Statistic analysis of IPL match score and winner inning wise using machine learning algorithms

Preety; Ray, Sachin Kumar; Yadav, Meenakshi; Khare, Akhilendra; Vidushi

doi:10.47974/JIM-1819

Open Access Research Article

Statistic analysis of IPL match score and winner inning wise using machine learning algorithms

^*PreetyCorresponding authorsunnypreety83@gmail.comDepartment of Computer ScienceChrist UniversityGhaziabad, Uttar Pradesh, IndiaView full profile → , Sachin Kumar Rayravsachin2002@gmail.comDepartment of Mathematics and StatisticsDr. Akhilesh Das Gupta Institute of Technology & ManagementDelhi, IndiaView full profile → , Meenakshi Yadavyadavmeenakshi@gmail.comDepartment of Information TechnologyGalgotias College of Engineering and TechnologyUttar Pradesh, IndiaView full profile → , Akhilendra Kharea.khare0007@gmail.comDepartment of Computer Science and EngineeringGalgotias UniversityUttar Pradesh, IndiaView full profile → , Vidushividushi@christunieristy.inDepartment of Computer ScienceChrist UniversityGhaziabad, Uttar Pradesh, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 245–258Vol. 27Issue 2March 2024DOI: 10.47974/JIM-1819XML

Published Online:: 19 Mar 2024
Article type:: Research Article
Language:: EN
Article no.:: JIM-1819
Pages:: 245–258

Abstract

This study explains the statistical analysis of cricket match score prediction using machine learning. According to recent changes in data science and sports, the use of sports-based machine learning and data mining shows the importance of process in outcome performance and prediction. The scope of this research paper is to evaluate current measurements used in the previous work to understand the estimation the ways used to model and analyze data and characterize the variables that govern performance using statistical methods. Actually, this research article will present a reliable statistical tool for data analysis using machine learning algorithms. At present, sports organizations produce enough statistical information on every player, team, match, and season for particular related sports. The first sports researchers were thought to be experts, coaches, team managers, and analysts. Sports organizations want to do statistical analysis of player from their previous data stored on their database using different data mining and machine learning algorithms. Sports data helps coaches and managers in many ways, such as predicting results, analyzing player performance, and skills, and evaluating strategies. Forecasts help managers and organizations make decisions to win teams and competitions. The current evaluation of research shows that primary studies of data mining systems can predict outcomes and evaluate the strengths and weaknesses of each system. Statistical analyses are made for each match for result predictions. Although in many respects this application is very limited. These are prime factors which important to examine machine learning algorithms in these situations to see if the application can give the nearest results in analysis. This research aims to give solutions that will help to make predictions more accurate and precise than previous methods, using more accurate data and machine learning.

Keywords

IPL score prediction Machine learning Data analysis Predictive modeling Cricket analytics

Subject Classifications

62M1065C60

References

[1] Witten, I. H., & Frank, E., Data mining: Practical machine learning tools and techniques. In BioMedical Engineering OnLine ISBN 0-12-088407-0, 5, 51 (2006). https://doi.org/10.1186/1475-925X-5-51
[2] Sharma, A., & Saini, R., IPL score prediction using machine learning techniques. International Journal of Computers and Applications, 180(2), 13–17 (2018).
[3] Kulkarni, P., & Gokhale, A., Cricket score prediction using ensemble machine learning techniques. International Journal of Advanced Research in Computer Science, 10(5), 220–225 (2019).
[4] Awan, M. J., Gilani, S. A. H., Ramzan, H., Nobanee, H., Yasin, A., Zain, A. M., & Javed, R.,” Cricket Match Analytics using Big Data Approach”. 10, 2350 MDPI. In Journal of Electronics, 10192350 (2021). https://doi.org/10.3390/electronics.
[5] ernandes, T., Camerino, O., & Castañer, M., T-Pattern Detection and Analysis of Football Players’ Tactical and Technical Defensive Behaviour Interactions: Insights for Training and Coaching Team Coordination. In Frontiers in Psychology, 12, 798201 (December 6, 2021). https://doi.org/10.3389/fpsyg.2021.798201
[6] García-Aliaga, A., Marquina, M., Coterón, J., Rodríguez-González, A., & Luengo-Sánchez, S., In-game behaviour analysis of football players using machine learning techniques based on player statistics. International Journal of Sports Science and Coaching, 16(1), 148–157 (2021). https://doi.org/10.1177/1747954120959762.
[7] Sanjeeva, P., Varma, J. A., Sathvik, V., Ratan, A. A. S., & Mishra, Sanjan. Automated Cricket Score Prediction. In. E3S Web of Conferences 15th international conference on Materials Processing and characterization (ICMPC 2023) Web of Conference 430, 01053, Page number 7, 430 (October 6, 2023). https://doi.org/10.1051/e3sconf/202343001053.
[8] Singh, H., & Grover, A., Predicting IPL scores using LSTM and random forest regression. In 4th International Conference on Intelligent Computing and Control. Systems (ICICCS), pp. 358–362 (2020). IEEE Publications.
[9] Jaiswal, A., & Singh, S. (2021). IPL score prediction using XGBoost algorithm. In International Conference on Information Technology (ICIT), pp. 1–5 (2021). IEEE Publications.
[10] Gupta, M., & Khandelwal, M., IPL score prediction using deep learning techniques. In 2021 International Conference on Innovative Computing and Communication (ICICC), pp. 1-5 (2021). IEEE.
[11] C, Srikantaiah & Khetan, Aryan & Kumar, Baibhav & Tolani, Divy & Patel, Harshal. Prediction of IPL Match Outcome Using Machine Learning Techniques (2021).
[12] Singh, A., & Verma, V. Year: IPL Score Prediction Using Machine Learning Techniques” International Conference on Smart Electronics and Communication (ICOSEC) (2020).
[13] Sharma, S, and rani, N. Year., IPL Score Prediction Using Ridge Regression International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE) (2018).
[14] Indika, W. r., ISSN: 2666-8270. singh,“Application of machine learning in cricket: A systematic review”,. In internal journal of Machine Learning with Application. Vol., 10, Pages -100435 (2022). https://doi.org/10.1016/j.mlwa.2022.100435.
[15] Title: “IPL Score Prediction using Machine Learning Techniques: A Comparative Study” Authors: Yadav, N., et al. Year: 2020 Journal/Conference: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)
[16] Title: “A Study on IPL Score Prediction Using Machine Learning Techniques” Authors: Sharma, S., et al. Year: 2019 Journal/Conference: 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU).
[17] Title: “Machine Learning-based Prediction of Indian Premier League (IPL) Match Outcomes” Authors: Kannan, S., et al. Year: 2020 Journal/Conference: 2020 IEEE Region 10 Symposium (TENSYMP).
[18] Title: “IPL Score Prediction using Machine Learning Techniques” Authors: Raj, R., et al. Year: 2020 Journal/Conference: 2020 International Conference on Computing, Power and Communication Technologies (GUCON).
[19] Rabindra Lamsal and Ayesha Choudhary, “Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning”, arXiv:1809.09813 [stat.AP] (September 2018).
[20] Singh, P., Agarwal, V., & Goel, P., IPL Match Result and Score Prediction: An Approach using Machine Learning. In 2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC), pp. 424-429 (2020). IEEE.
[21] Kaggle IPL Dataset: Kaggle is a platform that hosts various datasets and machine learning competitions. The Kaggle IPL dataset contains historical data of IPL matches, including ball-by-ball details, player performances, and match outcomes. You can use this dataset to build and train your own IPL score prediction model. You can find the dataset here: https://www.kaggle.com/nowke9/ipldata.
[22] GitHub repositories: There are several open-source projects and code repositories on platforms like GitHub that provide IPL score prediction models. Searching for “IPL score prediction machine learning” on GitHub will yield multiple repositories with code and examples to guide you.
[23] Stefan M. Stefanov. Numerical solution of some systems of nonlinear algebraic equations, Journal of Interdisciplinary Mathematics, 24:6, 1545-1564 (2021), DOI: 10.1080/09720502.2020.1833462.
[24] Md. Ashraful Babu, Md. Mortuza Ahmmed, Amena Ferdousi, M. Mostafizur Rahman, Md. Saiduzzaman, Vaibhav Bhatnagar, Linesh Raja & Ramesh Chandra Poonia. The mathematical and machine learning models to forecast the COVID-19 outbreaks in Bangladesh, Journal of Interdisciplinary Mathematics, 25:3, 753-772 (2022), DOI: 10.1080/09720502.2021.2015095.
[25] Kumar, Anil , Gupta, Amit Kumar , Panwar, Deepak , Chaurasia, Sandeep & Goyal, Dinesh. Operating system security with discrete mathematical structure for secure round robin scheduling method with intelligent time quantum, Journal of Discrete Mathematical Sciences and Cryptography, 26:5, 1519–1533 (2023).
[26] Kushwaha, Satpal Singh, Joshi, Sandeep & Gupta, Amit Kumar. An efficient approach to secure smart contract of Ethereum blockchain using hybrid security analysis approach, Journal of Discrete Mathematical Sciences and Cryptography, 26:5, 1499–1517 (2023), DOI: https://doi.org/10.47974/JDMSC-1815.
[27] Joshi, Ruchi, Mathur, Priya, Gupta, Amit Kumar, Singh, Suyesha, Paliwal, Vismita & Nayar, Sejal. Mathematical modeling of intelligent system for predicting effectiveness of premenstrual syndrome, Journal of Interdisciplinary Mathematics, 26:3, 551-562 (2023).

Views: 343Downloads: 60Citations: 0