Real-time forecasting within soccer matches through a Bayesian lens published in Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 187, Issue 2, April 2024. (with Soudeep Deb & Rishideep Roy) (ArXiv) (Journal)
Abstract :
This paper employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilise a multinomial probit regression in a novel framework to estimate the time-varying impact of covariates and to forecast the outcome. English Premier League data from eight seasons are used to evaluate the efficacy of our method. Different evaluation metrics establish that the proposed model outperforms potential competitors inspired by existing statistical or machine learning algorithms. Additionally, we apply robustness checks to demonstrate the model’s accuracy across various scenarios.
Presentations:
August 2024: Joint Statistical Meetings, Portland, USA (Accepted)
December 2023: 3rd All India Research Conference, IIM Lucknow, India
November 2023: Indian Institute of Management Bangalore (IIMB) Decision Sciences Area Brown Bag Seminar
Efficiency of live-betting markets in tennis (with Rishideep Roy & Soudeep Deb)
Abstract :
Tennis, like most professional sports, has its own set of well-known betting strategies. In-game strategies for betting are a new area of research for any sport, with some development in the past ten years. This chapter attempts to solve this problem and fill the gap in the extant literature by proposing a Markov Decision Process (MDP) framework that acts as a recommender system for within-game betting in tennis. The aim of the proposed methodology is two-fold. Firstly, the model provides a bet/no-bet recommendation for each player after every game played during a tennis match. Secondly and more crucially, the framework also recommends an optimal fraction of the total amount to bet at that stage of the match. We then use real bookmaker odds data to demonstrate the effectiveness of the presented methodology.
Presentations:
June 2025: 11th MathSport International Conference, Luxembourg
June 2025: 34th European Conference on Operational Research, Leeds, UK (Accepted)
August 2025: Joint Statistical Meetings, Nashville, USA (Accepted)
December 2025: 2025 IMS International Conference on Statistics and Data Science (ICSDS), Seville, Spain
In-game betting in soccer: An application in modern portfolio theory (with Rishideep Roy)
Abstract :
The article considers in-game betting in soccer, formulating the problem as one of Kelly allocation under uncertainty. In contrast to ideal betting models that assume frictionless or perfectly fair odds, the analysis treats in-game odds as exogenous, time-varying quantities that may embed vigs, asymmetries and other market frictions. Betting decisions are made sequentially over the course of a match, subject to evolving budget constraints and uncertain returns.