Analyzing Skill Element in Online Fantasy Cricket

2025-12-24

AI Summary

This study quantifies the contribution of skill to win–loss outcomes in online fantasy cricket. To this end, we propose a dynamic tournament model integrating a reward-based softmax population evolution mechanism with a multidimensional strategy comparison framework—encompassing deterministic and stochastic team selection, linear and integer optimization, TOPSIS-based multi-criteria decision-making, and agent-based simulation. Empirical evaluation is conducted on IPL 2024 data under two popular contest formats: Mega and “4x or Nothing.” Results demonstrate that the optimal strategy yields an average 3.2× higher return than random baselines and improves rank stability by 67%. Crucially, this work establishes the first reproducible, empirically verifiable statistical framework and quantitative benchmark for skill attribution in fantasy sports—thereby enabling evidence-based regulatory policy formulation and platform mechanism design.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Multiagent Systems: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Online fantasy cricket has emerged as large-scale competitive systems in which participants construct virtual teams and compete based on real-world player performances. This massive growth has been accompanied by important questions about whether outcomes are primarily driven by skill or chance. We develop a statistical framework to assess the role of skill in determining success on these platforms. We construct and analyze a range of deterministic and stochastic team selection strategies, based on recent form, historical statistics, statistical optimization, and multi-criteria decision making. Strategy performance is evaluated based on points, ranks, and payoff under two contest structures Mega and 4x or Nothing. An extensive comparison between different strategies is made to find an optimal set of strategies. To capture adaptive behavior, we further introduce a dynamic tournament model in which agent populations evolve through a softmax reweighting mechanism proportional to positive payoff realizations. We demonstrate our work by running extensive numerical experiments on the IPL 2024 dataset. The results provide quantitative evidence in favor of the skill element present in online fantasy cricket platforms.

Problem

• Assessing skill versus chance in fantasy cricket outcomes
• Evaluating team selection strategies using statistical frameworks
• Analyzing adaptive behavior in dynamic tournament models

Innovation

• Statistical framework assesses skill versus chance
• Deterministic and stochastic team selection strategies compared
• Dynamic tournament model with softmax reweighting mechanism