Predicting Team Success in the Indian Premier League Cricket 2024 Season Using Random Forest Analysis
DOI:
https://doi.org/10.17309/tmfv.2024.2.16Keywords:
Indian Premier League, Random Forest, machine learning, team performance prediction, cricket analyticsAbstract
Background. Random Forest is a popular machine learning algorithm used for classification and regression tasks. The study purpose is to investigate the use of Random Forest machine learning to predict the winning chances of teams in the 2024 Indian Premier League (IPL) season.
Objectives. By analyzing comprehensive player statistics, including matches played, batting and bowling averages, as well as fielding contributions, the study aims to understand the factors that influence team success in T20 cricket and provide actionable insights for team management, betting markets, and cricket enthusiasts.
Material and methods. The study involved 10 cricket teams competing in the IPL 2024 season. Player statistics and match outcomes data from previous IPL seasons were collected and cleaned, with missing values addressed through imputation. The features were based on player statistics, including both aggregate measures and calculated metrics. A Random Forest is chosen as the machine learning model, trained using preprocessed data, with features derived from player statistics as input and match outcomes as the target variable. The dataset was split into training and validation sets, using methodologies such as cross-validation to ensure a robust model performance.
Results. The Random Forest model demonstrates strong predictive performance, with a low Mean Squared Error (MSE) of 8.2174, Root Mean Squared Error (RMSE) of 2.8666, and a high R-Squared value of 0.9173, indicating approximately 91.73% variance explained in the data. Chennai Super Kings emerge as frontrunners with a predicted performance percentage of 83.4%, while teams like Punjab Kings and Lucknow Super Giants show lower predicted performance percentages, suggesting potential areas for its improvement.
Conclusions. This study reveals the effectiveness of Random Forest machine learning in forecasting IPL match outcomes based on player statistics. It provides valuable insights into team dynamics and offers actionable recommendations for team management and cricket enthusiasts. The findings enrich our understanding of IPL match dynamics, contribute to the evolution of cricket analytics, and promote greater engagement with sport, ultimately enhancing the fan experience in the IPL.
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Subburaj, M., Rao, G. R. K., Parashar, B., Jeyabalan, I., Semban, H., & Sengan, S. (2023). Artificial Intelligence for Smart in Match Winning Prediction in Twenty20 Cricket League Using Machine Learning Model. EAI/Springer Innovations in Communication and Computing, Part F632, 31–46. https://doi.org/10.1007/978-3-031-23602-0_3 DOI: https://doi.org/10.1007/978-3-031-23602-0_3
Kapadia, K., Abdel-Jaber, H., Thabtah, F., & Hadi, W. (2022). Sport analytics for cricket game results using machine learning: An experimental study. Applied Computing and Informatics, 18(3-4), 256–266. https://doi.org/10.1016/j.aci.2019.11.006 DOI: https://doi.org/10.1016/j.aci.2019.11.006
Sanjaykumar, S., Udaichi, K., Rajendiran, G., Cretu, M., & Kozina, Z. (2024). Cricket performance predictions: A comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023. Health, Sport, Rehabilitation, 10(1), 6–19. https://doi.org/10.58962/HSR.2024.10.1.6-19 DOI: https://doi.org/10.58962/HSR.2024.10.1.6-19
Passi, K., & Pandey, N. (2018). Increased Prediction Accuracy in the Game of Cricket Using Machine Learning. International Journal of Data Mining & Knowledge Management Process, 8, 19–36. https://doi.org/10.5121/ijdkp.2018.8203 DOI: https://doi.org/10.5121/ijdkp.2018.8203
Sumathi, M., Prabu, S., & Rajkamal, M. (2023). Cricket Players Performance Prediction and Evaluation Using Machine Learning Algorithms. Proceedings of the 1st IEEE International Conference on Networking and Communications 2023, ICNWC 2023. https://doi.org/10.1109/ICNWC57852.2023.10127503 DOI: https://doi.org/10.1109/ICNWC57852.2023.10127503
Abebe, M., Shin, Y., Noh, Y., Lee, S., & Lee, I. (2020). Machine learning approaches for ship speed prediction towards energy efficient shipping. Applied Sciences (Switzerland), 10(7), 2325. https://doi.org/10.3390/app10072325 DOI: https://doi.org/10.3390/app10072325
Passi, K., & Pandey, N. (2018). Predicting Players’ Performance in One Day International Cricket Matches Using Machine Learning. Proceedings of the 8th International Conference on Computer Science, Engineering, and Applications. https://doi.org/10.5121/csit.2018.80310 DOI: https://doi.org/10.5121/csit.2018.80310
Wickramasinghe, I. P. (2014). Predicting the performance of batsmen in test cricket. Journal of Human Sport and Exercise, 9(4), 744–751. https://doi.org/10.14198/jhse.2014.94.01 DOI: https://doi.org/10.14198/jhse.2014.94.01
Bai, Z., & Bai, X. (2021). Sports Big Data: Management, Analysis, Applications, and Challenges. Complexity. https://doi.org/10.1155/2021/6676297 DOI: https://doi.org/10.1155/2021/6676297
Aburas, A., Mehtab, M., & Mehtab, Y. (2018). ICC World Cup Prediction Based on Data Analytics and Business Intelligent (BI) Techniques. In Proceedings - International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2018 (pp. 273-279). https://doi.org/10.1109/CyberC.2018.00056
Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied Computing and Informatics, 15(1), 27-33. https://doi.org/10.1016/j.aci.2017.09.005 DOI: https://doi.org/10.1016/j.aci.2017.09.005
ESPNcricinfo. (n.d.). Retrieved December 27, 2023, from https://www.espncricinfo.com/
Indian Premier League official website. (n.d.). Retrieved December 29, 2023, from https://www.iplt20.com/
Sivaramaraju Vetukuri, V., Rajender, R., & Sethi, N. (2019). A multi-aspect analysis and prediction scheme for cricket matches in standard T-20 format. International Journal of Knowledge-Based and Intelligent Engineering Systems, 23(3), 149–154. https://doi.org/10.3233/KES-190407 DOI: https://doi.org/10.3233/KES-190407
Hudnurkar, S., & Rayavarapu, N. (2022). Binary classification of rainfall time-series using machine learning algorithms. International Journal of Electrical and Computer Engineering, 12(2), 1945–1954. https://doi.org/10.11591/ijece.v12i2.pp1945-1954 DOI: https://doi.org/10.11591/ijece.v12i2.pp1945-1954
Baboota, R., & Kaur, H. (2019). Predictive analysis and modeling football results using a machine learning approach for the English Premier League. International Journal of Forecasting, 35(2), 741–755. https://doi.org/10.1016/j.ijforecast.2018.01.003 DOI: https://doi.org/10.1016/j.ijforecast.2018.01.003
Wickramasinghe, I. (2020). Naive Bayes approach to predict the winner of an ODI cricket game. Journal of Sports Analytics, 6, 75–84. https://doi.org/10.3233/jsa-200436 DOI: https://doi.org/10.3233/JSA-200436
Lakshmi, P. Y., Sanjaykumar, S., Dharuman, M., & Elangovan, A. (2024). Using Support Vector Regression Kernel Models for Cricket Performance Prediction in the Women’s Premier League 2024. Physical Education Theory and Methodology, 24(1), 72–78. https://doi.org/10.17309/tmfv.2024.1.09 DOI: https://doi.org/10.17309/tmfv.2024.1.09
Saikia, H. (2020). Quantifying the Current Form of Cricket Teams and Predicting the Match Winner. Management and Labour Studies, 45(2), 151-158. https://doi.org/10.1177/0258042X20912603 DOI: https://doi.org/10.1177/0258042X20912603
Kaur, A., Kaur, R., & Jagdev, G. (2021). Analyzing and Exploring the Impact of Big Data Analytics in the Sports Sector. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00575-y DOI: https://doi.org/10.1007/s42979-021-00575-y
Bhattacharjee, D., & Talukdar, P. (2020). Predicting outcome of matches using pressure index: Evidence from Twenty20 cricket. Communications in Statistics - Simulation and Computation, 49(11), 3028–3040. https://doi.org/10.1080/03610918.2018.1532003 DOI: https://doi.org/10.1080/03610918.2018.1532003
Van Eetvelde, H., Mendonça, L. D., Ley, C., Seil, R., & Tischer, T. (2021). Machine learning methods in sport injury prediction and prevention: a systematic review. Journal of Experimental Orthopaedics, 8(1), 27. https://doi.org/10.1186/s40634-021-00346-x DOI: https://doi.org/10.1186/s40634-021-00346-x
Men, Y. (2022). Intelligent sports prediction analysis system based on improved Gaussian fuzzy algorithm. Alexandria Engineering Journal, 61(7), 5351–5359. https://doi.org/10.1016/j.aej.2021.08.084 DOI: https://doi.org/10.1016/j.aej.2021.08.084
Turhan, F. H., & Canpolat, B. (2023). Researching the future expectations of sports sciences students. Physical Education of Students, 27(3), 126–134. https://doi.org/10.15561/20755279.2023.0305 DOI: https://doi.org/10.15561/20755279.2023.0305
Šuštaršič, A., Videmšek, M., Karpljuk, D., Miloloža, I., & Meško, M. (2022). Big Data in Sports: A Bibliometric and Topic Study. Business Systems Research, 13(1), 19–34. https://doi.org/10.2478/bsrj-2022-0002 DOI: https://doi.org/10.2478/bsrj-2022-0002
Gu, B., Cao, J., Pan, F., & Xiong, W. (2023). Incremental learning for Lagrangian ε-twin support vector regression. Soft Computing, 27(9), 5357–5375. https://doi.org/10.1007/s00500-022-07755-9 DOI: https://doi.org/10.1007/s00500-022-07755-9
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Copyright (c) 2024 Swamynathan Sanjaykumar, Subhashree Natarajan, Ponnusamy Yoga Lakshmi, Farjana Akter Boby
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