Dr Rishideep Roy

School of Mathematics, Statistics and Actuarial Science (SMSAS)
Dr Rishideep Roy



I am a Lecturer in the School of Mathematics, Statistics, and Actuarial Science at the University of Essex, Colchester Campus. Before this, I was an Assistant Professor of Decision Sciences at the Indian Institute of Management Bangalore (IIMB). I have completed my PhD from the Department of Statistics at the University of Chicago. My thesis was on Extreme values of log-correlated Gaussian fields. Before that, I studied at the Indian Statistical Institute Kolkata, completing my Bachelor's with Honours and Master's in Statistics. I have used some of my works in predicting the results of elections and predicting market shares of brands, under the effect of market churns. I have also used voter models to detect and identify fraud in elections and provide a framework to assess the effect of campaigns on the outcomes of elections. I am also interested in sports analytics and have worked on the prediction of the outcome of games through data on the events on the field of a football match. I have furthered these works to work on frameworks for sports bettings in football, and how portfolio optimization can be used to refine these frameworks. I have also worked on basketball, and strategies for effective replacements during a match, based on the events on the court. I have also worked on cricket and the use of classification methods for time series to predict the outcome of games. In statistics lingo, I have worked on Bayesian methods in categorical time series and continuous forecasting based on partial information. I have worked on logarithmically correlated fields, encompassing the discrete Gaussian free field, Gaussian membrane model, branching random walks, etc. Some of these have been used for modeling financial data, and have been shown to act as better models compared to more popular ones like geometric Brownian models. I have also worked on interacting particle systems, particularly the Frog model, Rumour model, and voter models.


  • PhD in Statistics University of Chicago, (2016)

  • Masters in Statistics Indian Statistical Institute, (2011)

  • Bachelors in Statistics with Honours Indian Statistical Institute, (2009)


University of Essex

  • Lecturer, School of Mathematics, Statistics and Actuarial Science, University of Essex (1/1/2024 - present)

Other academic

  • Assistant Professor, Indian Institute of Management Bangalore (19/9/2016 - 30/9/2023)

Research and professional activities

Research interests

Bayesian Statistics

The idea behind Bayesian statistics is making use of previously acquired knowledge as well as recent data to make better predictions. Sometimes we might take into account only the recent data, it might not capture previous or broader insights that one might have on the underlying phenomenons or models. The recent data might just be off. Or might happen that the basic nature of the underlying phenomenon has changed, and so, making a prediction based purely on previous insights is not effective. But Bayesian models bring previous knowledge into play and also take into account recent data to give an effective combined prediction. We have used these techniques in various applications like market shares of brands going through market churn, prediction of election results, and prediction of outcome of games.

Key words: Bayesian Methods
Open to supervise

Sports Analytics

I have been interested in sports analytics in general, and have worked on various sports like football, cricket, basketball, and tennis. I have worked on various projects, like predicting the outcome of a football match based on the events on the field, like goals, fouls, cards, corners, challenges, etc. We have also been working on refining this based on live commentaries and sounds captured in the live recording of the games. We have also been working on effective strategies for betting companies as well as their customers to find an optimal time to enter the bets, as well as decisions on whether to enter them or not. We have also been working on within-game substitution strategies in basketball to optimize the skills and form of players. We have further used sports analytics techniques to find the best sparting line up in cricket, given a fixed pool of cricketers, as well as strategies for business franchises in auctions for an optimised team selection, to buy and sell players.

Key words: Financial aspects of Sports
Open to supervise

Stochastic Processes

Log-correlated Gaussian fields are Gaussian processes with wide applications, from models in physics to financial data. Log-correlated Gaussian fields have been observed in a wide range of mathematical models, for example, the Random energy model, the Gaussian multiplicative chaos, as well the Circular Unitary Ensemble (CUE) of random matrix theory. They also appear in the maximum of Riemann Zeta function in number theory. They also have strong relations with Louville Quantum Gravity.

Key words: Branching Random Walk
Open to supervise

Interacting Particle Systems

I have worked on various stochastic models in the class of interacting particle systems. More popularly, some of them have been used to study spread of diseases, with various natures of diseases being studied by different models. One assumption about them is that these diseases spread through contact. In some, the diseased individual can spread it indefinitely, in some others they can recover and don't spread it anymore. In some models not everyone coming in contact with a disease will get infected. I have worked on various forms of these models. A very similar model in this regard is a model on spread of rumours, and how quickly they can flow.

Key words: Disease spread models
Open to supervise

Election Analytics

Elections in modern days are one of the most effective ways for citizens to voice their opinions. With democracy being the most common vehicle of governance, the utility of elections is ever-increasing. So, the process of electioneering has also generated more interest. We have used analytics in the prediction of elections, based on partial voting information. We have also developed techniques which can be used to detect electoral fraud. Similar techniques can be used to assess the effect that campaigns might have on the final electoral outcome, and whether they could tilt the result from one party's favor to the other or not.

Key words: Election Prediction
Open to supervise

Conferences and presentations

Co-existence in probability models

Invited presentation, 2022 IISA Conference, Bengaluru, India, 29/12/2022

Effect of media attention on crude oil price volatility using a non-parametric time series regression.

Invited presentation, 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022), London, United Kingdom, 18/12/2022

Effect of media attention on crude oil price volatility using a non-parametric time series regression.

Invited presentation, IMS International Conference on Statistics and Data Science (ICSDS), Florence, Italy, 15/12/2022

Coexistence in discrete time multi-type competing frog models

IMS Annual Meeting, June 2022, London, United Kingdom, 28/6/2022

Teaching and supervision

Current teaching responsibilities

  • Data Visualisation (MA304)

  • Mathematics Careers and Employability (MA199)


Publications (2)

Roy, R. and Saha, K., (2023). How fast do rumours spread?

Paul, M., Roy, R. and Deb, S., (2022). Effect of influence in voter models and its application in detecting significant interference in political elections

Journal articles (12)

Roy, R., (2024). A branching random walk in the presence of a hard wall. Journal of Applied Probability. 61 (1), 1-17

Divekar, C., Deb, S. and Roy, R., (2024). Real-time forecasting within soccer matches through a Bayesian lens. Journal of the Royal Statistical Society Series A: Statistics in Society. 187 (2), 513-540

Deb, S., Roy, R. and Das, S., (2024). Forecasting Elections from Partial Information Using a Bayesian Model for a Multinomial Sequence of Data. Journal of Forecasting

Roy, R. and Saha, K., (2021). Coexistence in discrete time multi-type competing frog models. Electronic Communications in Probability. 26 (1), 1-9

Ding, J., Roy, R. and Zeitouni, O., (2017). Convergence of the centered maximum of log-correlated Gaussian fields. The Annals of Probability. 45 (6A), 3886-3928

Roy, R. and Roy, D., (2014). The Lack of Memory Property in the Density Form for the Bivariate Setup. Communications in Statistics - Theory and Methods. 43 (14), 2859-2869

Roy, D. and Roy, R., (2013). Stability of the Characterization Results in Terms of Hazard Rate and Mean Residual Life for the Univariate and Bivariate Setups. Communications in Statistics - Theory and Methods. 42 (9), 1583-1598

Roy, D. and Roy, R., (2013). Distribution of the Activity Time in Network Analysis: A Critical Revisit with a Gamma Alternative. Communications in Statistics - Simulation and Computation. 42 (6), 1288-1297

Sengupta, A. and Roy, R., (2011). Testing Homogeneity of Mean Directions for Circular Dispersion Models. Calcutta Statistical Association Bulletin. 63 (1-4), 141-156

Roy, D. and Roy, R., (2010). A bivariate exponential distribution based on linear regression approach. Journal of Applied Statistical Science. 18 (3), 327-340

Roy, R. and Roy, D., (2009). Reliability analysis: A curve of concentration approach. Journal of Applied Statistical Science. 17 (2), 235-245

Roy, D. and Roy, R., (2009). Characterizations of Bivariate and Multivariate Life Distributions Based on Reciprocal Subtangent. Communications in Statistics - Theory and Methods. 39 (1), 158-169

Book chapters (1)

Roy, R. and Roy, D., (2012). Reliability analysis: A curve of concentration approach. In: New Developments in Applied Statistics. 251- 262. 9781613246481

+44 (0) 1206 876232


2.410, Colchester Campus