A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket

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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]14/09/2025 at 4:06 PM[/if 2224] | [if 1553 equals=””] Volume : 02 [else] Volume : [/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02 | Page :

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    Nagajayant Nagamani,

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  1. Student, Department of Physical Education, Chennai University,, Tamil Nadu, India

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Abstract

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nThis paper presents a data-driven framework for forecasting outcomes in One Day International (ODI) cricket matches using machine learning techniques. Leveraging a historical dataset of over 3,900 ODIs from 1971 to 2017, the study applies supervised classification algorithms—specifically Decision Trees and Multilayer Perceptron (MLP) networks—to predict match results based on pre-game contextual and performance features. Key input variables include team strength, toss outcome, venue, historical head-to-head statistics, and match type. Extensive feature engineering was conducted to enrich the dataset with innings-specific and venue-based insights. Model performance was evaluated using multiple metrics such as Accuracy, Precision, Recall, F1 Score, and Average Precision, offering a comprehensive view of each model’s predictive capability. The MLP model demonstrated robust classification performance, while the Decision Tree provided greater interpretability. To enable practical usage, a desktop application named CricAI was developed, offering real-time predictions based on user-input match details. This work not only highlights the viability of machine learning in cricket analytics but also provides a scalable blueprint for predictive modeling in structured sports. Future directions include enhancing temporal relevance, incorporating player-level metrics, and extending the approach to in-game predictions and other sports domains.nn

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Keywords: One Day International (ODI) cricket, Machine learning, Data-driven framework, Outcome prediction, Sports analytics, Forecasting models

n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Sports ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Recent Trends in Sports (rts)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nNagajayant Nagamani. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket[/if 2584]. Recent Trends in Sports. 13/09/2025; 02(02):-.

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How to cite this URL:
nNagajayant Nagamani. [if 2584 equals=”][226 striphtml=1][else]A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket[/if 2584]. Recent Trends in Sports. 13/09/2025; 02(02):-. Available from: https://journals.stmjournals.com/rts/article=13/09/2025/view=0

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[1] Swetha and Saravanan KN, ”Analysis on Attributes Deciding Cricket Winning”, International Research Journal of Engineering and Technol- ogy (IRJET), p-ISSN: 2395-0072, Volume: 04 Issue: 03 — March-2017
[2] Mehvish Khan and Riddhi Shah. ”Role of External Factors on Out- come of a One Day International Cricket (ODI) Match and Predictive Analysis”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 6, June 2015.
[3] ESPN Cricinfo, http://www.stats.espncricinfo.com
[4] Scikit learn, http://scikit-learn.org/stable/index.html
[5] Bailey and Clarke, Journal of Sports Science and Medicine, 2006, Vol. 5, pp. 480487.
[6] Neeraj Pathak and Hardik Wadhwa,”Applications of modern classi- fication techniques to predict the outcome of ODI Cricket”. 2016 International Conference on Computational Science.
[7] ”CRICKET SCORE PREDICTION SYSTEM (CSPS) USING CLUS-
TERING ALGORITHM”, Preeti Satao, Ashutosh Tripathi, Jayesh Vankar, Bhavesh Vaje, Vinay Varekar. International Journal Of Current Engineering and Scientific Research (IJCESR), 23940697, Volume-3, Issue-4, 2016.
[8] Parag Shah and Mitesh Shah, ”Predicting ODI Cricket Result”. Journal of Tourism, Hospitality and Sports, 2312-5179, Vol.5, 2015.
[9] Kaluarachchi, Amal, and S. Varde Aparna. ”CricAI: A classification based tool to predict the outcome in ODI cricket.” 2010 Fifth Inter- national Conference on Information and Automation for Sustainability. IEEE, 2010.
[10] Madan Gopal Jhanwar and Vikram Pudi, ”Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach”, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Report No: IIIT/TR/2016/-1, Conference Center, Riva del Garda.

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Volume 02
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received 12/07/2025
Accepted 02/09/2025
Published 13/09/2025
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Publication Time 63 Days

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