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.
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...read more
- 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.
This application allows you to explore how the batting abilities of individual players change during an innings (i.e. how the ‘getting your eye in’ process affects different players). Additionally, you can explore how the batting abilities of players have evolved over their careers, including predictions for the number of runs the model expects players to score in their next innings. We can use the models to rank all current Test batsmen by predicting the number of runs we expect each player to score in their next innings. By maintaining an intuitive cricketing interpretation, we are able to not only rank all current Test batsmen, but we can also quantify differences in ability between players, something which other ranking systems, such as the official ICC rankings, are unable to do.
Current Top 10 Test Batting Rankings (updated 12th September 2018):
|Rank||Player||Innings||Career Average||Predicted Average||Career Best Predicted Average||ICC Rating (#)|
|1||Steven Smith (AUS)||117||61.38||62.37||64.96 (73rd Innings)||929 (2)|
|2||Virat Kohli (IND)||122||54.49||55.21||57.07 (105th Innings)||930 (1)|
|3||Kane Williamson (NZ)||116||50.36||51.38||57.28 (77th innings)||847 (3)|
|4||Joe Root (ENG)||135||51.05||50.52||52.77 (40th Innings)||835 (4)|
|5||Cheteshwar Pujara (IND)||105||49.58||48.94||51.01 (9th Innings)||772 (6)|
|6||David Warner (AUS)||137||48.20||47.88||48.78 (87th Innings)||820 (5)|
|7||Ross Taylor (NZ)||152||47.23||47.69||48.49 (129th Innings)||697 (12)|
|8||Dinesh Chandimal (SL)||88||44.96||46.12||46.13 (87th Innings)||733 (8)|
|9||Alastair Cook (ENG)||291||45.35||45.61||47.14 (116th Innings)||709 (10)|
|10||Azhar Ali (PAK)||124||44.84||45.10||46.41 (93rd Innings)||672 (15)|
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. 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 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. The models which have been developed have benefit of maintaining an intuitive cricketing interpretation, unlike other ranking metrics, such as the official ICC rankings.
See the applications section for my current Test batting 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’.
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.