditionally, selectors rely on their experience and knowledge to evaluate a player's physical fitness, batting, and bowling performance. However, with the advancements in machine learning algorithms, it is possible to automate and improve the selection process. In this research, a Machine Learning (ML)-based approach is proposed for cricket player selection. This approach uses a combination of physical fitness data, batting and bowling statistics, and other relevant metrics to create a comprehensive player profile. Then three ML algorithms-linear regression, support vector regression, and random forests-are employed to identify the most promising players. Data is collected on a large number of cricket players and their performance in national and franchise leagues from the two most prominent cricket websites, espncricinfo, and cricbuzz, to evaluate the proposed approach. Then the proposed approach trained and tested the ML models on this data and compared their accuracy and performance. Based on the performance scores obtained from these models, two squads are selected for the national team and one squad for each franchise league team. The results demonstrate that the proposed approach can significantly improve the selection process and identify players with high potential. Furthermore, results found that the support vector regression algorithm outperformed other ML models in terms of prediction and player selection.

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Section: Literature Reviewmentioning

confidence: 99%