Department of Statistics
University of Auckland
I am a PhD candidate in the Department of Statistics at the University of Auckland, where I spend my time researching and developing statistical models that can applied to sports, with a particular focus on cricket.
I originally completed a Bachelor of Science in 2014 at the University of Otago, majoring in statistics. In 2015 I returned to my hometown of Auckland, where I have been studying statistics at the University of Auckland since.
- Sporting statistics
- Bayesian inference
- Statistical computing
- Doctor of Philosophy (2017 - Present)
- Master of Science (2016 - 2017)
- Bachelor of Science (Honours) (2015)
Click here for interactive visualisations of the abilities of cricket batsmen.
Following on from the work I did as part of my Masters, I began a Doctor of Philosophy in mid-2017, focussing on statistical applications in cricket, this time collaborating with the national cricketing board, New Zealand Cricket.
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’.
Click here to read thesis.
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.
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.