Game Within the Game: "Unveiling Hidden Patterns of the IPL using Machine Learning techniques"
Abstract
The Indian Premier League (IPL) is a premier cricket tournament in India, featuring ten teams representing various regions across the country. This study presents a detailed performance analysis of IPL players using key metrics such as total runs, wickets, batting averages, and economy rates. Utilizing IPL data from the 2020 to 2023 seasons, the research leverages data analytics to generate visual representations that uncover player trends and team dynamics. These insights serve as valuable tools for strategic decision-making, from player selection to match planning. The increasing reliance on analytics by team management and platforms like fantasy cricket and betting systems underscores its growing importance in the modern game. Furthermore, the study implements machine learning algorithms—including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost—to predict match outcomes based on player-specific features such as strike rates, economy, and wicket performance. Among the models tested, Decision Tree and XGBoost emerge as the most accurate and effective predictors of match results.
References
Lamsal, R., Choudhary, A.: Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning; International Journal of Computer Applications, ISSN: 0975-8887; September 2020, vol.175, no.30, pp.15-20
Passi, K., Pandey, N. K.: Increased prediction accuracy in the game of cricket using machine learning; International Journal of Data Mining & Knowledge Management Process, ISSN: 2230-9608; April 2018, vol.8, no.2, pp.1-11
Mahajan, S., Kandhari, G., Shaikh, S., Pawar, R., Vora, R., Deshpande,A.R.: Cricket Analytics and Predictor; International Journal of Advanced Research in Computer Science, ISSN: 0976-5697; March 2021, vol.12, no.2, pp.450-457
Chawan, P.M.: Prediction of Live Cricket Score and Winning; International Journal of Computer Applications., ISSN: 0975-8887; July 2018, vol.182, no.1, pp1-4
Passi, K., Pandey, N.: Predicting player's performance in one-day international cricket match using machine learning; International Journal of Computer Applications, ISSN: 0975-8887; February 2018, vol.179, no.7, pp.30-34
Prakash, C.D., Patvardhan, C., Lakshmi, C.V.: Data Analytics based Deep Mayo Predictor for IPL-9; International Journal of Advanced Research in Computer Science, ISSN: 0976-5697; April 2019, vol.10, no.4, pp.45-52
Das, D.: An Integer Optimization Framework for Fantasy Cricket League Selection and Substitution; International Journal of Operations Research and Optimization, ISSN: 2349-4327; June 2017, vol.8, no.2, pp.23-29
Swartz, T.B.: Research Directions in Cricket; Journal of Sports Analytics, ISSN: 2215-020X; March 2018, vol.4, no.1, pp.1-12
Mahata, S., Kamra, N., Agarwal, N.K.: Application of Machine Learning for Predictive Analytics: Indian Premier League (IPL) T-20 Cricket Matches; International Journal of Machine Learning and Computing, ISSN: 2010-3700; February 2022, vol.12, no.1, pp.14-21
Davenport, T.H.: What businesses can learn from sports analytics; MIT Sloan Management Review;2014, vol. 55, no. 4, pp.10
Kumar, S., Roy,S: Score Prediction and Player Classification Model in the Game of Cricket Using Machine Learning; International Journal of Scientific and Engineering Research, ISSN: 2229-5518; August 2018, vol. 9, issue 8
Sudhamathy, G., Meenakshi, G.R.: Prediction on IPL Data Using Machine Learning Techniques in R Package; International Journal of Computer Applications, ISSN: 0975-8887; December 2019, vol. 185, no. 10, pp. 12-18
Banasode, P., Patil, M., Verma, S.: Analysis and Predicting Results of IPL T20 Matches; International Journal of Data Science and Analytics, ISSN: 2364-415X; September 2020, vol.10, no.3, pp.265-278
Prakash, C.D., Patvardhan, C., Singh, S.: A new Category based Deep Performance Index using Machine Learning for ranking IPL Cricketers; International Journal of Machine Learning and Computing, ISSN: 2010-3700; May 2020, vol.10, no.5, pp.690-696
Reddy, K.R., Chandrakanth, V., Premsai, A.: "Super Predictor of Indian Premier League (IPL) using Various ML techniques with help of IBM"; International Journal of Electronics, Electrical and Computational System (IJEECS), ISSN: 2321-9653; February 2016, Volume 5, Issue 2
Beal, R., Norman, T.J., & Ramchurn, S.D.: 'Artificial intelligence for team sports: a survey'; International Journal of Sports Science & Coaching, ISSN: 1747-9541; January 2019, vol.14, no.1, pp.30-45.
Das, N.R., Priya, R., Mukherjee, I., & Paul, G.: Modified hedonic based price prediction model for players in IPL auction; International Journal of Sports Science & Coaching, ISSN: 1747-9541; January 2021, vol.16, no.1, pp.112-125.
Ishi, M., Patil, D.J., Patil, D.N., & Patil, D.V.: Winner prediction in one day international cricket matches using machine learning framework: An ensemble approach; International Journal of Electronics, Electrical and Computational System (IJEECS), ISSN: 2348-117X; April 2022; vol. 11, Issue 4.
Kadapa, S.: "How sustainable is the strategy of the Indian Premier League (IPL)? A critical review of 10 key issues that impact the IPL strategy"; International Journal of Sports Marketing and Sponsorship, ISSN: 1464-6668; 2013, vol.14, no.4, pp.348-365.
Pansare, J., Khande, S., Oswal, A., Munsiff, Z., Choudhary, S., & Kumbhar, V.: Cricket score prediction using XGBoost regression; Journal of Sports Analytics, ISSN: 2543-6358; May 2023, vol.7, no.2, pp.65-73
Patel, N., & Pandya, M.: IPL players performance prediction; International Journal of Computer Sciences and Engineering, ISSN: 2347-2693; 2019, vol.7
Rodrigues, N., Sequeira, N., Rodrigues, S., & Shrivastava, V: Cricket squad analysis using multiple random forest regression. International Journal of Machine Learning and Computing, 2019; vol.9, no.2, pp.156-163.
Shakil, F.A., Abdullah, A.H., Momen, S., & Mohammed, N: Predicting the result of a cricket match by applying data mining techniques. International Journal of Data Mining and Knowledge Management Process, ISSN: 2230-9608, 2020; vol.10, no.1, pp.12-24
K. Gawande,,“Predictive analysis of an IPL match using machine learning”, International journal of Creative Research Thoughts (IJCRT), ISSN 2320-2882, Vol. 10, Issue 4, April 2022.
R. Suguna, Y. Praveen Kumar, J. Suriya Prakash, P. S. Neethu and S. Kiran, "Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization," 2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon), HASSAN, India, 2023, pp. 1-6, doi: 10.1109/MysuruCon59703.2023.10396955.
P. Sanjeeva et. al., “Automated Cricket Score Prediction”, E3S Web of Conferences ICMPC 2023 53 ,2023, https://doi.org/10.1051/e3sconf/202343001053
A. V. Shenoy, A. Singhvi, S. Racha, and S. Tunuguntla, "Prediction of the outcome of a Twenty-20 Cricket Match: A Machine Learning Approach," arXiv preprint arXiv:2209.06346, 2022. [Online]. Available: https://arxiv.org/abs/2209.06346
V. Gayatri and V. Bharathi, "Comprehensive Data Analysis and Prediction on Indian Premier League Using Machine Learning Techniques," International Journal of Engineering and Techniques, vol. 10, no. 3, pp. 1–6, Jun. 2024 [Online]. Available: https://new.ijetjournal.org/ipl-prediction-machine-learning/
C. S. Sanaboina and K. V. Kumar, "Win Probability Prediction for IPL Match Using Various Machine Learning Techniques," International Journal of Engineering and Computer Science, vol. 5, no. 2, pp. 13–20, 2023. [Online]. Available: https://www.computersciencejournals.com/ijecs/archives/2023.v5.i2.A.94
M. Bhatnagar, "FanCric: Multi-Agentic Framework for Crafting Fantasy 11 Cricket Teams," arXiv preprint, arXiv:2410.01307, Oct. 2024. [Online]. Available: https://arxiv.org/abs/2410.01307
A. J. Lal, A. Shaji, B. Binoy, D. R. Kiran, D. P. S., and G. Mohan, "Unveiling the Dynamics of Cricket: A Sports Analytics Framework for IPL Match Prediction," Journal of Advances in Computational Intelligence Theory, vol. 7, no. 3, pp. 45–52, 2024. [Online]. Available: https://hbrppublication.com/OJS/index.php/JACIT/article/view/5971
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Sustainable Development Through AI, ML and IoT
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.