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Unsupervised Learning Based Evaluation of Player Performances

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Abstract

In the following paper, we design a model that uses real-time data to segregate players into categories according to their performances in the T-20 tournaments. The data are gathered from reliable websites, cleaned and analysed through cluster space maps based upon certain proposed formula. A thorough research on players’ statistics with different unsupervised clustering algorithms in machine learning and deep learning models is documented and compared through silhouette scores. They are classified based on their strength according to bowlers, batsmen and all-rounders. A comparative study of machine learning algorithms with its deep learning counterparts using auto-encoder is also shown. The paper depicts how the models perform on the given dataset and concludes with the most effective model.

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Authors and Affiliations

  1. Institute of Engineering and Management, Salt Lake Electronics Complex, Y-12 Gurukul, Street Number 18, EP Block, Sector V, Bidhannagar, Kolkata, West Bengal, 700091, India

    Avijit Bose, Sannoy Mitra, Souham Ghosh, Raima Ghosh, Tiyash Patra & Satyajit Chakrabarti

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  1. Avijit Bose
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  2. Sannoy Mitra
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  3. Souham Ghosh
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  4. Raima Ghosh
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  5. Tiyash Patra
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  6. Satyajit Chakrabarti
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Correspondence to Sannoy Mitra.

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Bose, A., Mitra, S., Ghosh, S. et al. Unsupervised Learning Based Evaluation of Player Performances. Innovations Syst Softw Eng 17, 121–130 (2021). https://doi.org/10.1007/s11334-020-00374-3

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  • DOI: https://doi.org/10.1007/s11334-020-00374-3

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