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 September 11th 2019. Players must have batted in a minimum of 20 Test innings to be ranked.

RankPlayerCountryInningsRunsCareer averagePredicted averageICC rating (#)
1Steve SmithAUS120657763.264.1904 (1)
2Kane WilliamsonNZ130616352.254.9878 (3)
3Virat KohliIND133667353.452.1903 (2)
4Henry NichollsNZ41159344.248.2749 (5)
5Cheteshwar PujaraIND116545350.547.6825 (4)
6Angelo MathewsSL148564144.446.6643 (19)
7Tom LathamNZ79334744.046.5724 (8)
8Ross TaylorNZ166683946.545.8669 (15)
9David WarnerAUS143644246.745.2686 (14)
10Joe RootENG155689448.245.0726 (6)
11Rohit SharmaIND47158539.644.9513 (54)
12Travis HeadAUS2082345.744.8629 (25)
13Faf du PlessisSA98360843.043.5702 (12)
14Dinesh ChandimalSL97376841.941.0591 (31)
15Azhar AliPAK139566943.340.9639 (21)
16BJ WatlingNZ100327938.640.7620 (27)
17Ajinkya RahaneIND97367141.739.9725 (7)
18Usman KhawajaAUS77288740.739.5627 (26)
19Ben StokesENG101347935.939.5693 (13)
20Tamim IqbalBAN112432739.039.4632 (24)
21Asad ShafiqPAK117432338.939.3643 (19)
22Shikhar DhawanIND58231540.639.1517 (53)
23Joe BurnsAUS28112340.138.7434 (72)
24Shakib Al HasanBAN103380739.738.4604 (29)
25Brendan TaylorZIM56184035.438.3607 (28)
26Colin de GrandhommeNZ2790339.338.3519 (52)
27Quinton de KockSA66239839.337.8718 (11)
28Dimuth KarunaratneSL121432136.937.6723 (9)
29Babar AzamPAK40123535.337.2658 (17)
30Peter HandscombAUS2993438.936.3501 (57)
31Kusal MendisSL79275436.236.3645 (18)
32Roshen SilvaSL2370235.136.0447 (67)
33KL RahulIND58198735.536.0541 (43)
34MahmudullahBAN85265533.235.7574 (36)
35Darren BravoWI96347937.835.4465 (64)
36Mushfiqur RahimBAN123400635.135.3588 (33)
37Dean ElgarSA96341238.835.2639 (21)
38Mominul HaqueBAN65255841.934.9551 (40)
39Soumya SarkarBAN65255841.934.8453 (65)
40Jason HolderWI66183033.334.7580 (35)
41Ravindra JadejaIND62154432.934.1510 (55)
42Sikandar RazaZIM2481834.133.9466 (63)
43Sarfraz AhmedPAK86265736.433.5562 (39)
44Roston ChaseWI55168133.033.3533 (47)
45Aiden MarkramSA31135843.833.1719 (10)
46Parthiv PatelIND3893431.132.9NA
47Dhananjaya de SilvaSL52162433.132.8538 (44)
48Jos ButtlerENG60177732.932.8547 (41)
49Murali VijayIND105398238.332.6496 (59)
50Matt RenshawAUS2063633.532.5417 (80)
51Temba BavumaSA59171633.031.8563 (37)
52Jonny BairstowENG117394235.831.7589 (32)
53James PattinsonAUS2340126.731.2NA
54Kraigg BrathwaiteWI108346434.331.0526 (49)
55Niroshan DickwellaSL61173830.531.0582 (34)
56Shane DowrichWI55140229.831.0525 (50)
57Kusal PereraSL3393431.130.7501 (57)
58Shimron HetmyerWI2779029.330.6527 (48)
59Matthew WadeAUS44103728.030.6NA
60Hamilton MasakadzaZIM76222330.030.4544 (42)
61Shaun MarshAUS68226534.330.4524 (51)
62Jeet RavalNZ32107434.630.3538 (44)
63Tim PaineAUS41106131.230.3449 (66)
64Rory BurnsENG2055427.730.1447 (67)
65Sean WilliamsZIM2055327.629.7401 (83)
66Wriddhiman SahaIND46116430.629.1NA
67Chris WoakesENG50113729.228.6423 (75)
68Mark StonemanENG2052627.727.9357 (97)
69Dawid MalanENG2672427.827.9395 (87)
70Ravichandran AshwinIND93236129.127.6413 (81)
71Shan MasoodPAK3079326.427.6470 (62)
72Regis ChakabvaZIM2867826.126.5NA
73James VinceENG2254824.926.4342 (99)
74Vernon PhilanderSA82153824.026.3401 (83)
75Kaushal SilvaSL74209928.426.3NA
76Shai HopeWI56148527.526.1485 (60)
77Mitchell SantnerNZ2356024.325.7NA
78Keaton JenningsENG3278125.225.6419 (78)
79Kieron PowellWI76201126.825.3400 (85)
80Liton DasBAN2662223.924.7366 (96)
81Mitchell MarshAUS53121925.424.7394 (89)
82Imrul KayesBAN72177625.424.4382 (92)
83Sabbir RahmanBAN2248124.124.0NA
84Ish SodhiNZ2544821.324.0NA
85Moeen AliENG104278229.023.7412 (82)
86Mitchell StarcAUS78137721.923.4367 (95)
87Bhuvneshwar KumarIND2955222.123NA
88Lahiru ThirimanneSL68140422.622.9342 (99)
89Devon SmithWI76176023.822.6NA
90Adil RashidENG3354019.322.4NA
91Pat CumminsAUS3658619.522.1346 (98)
92Tim SoutheeNZ98161118.320.3NA
93Mehidy HasanBAN3655418.519.7NA
94Trent BoultNZ7860614.819.4NA
95Dinesh KarthikIND42102525.018.9NA
96Abdur RazzakBAN2224815.518.8NA
97Mark WoodENG2329716.518.8NA
98Devendra BishooWI6170715.416.6NA
99Kyle JarvisZIM2212610.516.2NA
100Peter SiddleAUS92113314.516.1NA

The batting rankings are based on the models developed as part of my Masters and PhD at the University of Auckland. The model accounts for a player’s recent form, venues of matches played in (i.e. home, away or neutral) and whether the player was batting in their team’s first or second innings of the match. The data support the general belief that players tend to score more runs when batting in their team’s first innings of a match, at a home venue.

The “predicted average” is the the number of runs we expect the player to score in their next Test innings, assuming their next innings is played at a neutral venue and it is unknown whether they are batting in their team’s first or second innings of the match. The official International Cricket Council (ICC) ratings (and world ranking #) are also provided for comparison. The ranking of players is generally similar between the two methods, although there are a couple of notable differences.

Firstly, our model rewards players who are able to overcome the “getting your eye in” process and remain on a not out score, while the ICC ratings simply provide not out innings with a “bonus” that we susepct is too low. For example, Rohit Sharma is currently ranked 11th in the world by our model and 54th by the ICC. Sharma has a large number of not out scores between 50 and 100, suggesting he frequently overcomes the difficult “getting your eye in” process, but for various reasons, has not had the opportunities to convert these not out innings into big scores.

Secondly, the ICC ratings tend to place more emphasis on recent innings compared with our models. Our general findings suggest that there is no evidence to suggest that recent form is a significant predictor of current batting ability for the majority of players. Instead, we believe a player’s underlying ability tends to change slowly over time, rather than erratically between innings as a direct result of recent performances. It is unclear whether the ICC ratings attempt to provide predictive accuracy of ability, or instead tries to formalise expert judgement about who is in and out of form. These two goals may not be entirely compatible.

Finally, while both methods provide a general indication of batting ability, by measuring underlying batting ability in units of a batting average, we are able to maintain an intuitive cricketing interpretation when comparing players. Instead of concluding “Steve Smith is 26 rating points better than Kane Williamson”, we can make more meaningful probabilistic statements, such as “we expect Steve Smith to outscore Kane Williamson by 9.2 runs in their next respective innings”, or “we expect Steve Smith has a 54.1% chance of outscoring Kane Williamson in their next respective innings”. In both statements we are assuming a neutral venue and it is unknown whether they are batting in their team’s first or second innings of the match. Of course, we can update these estimates to include specific match information, if we know the venue of the next match and whether the player is batting in their team’s first or second innings of the match.

Here you can find an application that allows users to visualise how the models estimate batting ability on two scales:

1. Short-term changes in ability that occur during an innings due to the “getting your eye in” process

2. Long-term changes in ability that occur between innings, over a playing career, providing an estimate of a player’s batting career trajectory to date, as well as a prediction for their current ability. These estimates are what are used to compute our batting rankings


Stevenson, O. G., & Brewer, B. J. (2019). Finding your feet: a Gaussian process model for estimating the abilities of batsmen in Test cricket. Submitted to Journal of the Royal Statistical Society: Series C (Applied Statistics). Preprint.

Stevenson, O. G., & Brewer, B. J. (2019). Modelling career trajectories of cricket players using Gaussian processes. In Press, 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.

read more



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