Decentralised Cricket Strategy Analysis System

Abstract

The integration of data science methodologies, which provide deep insights into player performance, match dynamics, and predictive analytics, has reshaped the cricket strategy landscape. At the same time, blockchain embodies a paradigm shift toward secure, transparent data management in many sectors. This paper discusses possible synergies between these two domains in the context of cricket strategy management. Concretely, it describes the development of a Python-based application that combines data-driven generation of cricket strategies from historical match data with their secure storage and sharing using a simulated multi-node blockchain. After touching upon the architecture of the application regarding role-based access for coaches and players, data analysis towards strategizing, and persistence through blockchain, the results also highlight the potential of this integrated approach to enhance security, transparency, and accessibility of strategic information in cricket, therefore opening further avenues in the domain of sports analytics and technology.

Introduction

Modern cricket has become increasingly data-driven, with teams relying on analytics to optimize tactics, player matchups, and game-time decisions. However, while data science enables sophisticated strategy generation, securely storing and sharing these sensitive insights remains a challenge. Traditional storage methods lack integrity, transparency, and controlled access. To address this, the proposed system integrates a Python-based cricket strategy analytics engine with a simulated blockchain ledger, ensuring secure, immutable, and auditable management of strategic information.

The system offers four key contributions:
(1) generating context-aware cricket strategies using historical match data;
(2) implementing a simulated blockchain framework for secure and transparent storage;
(3) analyzing the strengths and limitations of this combined approach; and
(4) demonstrating how such an integrated method can support cricket teams and broader domains that require protected strategic information.

The literature review shows extensive research on machine learning, big data, deep learning, and predictive analytics in cricket, covering areas such as score prediction, player evaluation, match outcome forecasting, video-based shot recognition, and multivariate performance assessment. These studies emphasize how data science and AI enhance competitive advantage in cricket through analysis, prediction, and decision support. However, none address secure storage or controlled distribution of strategy—highlighting the need filled by this work.

The methodology follows four stages:
(1) Data Acquisition & Preprocessing, where historical match data is cleaned, numerically normalized, and filtered dynamically based on selected batsman, bowler, and venue;
(2) Strategy Generation, where detailed insights—such as head-to-head statistics, venue trends, matchups, tactical suggestions, and form analysis—are produced using pandas-based aggregation and rule-based interpretation;
(3) Insight Computation, which performs multi-layered statistical evaluations covering H2H metrics, venue-specific records, style matchups, and overall form; and
(4) Blockchain Integration & Interface, where strategies are securely stored in a simulated distributed ledger and presented to users through a Streamlit interface.

Conclusion

In this research we combined data science techniques for creating cricket strategies with a simulated blockchain for secure data management. The Python based application developed in this project serves as a practical example of this idea, offering role based access for coaches and players. It generates data driven strategies using past cricket data and securely storing them in a tamper proof blockchain simulation. This approach shows how technology can make managing team strategies safer and more efficient, offering an improvement over traditional methods.

References

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Copyright

Copyright © 2025 Dr. Kavita A. Sultanpure, Nidhi Karkera, Prajakta Kumbhare, Mandar Mahapure, Jay Kumawat, Ameya Pathak. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.