Oliver Stevenson


Hello, thanks for visiting!

I was formerly a PhD candidate in the Department of Statistics at the University of Auckland, where I spent my time investigating new statistical methods and models in the field of sports analytics. My research focussed on the development and implementation of statistical models that can be applied to sport of cricket, with the primary aim of estimating and forecasting the past, present, and future abilities of professional cricketers, and to predict the likely outcome of upcoming matches. These models have been proven to provide more accurate predictions of future player performance than traditional cricketing statistics, such as batting and bowling averages.

Since completing my doctorate in 2020, I have been working at Luma Analytics, a data and analytics consultancy based in Auckland, New Zealand.

Feel free to take a look at my past and present research below, or get in touch if you have any questions regarding statistics, data, cricket or otherwise.


Research interests:

  • Sports analytics
  • Bayesian inference
  • Machine learning
  • Computational statistics

Doctor of Philosophy (2017 - 2020)

Between 2017 and 2020 I completed a Doctor of Philosophy at the University of Auckland under the supervision of Dr Brendon Brewer. Following on from my Masters, my research involved developing a range of statistical models that can be applied to the analysis of sporting data, with a particular focus on cricket. As with any sport or profession, we shouldn’t expect a player to perform with some constant ability throughout their entire career. Instead, we are likely to observe both short and long-term variations and fluctuations in ability due to the likes of age, experience, fitness and general improvements or deteriorations in technique.

As part of my PhD research, I derived a range of statistical models that employ machine learning algorithms to better estimate and predict the past, present, and future batting and bowling abilities of professional cricketers. When estimating current player ability, these models account for a range of external factors, including recent form, strength of opposition, and venue of past performances (e.g. home or away). These estimates have been proven to provide more accurate predictions of player peformance, compared with traditional cricketing metrics, such as batting and bowling averages. Additionally, the models have the benefit of maintaining an intuitive cricketing interpretation, unlike other popular ranking metrics, such as the official ICC rankings. Given two proposed playing XIs, it is then possible to feed the estimated player abilities into a custom built match-simulation engine to provide a probabilistic prediction of either team winning a match.

Click here to read thesis titled “Form is temporary, class is permanent: Statistical methods for predicting the career trajectories and contributions of players in the sport of cricket”.

Master of Science (2016 - 2017)

In 2017 I completed my Masters degree under the supervision of Dr Brendon Brewer. My research looked to tell a more meaningful story behind a cricket player’s batting average. Using Bayesian statistical techniques, I explored more in-depth methods of quantifying a cricketer’s batting ability than the simple batting average. More specifically, I built statistical models which estimate how well a batsman is playing at any given point in their innings, allowing us to quantify the cricketing concept of a batsman ‘getting their eye in’. The primary focus was on Test match cricket, with wider applications to 4-day first-class cricket. Using these models, I also explored the plausibility of popular cricketing superstitions from a statistical point of view, such as the commentator’s favourite, the ‘nervous 90s’.

ABSTRACT: Cricketing knowledge tells us batting is more difficult early in a player’s innings, but gets easier as a player becomes familiar with the local conditions. Using Bayesian inference and nested sampling techniques, a model is developed to predict the Test match batting abilities of international cricketers. The model allows for the quantification of players’ initial and equilibrium batting abilities, and the rate of transition between the two. Implementing the model using a hierarchical structure provides more general inference concerning a selected group of international opening batsmen from New Zealand. More complex models are then developed, which are used to identify the presence of any score-based variation in batting ability among a group of modern-day, world-class batsmen. Additionally, the models are used to explore the plausibility of popular cricketing superstitions, such as the ‘nervous 90s’. Evidence is found to support the existence of score-based variation in batting ability, however there is little support to confirm a widespread presence of the ‘nervous 90s’ affecting player batting ability. Practical implications of the findings are discussed in the context of specific match scenarios.

Click here to read thesis titled “The nervous 90s: a Bayesian analysis of batting in Test cricket”.

Bachelor of Science (Honours) (2015)

ABSTRACT: At a glance, data is more meaningful when presented in graphical form. This project explored innovative methods of automating the display of catch data for large-scale conservation projects. High priority was given to developing methods that allow users to interact with their data, affording them some control over the graphics that are produced. Two interactive applications were developed that allow conservation volunteers to select the data they want to view and how to view it. After a day in the field, volunteers are able to use these applications to see their day’s work summarised on a map or graphic. These graphics highlight the positive impact their efforts are having on the local environment, keeping volunteers motivated and engaged in their work. Various methods of improving the automation of these graphics are outlined, as well as other practical uses of these statistical applications.

Click here to read dissertation titled “Graphical applications for large-scale conservation projects”.


Stevenson, O. G. (2021). Form is temporary, class is permanent: Statistical methods for predicting the career trajectories and contributions of players in the sport of cricket. Doctoral thesis, University of Auckland. Online version.

Stevenson, O. G., & Brewer, B. J. (2021). Finding your feet: A Gaussian process model for estimating the abilities of batsmen in Test cricket. Journal of the Royal Statistical Society: Series C (Applied Statistics), 70(2), 481-506. Online version.

Stevenson, O. G., & Brewer, B. J. (2018). Modelling career trajectories of cricket players using Gaussian processes. In R. Argiento, D. Durante, & S. Wade (Eds.), Bayesian Statistics and New Generations: Proceedings of the 2018 Bayesian Young Statisticians Meeting (pp. 165-173). Springer, Cham. Preprint.

Stevenson, O. G., & Brewer, B. J. (2017). Bayesian survival analysis of batsmen in Test cricket. Journal of Quantitative Analysis in Sports13(1), 25-36. Preprint.

Stevenson, O. G. (2017). The Nervous 90s: A Bayesian Analysis of Batting in Test Cricket. Masters thesis, University of Auckland. Online version.

Blog & News

RNZ interview with Nathan Rarere

I recently had the pleasure to chat with RNZ's Nathan Rarere about the Blackcaps' chances of making the World Test Championship Final earlier this week. At present, my models give New Zealand an 83% chance of going to the big dance in June later this year --- go the...

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