Venue-Specific Probabilistic Modeling of High Run Chases in T20I Cricket: A Bayesian Network Approach

Abstract

This study investigates the impact of venue-specific factors on high run chases in T20 International (T20I) cricket using Bayesian Networks (BNs) derived from Gaussian Graphical Models (GGMs). Analyzing data from 458 high-scoring matches between 2005 and 2024, the study incorporates variables such as toss outcomes, pitch conditions, team rankings, and match results to construct probabilistic models tailored to home, neutral, and away venues. Regularization through Graphical Lasso ensured sparsity, yielding interpretable networks with optimal complexity.

Results reveal that venue conditions significantly influence dependency structures, with away venues exhibiting denser interdependencies and requiring greater adaptability. Critical factors identified include Toss Outcome (TO), Toss Decision (TD), and Pitch Conditions (PC), while Result (R) consistently emerged as the most influential variable across all venues. Networks with 16 edges provided the best balance of fit and simplicity, validated through posterior probabilities.

These findings highlight the importance of venue-specific strategies for optimizing high run chases in T20I cricket. The study advances the application of probabilistic modeling in cricket analytics, offering actionable insights for teams and decision-makers. Future research could integrate additional factors, such as player metrics and weather conditions, to refine predictive models further and expand their applicability across different cricket formats.