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Sports result prediction using data mining techniques in comparison with base line model

Author

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  • Praphula Kumar Jain

    (Indian Institute of Technology (ISM))

  • Waris Quamer

    (Indian Institute of Technology (ISM))

  • Rajendra Pamula

    (Indian Institute of Technology (ISM))

Abstract

Sports prediction is one of the recent growing areas of interest entailing good prediction accuracy. Coaches require models in order to assess their players, analyse opponent teams and formulate winning strategies. Generation of comprehensive statistical data in sports has enabled data mining (DM) techniques to be applied to it in order to extract underlying predictive information. In this paper, an approach based on data mining is proposed for result prediction in sports. The work includes pre-processing of data, feature extraction, attribute selection and application of DM algorithms as a learning strategy. To validate our proposed model, a case study concerning prediction of the results of Indian Premier League (IPL) matches is illustrated. The constructed models are based on the performance of teams in past matches, player performance indices, opposition team information and external factors, and therefore, relevant features are engineered to indicate the same. The best prediction accuracy was found to be 70.58%.

Suggested Citation

  • Praphula Kumar Jain & Waris Quamer & Rajendra Pamula, 2021. "Sports result prediction using data mining techniques in comparison with base line model," OPSEARCH, Springer;Operational Research Society of India, vol. 58(1), pages 54-70, March.
    • Handle: RePEc:spr:opsear:v:58:y:2021:i:1:d:10.1007_s12597-020-00470-9
    DOI: 10.1007/s12597-020-00470-9
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    References listed on IDEAS

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    1. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
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    1. Praveen Ranjan Srivastava & Prajwal Eachempati & Ajay Kumar & Ashish Kumar Jha & Lalitha Dhamotharan, 2023. "Best strategy to win a match: an analytical approach using hybrid machine learning-clustering-association rule framework," Annals of Operations Research, Springer, vol. 325(1), pages 319-361, June.
    2. You-Shyang Chen & Chien-Ku Lin & Yu-Sheng Lin & Su-Fen Chen & Huei-Hua Tsao, 2022. "Identification of Potential Valid Clients for a Sustainable Insurance Policy Using an Advanced Mixed Classification Model," Sustainability, MDPI, vol. 14(7), pages 1-22, March.

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