Markov Cricket: Using Forward and Inverse Reinforcement Learning to Model, Predict And Optimize Batting Performance in One-Day International Cricket

TL;DR

This paper models one-day international cricket as a Markov process and employs forward and inverse reinforcement learning to predict, optimize, and simulate batting performance, outperforming existing methods and offering new strategic insights.

Contribution

It introduces three novel RL-based tools for cricket analysis: a value function approximation, an inverse reward inference, and a game simulator, advancing strategic modeling in sports.

Findings

Outperforms Duckworth-Lewis-Stern method by 3-10 times in resource estimation

Infers reward models that align with expert and winning team performances

Provides a simulation framework for predicting scores under different strategies

Abstract

In this paper, we model one-day international cricket games as Markov processes, applying forward and inverse Reinforcement Learning (RL) to develop three novel tools for the game. First, we apply Monte-Carlo learning to fit a nonlinear approximation of the value function for each state of the game using a score-based reward model. We show that, when used as a proxy for remaining scoring resources, this approach outperforms the state-of-the-art Duckworth-Lewis-Stern method used in professional matches by 3 to 10 fold. Next, we use inverse reinforcement learning, specifically a variant of guided-cost learning, to infer a linear model of rewards based on expert performances, assumed here to be play sequences of winning teams. From this model we explicitly determine the optimal policy for each state and find this agrees with common intuitions about the game. Finally, we use the inferred reward models to construct a game simulator that models the posterior distribution of final scores under different policies. We envisage our prediction and simulation techniques may provide a fairer alternative for estimating final scores in interrupted games, while the inferred reward model may provide useful insights for the professional game to optimize playing strategy. Further, we anticipate our method of applying RL to this game may have broader application to other sports with discrete states of play where teams take turns, such as baseball and rounders.