How Is Data Science Changing Cricket Betting in India?

The limitations of intuition

Betting on cricket in India has long been guided by intuition rather than any type of systematic analysis. Bettors typically rely on a combination of factors such as their match observation, personal judgment or informal advice when making their decisions before placing a bet.

Given how cricket has traditionally been discussed and analysed in India these approaches are. However, these methods have a common limitation: they struggle to account for the complexity of factors that actually determine match outcomes in reality. For example, a team on a winning streak may seem to be dominant, but closer examination might reveal that those victories came against weaker opposition on favourable home pitches.

Research in psychology shows that a human’s working memory can typically only hold four to five variables simultaneously, whereas a cricket match involves multiple of potentially relevant factors such as batting and bowling averages, pitch behaviour, weather conditions, injury concerns, toss results, home advantage, recent form, head-to-head records, player fatigue and numerous contextual elements. Processing these variables together, while also accounting for their interactions, exceeds what intuitive judgment can reliably accomplish.

This gap is what machine learning methods are increasingly being developed to address where unlike human cognition, algorithm-based approaches can process large numbers of variables simultaneously and identify patterns that may not be apparent through conventional analysis.

A new approach to prediction

A recent research by Vishal Shaha of T.C. College of Arts, Science and Commerce in Baramati, Pune, and Dr. Jyotshna Dongardive of the University of Mumbai has examined whether machine learning techniques can outperform traditional prediction methods in the context of betting on the Indian Premier League (IPL). Their study, which was published by Springer in 2026 as part of the Proceedings of the International Conference on Paradigms of Communication, Computing and Data Analytics, drew on fifteen years of Indian Premier League match data to develop and evaluate predictive models.

The researchers compiled a substantial dataset covering 959 IPL matches from 2008 to 2023, which incorporated performance data on 171 individual players which was sourced from reliable repositories of cricket statistics ESPNcricinfo and CricMetric.

Shaha and Dongardive constructed two distinct predictive models for their analysis. The first model, known as the Team Performance Model, was designed to predict match outcomes at the team level. This model incorporated seven input variables: the batting average of team A, the bowling average of team B, venue classification, toss outcome, the presence of key player injuries, prevailing weather conditions and whether the team was playing at home or away.

The second model, Player Performance Model, focused on predicting the individual player’s performance, specifically batting averages. This model incorporated 21 variables which includes demographic factors such as age, career statistics including total matches and innings played, current form indicators such as strike rate and runs scored, historical performance data from the previous season covering batting average, strike rate, runs, centuries and fifties, bowling metrics for all-rounders including average, economy rate and wickets, injury history and batting position in the lineup.

For both models, the researchers employed Random Forest which is an ensemble learning algorithm that operates by constructing multiple decision trees during training and combining their outputs to produce final predictions.

Findings and performance

The Team Performance Model achieved 95% accuracy and when it was evaluated against 20 test cases predicting 19 correctly. The model demonstrated strong performance across standard classification metrics with precision, recall and F1 scores all reaching 0.95.

Analysis of the confusion matrix shows that the model correctly classified 8 losses and 11 wins, with only a single misclassification where an actual loss was predicted as a win. For betting on cricket, these results indicate a meaningful improvement over existing approaches and increases the return.

The Player Performance Model showed an even stronger predictive capability where it archived an R-squared value of 99.74% with a mean squared error of just 0.065. Such a high value here suggests that the model captured nearly all of the meaningful signals that were present in the player performance data.

In the context of the previous research which did not show the same level of correct responses, the results become even more significant. Shaha and Dongardive also conducted a review of 18 previous studies on cricket betting prediction published between 2017 and 2024. The highest accuracy achieved in any of these earlier prediction models was approximately 80%, with most falling in the range of 65 to 75%. Decision tree approaches reached around 72% accuracy, while previous Random Forest implementations achieved between 67 and 78%. Support vector machines and logistic regression models generally produced accuracy rates of 70 to 75 %. The improvement from 80 to 95% accuracy shows a substantial advancement in prediction capabilities rather than an incremental increase.

Explaining the improvement

The researchers attribute the performance gains in their prediction model to their selection of input variables. The earlier prediction models for cricket relied mainly on static statistics such as career batting averages, historical team win rates and head-to-head records. While these metrics contain useful information, they do not capture factors that vary from match to match.

Shaha and Dongardive incorporated several dynamic variables that earlier researchers had overlooked: toss outcome, weather conditions, venue-specific effects and current injury status. The toss can prove decisive on pitches which change character as matches progress, weather conditions change how the ball performs, and the absence of a key player due to injury can substantially alter the competitive balance of the team. This research shows the importance of these factors instead of ignoring them for the prediction framework.

The Player Performance Model extended this approach by tracking form trajectories over time, comparing current season statistics against previous season performance to identify players whose output was improving or declining. The model also accounted for batting position, recognising that the expectations and conditions faced by opening batsmen differ meaningfully from those encountered by middle-order players or finishers.

Broader applications

The implications of this research extend beyond betting applications. Cricket analysts and commentators can use this template for developing evidence-based prediction methodologies. Sports media organisations could use such models for their pre-match coverage, offering analysis based on statistics instead of  subjective assessment alone.

The researchers themselves identify several promising directions for future investigation such as the development of real-time prediction systems capable of updating projections as matches progress, incorporating new information about wickets, scoring rates and momentum shifts. They also suggest extending the methodology to other T20 competitions such as the Big Bash League, Pakistan Super League and Caribbean Premier League, as well as to ODI and Test cricket formats.

A third option could be creating accessible tools that would allow non-specialists to benefit from these predictive capabilities.

Limitations and scope

As with any research, this study has important limitations that should be acknowledged. The dataset concludes in 2023, meaning that the most recent IPL seasons are not represented and the evaluation was conducted on historical match data rather than through live prediction, where conditions differ and unexpected developments can also influence outcomes. The models do not currently incorporate real-time variables such as momentum shifts during play, injuries sustained during matches or changes in pitch behaviour as conditions evolve.

Furthermore, the models were trained exclusively on IPL data and have not been validated against international matches, other domestic T20 competitions or longer formats of the game. Cricket is a context-sensitive  game where pitch characteristics in Chennai differ markedly from those in Melbourne or Bridgetown, and playing conditions that advantage a team at home may disadvantage them in away fixtures. The regulatory environment for cricket betting in India also presents its own complexities, with legal frameworks varying across states and platforms.

Any discussion of cricket betting prediction must also acknowledge the legal landscape of online gambling in India. The Promotion and Regulation of Online Gaming Act, 2025 now classifies activities involving monetary stakes as regulated online money gaming. This restricts most forms of online sports betting at the central level. Additionally, since gambling still remains a state subject under the Indian Constitution, laws vary considerably across different states. Readers interested in the practical and legal implications should consult detailed resources on Indian betting regulations before considering any application of predictive models in this domain.

Conclusion

Cricket has always been a sport that has been amenable to statistical analysis. Batting averages, strike rates, bowling economy figures and net run rates have long formed part of how the game is discussed and evaluated. What machine learning methodologies add the capacity to examine these metrics in a combination, identify interactions and patterns across numerous variables simultaneously. For those betting on cricket, the prediction model represents the evolution in how decisions for predictions might be approached.

The research conducted by Shaha and Dongardive forms part of a broader movement toward data-driven decision-making in sport not only just cricket. Football clubs increasingly employ analytical methods for recruitment and tactical planning, basketball organisations track player positioning with high precision, and motorsport teams conduct extensive computational modelling before races. India, where cricket us still a young and growing sport, the growth through data science will contribute to this trajectory.

It would be premature to suggest that approaches based on algorithms would be better than human judgment in understanding cricket. Models are analytical tools instead of comprehensive observers of the game. They cannot account for factors such as disagreement in the dressing room,  change in pitch behaviour after a rain delay, or the psychological pressure of a crucial playoff match. However, better analytical tools enable better-informed decisions. Whether applied to IPL betting, fantasy cricket participation or simply a deeper appreciation of the sport, the direction of travel points toward more systematic, evidence-based engagement with the game.

References

Shaha, V., & Dongardive, J. (2026). Cricket betting through machine learning: Enhancing decision-making. In H. Mittal, S. J. Nanda, & M.-H. Lim (Eds.), Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics (PCCDA 2025, Volume 2) (pp. 271-285). Springer Singapore. https://doi.org/10.1007/978-981-96-6847-2_21

Singh, A., Gupta, R., & Verma, S. (2021). Predicting the outcome of cricket matches using machine learning techniques. In IEEE 2nd International Conference on Computing, Communication, and Automation (ICCCA) (pp. 155-160). IEEE.

Galekwa, R. M., Tshimula, J. M., Tajeuna, E. G., & Kyandoghere, K. (2024). A systematic review of machine learning in sports betting: Techniques, challenges, and future directions. arXiv preprint. https://doi.org/10.48550/arXiv.2410.21484

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