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

arXiv: https://arxiv.org/abs/2103.04349 · HTML: https://mlsanity.com/paper/2103.04349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Reward modeling and RLHF](https://mlsanity.com/alignment/reward-modeling-and-rlhf/)
- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 3 of 3)

- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research](https://mlsanity.com/paper/1910.08639/) (2019)
- [Testing match-3 video games with Deep Reinforcement Learning](https://mlsanity.com/paper/2007.01137/) (2020)

## Abstract (arXiv)

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.