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 October 17th 2019.

Players must have participated in a Test match since 2018 and must have batted in a minimum of 20 Test innings to be ranked.

RankPlayerCountryInningsRunsCareer averagePredicted averageICC rating
1Steve SmithAUS124697364.665.0937 (1)
2Virat KohliIND138705455.156.5936 (2)
3Kane WilliamsonNZ130616352.254.8878 (3)
4Henry NichollsNZ41159344.248.3749 (5)
5Rohit SharmaIND50190244.247.8635 (22)
6Angelo MathewsSL148564144.446.7643 (19)
7Cheteshwar PujaraIND121563149.846.7817 (4)
8Tom LathamNZ79334744.046.4724 (7)
9Ross TaylorNZ166683946.545.8669 (13)
10Joe RootENG159704347.944.2731 (6)
11Ajinkya RahaneIND102386042.443.2721 (9)
12Faf du PlessisSA102374542.642.6689 (11)
13Travis HeadAUS2285442.741.9589 (32)
14Dinesh ChandimalSL97376841.941.1591 (31)
15Azhar AliPAK139566943.341.0639 (21)
16David WarnerAUS147645845.540.8623 (24)
17BJ WatlingNZ100327938.640.6620 (25)
18Dean ElgarSA100362839.439.6666 (14)
19Asad ShafiqPAK117432338.939.5643 (19)
20Usman KhawajaAUS77288740.739.4615 (27)
21Tamim IqbalBAN112432739.039.3626 (23)
22Shikhar DhawanIND58231540.639.0NA
23Colin de GrandhommeNZ2790339.338.6519 (53)
24Brendan TaylorZIM56184035.438.4607 (29)
25Joe BurnsAUS28112340.138.4425 (75)
26Shakib Al HasanBAN105386239.438.1599 (30)
27Dimuth KarunaratneSL121432136.937.6732 (8)
28Babar AzamPAK40123535.337.3658 (15)
29Ben StokesENG105359335.637.2685 (12)
30Quinton de KockSA70254539.237.1704 (10)
31Peter HandscombAUS2993438.936.5491 (59)
32Kusal MendisSL79275436.236.2645 (18)
33Ravindra JadejaIND66172134.435.9551 (40)
34Roshen SilvaSL2370235.135.9447 (68)
35Darren BravoWI98350637.735.6465 (67)
36Jos ButtlerENG64196933.935.0608 (28)
37KL RahulIND60200634.634.8530 (48)
38Soumya SarkarBAN67261341.534.3438 (70)
39Mushfiqur RahimBAN125402934.734.0570 (36)
40MahmudullahBAN87266932.533.9549 (41)
41Jason HolderWI68188733.133.9580 (34)
42Sikandar RazaZIM2481834.133.8466 (66)
43Mominul HaqueBAN67261341.533.7549 (41)
44Sarfraz AhmedPAK86265736.433.6562 (39)
45Matthew WadeAUS48122329.833.6424 (76)
46Parthiv PatelIND3893431.133.0NA
47Dhananjaya de SilvaSL52162433.132.8538 (44)
48Murali VijayIND105398238.332.6486 (60)
49Matt RenshawAUS2063633.532.6NA
50Tim PaineAUS45116431.532.0471 (63)
51Roston ChaseWI57169331.931.3533 (47)
52James PattinsonAUS2340126.731.1NA
53Jonny BairstowENG121402035.331.0570 (36)
54Shane DowrichWI55140229.830.9525 (52)
55Niroshan DickwellaSL61173830.530.9582 (33)
56Kusal PereraSL3393431.130.7501 (57)
57Sam CurranENG2054130.130.6492 (58)
58Shaun MarshAUS68226534.330.5514 (54)
59Temba BavumaSA63178031.830.4529 (49)
60Jeet RavalNZ32107434.630.4538 (44)
61Rory BurnsENG2470229.230.4509 (55)
62Sean WilliamsZIM2055327.629.7401 (84)
63Shimron HetmyerWI2982528.429.5527 (50)
64Kraigg BrathwaiteWI110347733.829.3526 (51)
65Aiden MarkramSA35140240.128.8658 (15)
66Wriddhiman SahaIND47118530.428.6401 (84)
67Mark StonemanENG2052627.728.0NA
68Dawid MalanENG2672427.827.9NA
69Ravichandran AshwinIND94236229.227.8415 (80)
70Shan MasoodPAK3079326.427.7470 (65)
71Chris WoakesENG52114527.927.1395 (87)
72Vernon PhilanderSA86161924.226.6407 (82)
73Regis ChakabvaZIM2867826.126.4NA
74James VinceENG2254824.926.3NA
75Kaushal SilvaSL74209928.426.2338 (94)
76Shai HopeWI56148527.526.2485 (61)
77Mitchell SantnerNZ2356024.325.7337 (100)
78Mitchell MarshAUS55126025.225.6390 (88)
79Keaton JenningsENG3278125.225.5410 (81)
80Mitchell StarcAUS80143422.825.5388 (89)
81Kieron PowellWI76201126.825.3400 (86)
82Liton DasBAN2866423.724.4370 (93)
83Imrul KayesBAN72177625.424.3378 (92)
84Sabbir RahmanBAN2248124.124.0NA
85Ish SodhiNZ2544821.323.8NA
86Moeen AliENG104278229.023.6403 (83)
87Bhuvneshwar KumarIND2955222.123.0NA
88Lahiru ThirimanneSL68140422.623.0342 (96)
89Devon SmithWI76176023.822.6NA
90Adil RashidENG3354019.322.4NA
91Theunis de BruynSA2239819.021.2NA
92Tim SoutheeNZ98161118.320.2NA
93Pat CumminsAUS3959918.220.1NA
94Trent BoultNZ7860614.819.3NA
95Dinesh KarthikIND42102525.018.9NA
96Mark WoodENG2329716.518.8NA
97Abdur RazzakBAN2224815.518.8NA
98Keshav MaharajSA4254915.218.6NA
99Mehidy HasanBAN3857718.018.4NA
100Devendra BishooWI6170715.416.7NA

*Assumes next innings is played at a neutral venue

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, venue 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 has a number of 50+ not out scores, 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 little 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 10.2 runs in their next respective innings”, or “we expect Steve Smith has a 54.9% 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. 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