Oliver Stevenson

PhD Candidate | 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 developing statistical models that can be applied to the sport of cricket. I also provide statistical consulting services to those who need help with data analysis or in resolving any statistical problems you might have.

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


Research interests:

  • Sports statistics
  • Bayesian inference
  • Computational statistics

Following on from the work I did as part of my Masters, I began a Doctor of Philosophy at the University of Auckland 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.

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

Last updated August 1st 2019.

RankPlayerCountryInningsRunsCareer averagePredicted averageICC rating (#)
1Kane WilliamsonNZ127613953.461.4913 (2)
2Steve SmithAUS117619961.457.4857 (4)
3Virat KohliIND131661353.853.2922 (1)
4Henry NichollsNZ38151045.850.4778 (5)
5Cheteshwar PujaraIND114542651.249.2881 (3)
6Angelo MathewsSL144555444.848.6660 (17)
7Joe RootENG147668549.548.6763 (6)
8David WarnerAUS137636348.247.2756 (7)
9Ross TaylorNZ163672746.747.2676 (13)
10Rohit SharmaIND47158539.644.4524 (51)
11Tom LathamNZ76311842.744.3680 (12)
12Faf du PlessisSA98360843.043.5702 (10)
13Dinesh ChandimalSL97376841.941.6603 (30)
14Usman KhawajaAUS71276542.541.6682 (11)
15Azhar AliPAK139566943.340.7639 (23)
16Hashim AmlaSA215928246.640.4664 (16)
17Tamim IqbalBAN112432739.039.4632 (25)
18Asad ShafiqPAK117432338.939.4643 (21)
19Shikhar DhawanIND58231540.639.1527 (50)
20Brendan TaylorZIM56184035.438.7607 (28)
21Shakib Al HasanBAN103380739.738.7604 (29)
22Joe BurnsAUS28112340.138.5447 (69)
23Quinton de KockSA66239839.338.4718 (9)
24BJ WatlingNZ97309637.338.0583 (33)
25Babar AzamPAK40123535.337.5658 (18)
26Jos ButtlerENG54172235.937.4624 (27)
27Peter HandscombAUS2993438.936.9517 (52)
28Kusal PereraSL2991035.036.8559 (41)
29Kusal MendisSL75263936.736.6657 (19)
30Ajinkya RahaneIND95348840.636.4643 (21)
31Dimuth KarunaratneSL117407436.136.4676 (13)
32Jason HolderWI64178333.636.1571 (37)
33Roshen SilvaSL2370235.136.1456 (65)
34Darren BravoWI94345938.435.9480 (60)
35MahmudullahBAN85265533.235.7574 (34)
36Tim PaineAUS3598435.135.7509 (53)
37Dean ElgarSA96341238.835.4639 (23)
38Soumya SarkarBAN65255841.935.2453 (67)
39KL RahulIND56190535.335.1550 (44)
40Mushfiqur RahimBAN123400635.134.7588 (32)
41Mominul HaqueBAN65255841.934.6551 (43)
42Jeet RavalNZ29103737.033.9572 (35)
43Sarfraz AhmedPAK86265736.433.8562 (40)
44Roston ChaseWI53162133.133.8552 (42)
45Ben StokesENG95315233.933.6590 (31)
46Sikandar RazaZIM2481834.133.6466 (63)
47Jonny BairstowENG109380637.033.3626 (26)
48Aiden MarkramSA31135843.833.2719 (8)
49Ravindra JadejaIND60148532.333.0488 (58)
50Murali VijayIND105398238.332.9506 (54)
51Parthiv PatelIND3893431.132.8NA
52Kraigg BrathwaiteWI106344934.832.4572 (35)
53Matt RenshawAUS2063633.532.2430 (75)
54Temba BavumaSA59171633.032.1563 (39)
55Chris WoakesENG43101230.732.0429 (77)
56Colin de GrandhommeNZ2682037.332.0487 (59)
57Shimron HetmyerWI2575430.231.9530 (49)
58Shane DowrichWI55140229.831.1536 (47)
59Shaun MarshAUS68226534.331.0540 (46)
60Hamilton MasakadzaZIM76222330.030.4544 (45)
61Niroshan DickwellaSL58162630.130.0564 (34)
62Dhananjaya de SilvaSL48149532.529.7502 (57)
63Sean WilliamsZIM2055327.629.4401 (84)
64Wriddhiman SahaIND46116430.629.2NA
65Mark StonemanENG2052627.728.1371 (91)
66Dawid MalanENG2672427.827.9411 (81)
67Shan MasoodPAK3079326.427.5470 (62)
68Ravichandran AshwinIND93236129.127.1421 (79)
69Moeen AliENG100276930.126.9466 (63)
70Shai HopeWI54145928.126.9503 (56)
71Vernon PhilanderSA82153824.026.8401 (84)
72Regis ChakabvaZIM2867826.126.5337 (100)
73James VinceENG2254824.926.4356 (95)
74Kaushal SilvaSL74209928.426.3345 (97)
75Keaton JenningsENG3278125.225.5436 (73)
76Kieron PowellWI76201126.825.3408 (82)
77Liton DasBAN2662223.924.6366 (92)
78Ish SodhiNZ2544821.324.3NA
79Mitchell MarshAUS53121925.424.1406 (83)
80Sabbir RahmanBAN2248124.123.9NA
81Mitchell StarcAUS78137721.923.6378 (89)
82Lahiru ThirimanneSL64132822.923.4NA
83Imrul KayesBAN72177625.423.3382 (88)
84Bhuvneshwar KumarIND2955222.122.9NA
85Devon SmithWI76176023.822.3NA
86Adil RashidENG3354019.322.1NA
87Pat CumminsAUS3052820.321.9360 (94)
88Mehidy HasanBAN3655418.519.8NA
89Tim SoutheeNZ95155018.019.6NA
90Mark WoodENG2329716.519.2NA
91Dinesh KarthikIND42102525.019.1NA
92Trent BoultNZ7656214.419.1NA
93Abdur RazzakBAN2224815.518.9NA
94Devendra BishooWI6170715.416.8NA
95Kyle JarvisZIM2212610.516.0NA
96Dilruwan PereraSL68112618.515.9NA
97Keshav MaharajSA3844613.915.7NA
98Peter SiddleAUS90108014.215.5NA
99Josh HazlewoodAUS5538812.515.1NA
100Kemar RoachWI8582712.214.7NA


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 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

The statistical rationale behind Cricket Australia’s statistical rationale to ignore Glenn Maxwell

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 Marnus Labuschagne have made the cut. Cricket Australia have since justified the selections of the batsman in the squad on the basis of a “statistical rationale”, focusing on three key metrics.

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University of Auckland | Department of Statistics | Room 303S.376