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
  • Statistical computing
  • Bayesian inference

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 26th March 2020.

Players must have participated in a Test match since January 1st, 2019 and must have batted in a minimum of 20 Test innings to be ranked.

RankPlayerCountryInningsRunsCareer averagePredicted averageICC rating
1Steve SmithAUS131722762.858.4911 (1)
2Marnus LabuschagneAUS23145963.457.0827 (3)
3Babar AzamPAK48185045.154.6800 (5)
4Virat KohliIND145724053.651.8886 (2)
5Kane WilliamsonNZ140647651.050.4813 (4)
6Angelo MathewsSL154598145.349.7658 (17)
7David WarnerAUS155724448.948.6793 (6)
8Rohit SharmaIND53214146.548.0674 (16)
9Joe RootENG169759948.447.2764 (8)
10Cheteshwar PujaraIND128584048.744.7766 (7)
11Ross TaylorNZ178723846.144.5677 (15)
12Ajinkya RahaneIND109420342.941.8726 (9)
13Rishabh PantIND2281438.841.8555 (40)
14Tom LathamNZ92372642.341.7710 (12)
15Dinesh ChandimalSL103387740.840.6562 (36)
16Asad ShafiqPAK123459339.340.1658 (17)
17Mushfiqur RahimBAN130441336.840.1655 (19)
18Usman KhawajaAUS77288740.740.1584 (31)
19Travis HeadAUS28109142.040.0643 (21)
20Azhar AliPAK147591942.639.8604 (26)
21Henry NichollsNZ50174739.739.8653 (20)
22BJ WatlingNZ110365838.539.2621 (24)
23Tamim IqbalBAN115440538.638.8598 (27)
24Ben StokesENG115405636.538.3718 (10)
25Dimuth KarunaratneSL128452436.837.8680 (14)
26Faf du PlessisSA112390139.837.6586 (30)
27Kusal MendisSL85299537.037.6628 (22)
28Peter HandscombAUS2993438.937.1NA
29Joe BurnsAUS36137938.336.7491 (57)
30Dean ElgarSA110388838.536.6624 (23)
31Roshen SilvaSL2370235.136.4NA
32Brendan TaylorZIM62205535.436.4598 (27)
33Darren BravoWI98350637.735.9460 (68)
34Quinton de KockSA80293439.135.8706 (13)
35Sean WilliamsZIM2477033.535.7480 (63)
36Rory BurnsENG2997933.835.7579 (33)
37Craig ErvineZIM36120835.535.6549 (43)
38KL RahulIND60200634.635.5504 (53)
39Ravindra JadejaIND71186935.335.4551 (41)
40Mominul HaqueBAN74286040.935.3556 (39)
41Matthew WadeAUS55144031.335.1488 (58)
42Sikandar RazaZIM30103734.635.0487 (59)
43Colin de GrandhommeNZ36118537.035.0589 (29)
44Dhananjaya de SilvaSL57186335.234.1561 (37)
45Shan MasoodPAK38118931.333.9582 (32)
46Sarfraz AhmedPAK86265736.433.8NA
47Soumya SarkarBAN67261341.533.6421 (74)
48Jason HolderWI69189832.733.0570 (35)
49MahmudullahBAN93276431.832.2514 (51)
50Tim PaineAUS50133031.731.8516 (48)
51Niroshan DickwellaSL66192131.031.7578 (34)
52Haris SohailPAK2381937.231.7496 (54)
53Jermaine BlackwoodWI49136230.331.5431 (72)
54Jos ButtlerENG73212731.731.3533 (45)
55Joe DenlyENG2678030.030.9550 (42)
56Shaun MarshAUS68226534.330.9NA
57Roston ChaseWI58169531.430.8516 (48)
58Shane DowrichWI56144430.130.8544 (44)
59Kusal PereraSL3393431.130.6481 (62)
60James PattinsonAUS2541726.130.6NA
61Kraigg BrathwaiteWI112349633.329.3508 (52)
62Temba BavumaSA67184530.828.7485 (61)
63Shimron HetmyerWI3083827.928.6521 (47)
64Jonny BairstowENG123403034.728.6516 (48)
65Sam CurranENG3071127.328.3462 (67)
66Wriddhiman SahaIND50123830.228.1389 (80)
67Aiden MarkramSA35140240.127.5608 (25)
68Liton DasBAN3485926.027.1450 (70)
69Mitchell SantnerNZ2974125.626.5380 (83)
70Imam ul-HaqPAK2148525.526.3377 (85)
71Shai HopeWI58149827.226.0479 (64)
72Mitchell MarshAUS55126025.225.7371 (90)
73Regis ChakabvaZIM3480625.225.6337 (94)
74Chris WoakesENG55117726.825.6358 (93)
75Keaton JenningsENG3278125.225.6386 (81)
76Jeet RavalNZ39114330.125.4456 (69)
77Ravichandran AshwinIND98238928.125.2373 (89)
78Moeen AliENG104278229.023.9380 (83)
79Mitchell StarcAUS85151522.323.6375 (87)
80Lahiru ThirimanneSL68140422.623.0329 (96)
81Mark WoodENG2639219.622.8NA
82Adil RashidENG3354019.322.4NA
83Theunis de BruynSA2342819.521.9317 (100)
84Imrul KayesBAN76179724.319.6334 (95)
85Trent BoultNZ8265415.219.5NA
86Donald TiripanoZIM2029919.919.3NA
87Mehidy HasanBAN4263817.719.3NA
88Tim SoutheeNZ106166817.418.4NA
89Keshav MaharajSA4864315.318.3NA
90Pat CumminsAUS4464717.017.9NA
91Peter SiddleAUS94116414.716.8NA
92Stuart BroadENG203321118.716.8NA
93Kyle JarvisZIM241289.114.6NA
94Dilruwan PereraSL70113918.114.4NA
95Josh HazlewoodAUS6240212.214.3NA
96Mohammed ShamiIND6449711.314.1NA
97Suranga LakmalSL9583611.614.0NA
98Kemar RoachWI9089012.213.8NA
99Yasir ShahPAK5870713.613.3NA
100Neil WagnerNZ6357512.513.3NA

The batting rankings are based on the models developed as part of my Masters and PhD at the University of Auckland. More detail about these models can be found in the papers I have published, which can be found in the Publications section below.

When estimating a player’s current ability the model accounts for 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. Additionally, the model accounts for the “getting your eye in” process for each individual player. 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, rather than arbitrary “rating points”, we are able to maintain an intuitive cricketing interpretation when comparing players. Instead of concluding “Steve Smith is 46 rating points better than Kane Williamson”, we can make more meaningful probabilistic statements, such as “we expect Steve Smith to outscore Kane Williamson by 7 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 match-specific 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 and future 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. (2018). Modelling career trajectories of cricket players using Gaussian processes. In R. Argiento, D. Durante, & S. Wade (Eds.), Bayesian Statistics and New Generations: Proceedings of the 2018 Bayesian Young Statisticians Meeting (pp. 165-173). Springer, Cham. 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.


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