Simulation-Based Optimisation of Batting Order and Bowling Plans in T20 Cricket

TL;DR

This paper introduces a Markov Decision Process framework for optimizing batting and bowling strategies in T20 cricket, using detailed player profiles and Monte Carlo simulations to maximize win and defend probabilities.

Contribution

It presents a novel unified MDP approach that considers phase-specific player profiles and directly optimizes win and defend probabilities, improving strategic decision-making in cricket.

Findings

Optimal batting order increased Mumbai Indians' win probability by 4.1%.

Optimized bowling plan improved Gujarat Titans' defend probability by 5.2%.

Phase-specific deployment decisions outperform aggregate metrics in cricket strategy.

Abstract

This paper develops a unified Markov Decision Process (MDP) framework for optimising two recurring in-match decisions in T20 cricket, namely batting order selection and bowling plan assignment, directly in terms of win and defend probability rather than expected runs. A three-phase player profile engine (Powerplay, Middle, Death) with James-Stein shrinkage (a technique that blends a player's individual statistics toward the league average when their phase-specific data is sparse) is estimated from 1,161 IPL ball-by-ball records (2008-2025). Win/defend probabilities are evaluated using vectorised Monte Carlo simulation over N = 50,000 innings trajectories. Batting orders are evaluated by comparing all feasible arrangements of the remaining players and selecting the one that maximises win probability. Bowling plans are optimised through a guided search over possible over assignments, progressively improving the allocation while respecting constraints such as the prohibition on consecutive overs by the same bowler. Applied to two 2026 IPL matches, the optimal batting order improves Mumbai Indians' win probability by 4.1 percentage points (52.4% to 56.5%), and the optimal Gujarat Titans bowling plan improves defend probability by 5.2 percentage points (39.1% to 44.3%). In both cases, the observed sub-optimality is consistent with phase-agnostic deployment: decisions that appear reasonable under aggregate metrics are shown to be costly when phase-specific profiles are applied.