Abstract
A comprehensive analysis of the complete IPL dataset and visualization of different highlights necessary for IPL assessment is performed. Many machine learning (classification) algorithms have been used to compare and predict the winner of the match. Every game has its own requirements; similarly, the T-20 game also has its own which were not satisfied by current models. By using Python, the intricacy of data analysis is reduced as it shows the analysis results using visual portrayals. The dataset is loaded, and pre-processing is done trailed by feature selection. Four machine learning (classification) algorithms such as decision tree, K-nearest neighbour, SVM, and random forest are applied, and the outcomes are compared. The best of the four classification techniques is then applied to anticipate the winner of the match and visualize the results as graphs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Banasode P, Patil M, Verma S (2021) Analysis and predicting results of IPL T20 matches. IOP Conf Ser Mater Sci Eng 1065:012040
Srikantaiah KC, Khetan A, Kumar B, Tolani D, Patel H (2021) Prediction of IPL match outcome using machine learning techniques. In: Proceedings of the 3rd international conference on integrated intelligent computing communication & security (ICIIC). Atlantis highlights in computer sciences, vol 4
Sudhamathy G, Raja Meenakshi G (2020) Prediction on IPL data using machine learning techniques in R package. ICTACT J Soft Comput 11(01)
Bhutada S, Team (2020) IPL match prediction using machine learning. Int J Adv Sci Technol 29(5):3438–3448
Amala Kaviya VS, Mishra AS, Valarmathi B (2020) Comprehensive data analysis and prediction on IPL using machine learning algorithms. Int J Emerg Technol 11(3):218–228
Sai Abhishek Ch, Patil KV, Yuktha P, Meghana KS, Sudhamani MV (2019) Predictive analysis of IPL match winner using machine learning techniques. Int J Innov Technol Explor Eng (IJITEE) 9(2S). ISSN: 2278-3075
Vistro DM, Rasheed F, David LG (2019) The cricket winner prediction with application of machine learning and data analytics. Int J Sci Technol Res 8(09)
Lamsal R, Choudhary A (2018) Predicting outcome of Indian premier league (IPL) matches using machine learning
Sankaranarayanan, Sattar J (2014) Auto-play: a data mining approach to ODI cricket simulation and prediction. In: Proceedings of SIAM conference on data mining, pp 1–7
Lemmer H (2004) A measure for the batting performance of cricket players. S Afr J Res Sport Phys Educ Recreation 26:55–64
Kimber AC, Hansford AR (1993) A statistical analysis of batting in cricket. J R Stat Soc 156:443–455
Rupai AAA, Mukta M, Islam AKMN (2020) Predicting bowling performance in cricket from publicly available data. In: International conference on computing advancements, pp 1–6
Passfield L, Hopker JG (2017) A mine of information: can sports analytics provide wisdom from your data? Int J Sports Physiol Perform 12(7):851–855
Gupta S, Jain H, Gupta A, Soni H (2017) Fantasy league team prediction. Int J Res Sci Eng 6(3):97–103
Deep Prakash Dayalbagh C, Patvardhan C, Vasantha Lakshmi C (2016) Data analytics based deep mayo predictor for IPL-9. Int J Comput Appl 152(6):6–11
Kampakis S, Thomas W (2015) Using machine learning to predict the outcome of English county twenty over cricket matches. arXiv preprint arXiv:1511.05837
Hajgude J, Parameshwaran A, Nambi K, Sakhalkar A, Sanghvi D (2015) IPL dream team-A prediction software based on data mining and statistical analysis. Int J Comput Eng Appl 9(4):113–119
Freitas AA (2014) Comprehensible classification models—a position paper. SIGKDD Explor 15(1)
Halvorsen P, Sægrov S, Mortensen A, Eichhorn A, Stenhaug M, Dahl S, Stensland HK, Gaddam VR, Griwodz C et al (2013) Bagadus: an integrated system for arena sports analytics: a soccer case study. In: Proceedings of the 4th ACM multimedia system conference. ACM, pp 48–59
Saikia H, Bhattacharjee D (2011) A Bayesian classification model for predicting the performance of all-rounders in the Indian premier league. Vikalpa 36(4):51–66
Lewis A (2008) Extending the range of player performance measures in one-day cricket. J Oper Res Soc 59:729–742
Bandulasiri A (2008) Predicting the winner in one day international cricket. J Math Sci Math Educ 3(1):6–17
Saikia H, Bhattacharjee D, Bhattacharjee A (2003) Performance based market valuation of cricketers in IPL. Sport Bus Manage Int J 3(2):127–146
Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1. IEEE, pp 278–282
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singhal, A., Agarwal, D., Singh, E., Valecha, R., Malik, R. (2023). IPL Analysis and Match Prediction. In: Bhateja, V., Sunitha, K.V.N., Chen, YW., Zhang, YD. (eds) Intelligent System Design. Lecture Notes in Networks and Systems, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-19-4863-3_3
Download citation
DOI: https://doi.org/10.1007/978-981-19-4863-3_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4862-6
Online ISBN: 978-981-19-4863-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)