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An intelligent clustering framework for substitute recommendation and player selection

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Abstract

Player selection is an important aspect of team-based sports such as cricket. Various situations, like players getting injured, rested, or falling under disciplinary action, etc., are common in cricket, and in those circumstances, the proper substitution of players is very important. We present an innovative knowledge-based intelligent framework for substitute suggestions by employing various clustering techniques like DBSCAN, Spectral clustering. We compared it with the substitution made in the real-time team selection process and obtained a large similarity between that and the recommendations generated using Spectral clustering. We have also compared our proposed results with existing state-of-the-art works in our experimental setup, where they have used the K-means clustering technique. The results highlighted that Spectral clustering is the best choice for substitute recommendations among the mentioned clustering techniques. We also present an intelligent framework for team selection and apply various similarity measures like Euclidean distance, Cosine Similarity, Manhattan distance, and Pearson Correlation Coefficient to find the most accurate combination of players. The recommendations obtained from the Pearson Correlation Coefficient have a maximum accuracy of 77.50% with a high F-measure (0.77) and present different directions for the team line-up formation.

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Authors and Affiliations

  1. Indian Institute of Information Technology, Kalyani, 741235, India

    Nayan Ranjan Das, Imon Mukherjee & Anubhav D. Patel

  2. CSRU, Indian Statistical Institute, Kolkata, 700108, India

    Goutam Paul

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Das, N.R., Mukherjee, I., Patel, A.D. et al. An intelligent clustering framework for substitute recommendation and player selection. J Supercomput 79, 16409–16441 (2023). https://doi.org/10.1007/s11227-023-05314-z

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