PhD Candidate | Department of Statistics
University of Auckland
The recent announcement of the Australian Test squad to take on Pakistan in the UAE has been turning heads, notably for the omission of Glenn Maxwell, who seemed to be poised for a return to the Test arena. Instead, the uncapped trio of Aaron Finch, Travis Head and...read more
- Sports statistics
- Bayesian inference
- Computational statistics
- Doctor of Philosphy (2017 - present)
- Master of Science (2016 - 2017)
- Bachelor of Science (Honours) (2015)
Following on from the work I did as part of my Masters, I began a Doctor of Philosophy in mid-2017, focusing on statistical applications in cricket, this time collaborating with the national cricketing board, New Zealand Cricket. The initial aim of my PhD has been to develop a means of quantifying a player’s current ability and tracking how it changes over the course of an entire playing career. As with any sport or profession, we shouldn’t expect a player to perform with some constant ability throughout their entire career. Rather, we are likely to observe variations and fluctuations in ability due to the likes of age, experience, fitness and luck. The models which I have developed have the benefit of maintaining an intuitive cricketing interpretation, unlike other ranking metrics, such as the official ICC rankings.
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 describe how well a batsman is playing at any given point in their innings, allowing us to quantify the cricketing idea 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 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’.
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.
Stevenson, O. G., & Brewer, B. J. (in press). Modelling career trajectories of cricket players using Gaussian processes. In Bayesian Statistics: New Challenges and New Generations – BAYSM 2018. Springer. Preprint.
Stevenson, O. G., & Brewer, B. J. (2017). Bayesian survival analysis of batsmen in Test cricket. Journal of Quantitative Analysis in Sports, 13(1), 25-36. Preprint.
University of Auckland | Department of Statistics | Room 303S.376