Oliver Stevenson

PhD Candidate | Department of Statistics
University of Auckland

Hello!

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

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 6th December 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 SmithAUS126701363.862.3923 (2)
2Kane WilliamsonNZ133632252.755.3877 (3)
3Virat KohliIND141720255.054.7928 (1)
4David WarnerAUS149694748.649.8764 (5)
5Ross TaylorNZ169702247.147.8688 (16)
6Rohit SharmaIND53214146.547.8688 (16)
7Joe RootENG162728248.547.4752 (7)
8Angelo MathewsSL148564144.446.5643 (24)
9Henry NichollsNZ43165043.446.3726 (9)
10Ajinkya RahaneIND105411243.746.1759 (6)
11Tom LathamNZ82347844.045.8716 (11)
12Cheteshwar PujaraIND124574049.545.7791 (4)
13BJ WatlingNZ102353940.743.8696 (14)
14Dinesh ChandimalSL97376841.940.9591 (31)
15Faf du PlessisSA104375041.740.6654 (20)
16Travis HeadAUS2387841.840.3578 (34)
17Babar AzamPAK44144537.140.3698 (13)
18Usman KhawajaAUS77288740.739.6602 (28)
19Asad ShafiqPAK121446538.839.4646 (22)
20Tamim IqbalBAN112432739.039.3613 (26)
21Shikhar DhawanIND58231540.639.2NA
22Dean ElgarSA102364439.238.9656 (19)
23Joe BurnsAUS30122440.838.7456 (67)
24Colin de GrandhommeNZ2896840.338.5538 (43)
25Brendan TaylorZIM56184035.438.3607 (27)
26Azhar AliPAK143573142.538.2602 (28)
27Dimuth KarunaratneSL121432136.937.6723 (10)
28Ben StokesENG108373835.937.3694 (15)
29Peter HandscombAUS2993438.936.6481 (59)
30Matthew WadeAUS50132131.536.4462 (64)
31Kusal MendisSL79275436.236.2645 (23)
32Roshen SilvaSL2370235.136.0447 (68)
33Ravindra JadejaIND69184435.535.8560 (37)
34Darren BravoWI98350637.735.6460 (66)
35Mushfiqur RahimBAN129421035.135.4614 (25)
36KL RahulIND60200634.634.9514 (51)
37Quinton de KockSA72255438.134.9668 (18)
38Soumya SarkarBAN67261341.534.3429 (71)
39Jos ButtlerENG66201233.534.1590 (32)
40Sikandar RazaZIM2481834.133.9466 (62)
41Sarfraz AhmedPAK86265736.433.5551 (39)
42Rory BurnsENG2788632.833.5565 (36)
43MahmudullahBAN91273932.233.4536 (46)
44Jason HolderWI69189832.733.0570 (35)
45Dhananjaya de SilvaSL52162433.132.9538 (43)
46Parthiv PatelIND3893431.132.8NA
47Murali VijayIND105398238.332.7471 (61)
48Matt RenshawAUS2063633.532.7NA
49Haris SohailPAK2173536.832.5490 (58)
50Sam CurranENG2358130.631.6500 (56)
51Jermaine BlackwoodWI49136230.331.4431 (70)
52James PattinsonAUS2340126.731.0NA
53Niroshan DickwellaSL61173830.530.9583 (33)
54Jonny BairstowENG121402035.330.9559 (38)
55Mominul HaqueBAN71265739.730.8509 (52)
56Roston ChaseWI58169531.430.8516 (50)
57Tim PaineAUS46117731.030.7462 (64)
58Kusal PereraSL3393431.130.6501 (55)
59Shaun MarshAUS68226534.330.5503 (54)
60Hamilton MasakadzaZIM76222330.030.4544 (41)
61Shane DowrichWI56144430.130.4544 (41)
62Sean WilliamsZIM2055327.629.7401 (82)
63Mitchell SantnerNZ2570928.429.4429 (71)
64Temba BavumaSA65181231.229.3517 (49)
65Kraigg BrathwaiteWI112349633.328.9508 (53)
66Aiden MarkramSA35140240.128.8651 (21)
67Shimron HetmyerWI3083827.928.7521 (48)
68Wriddhiman SahaIND50123830.228.3397 (86)
69Shan MasoodPAK3494927.928.3527 (47)
70Mark StonemanENG2052627.728.0NA
71Dawid MalanENG2672427.827.8NA
72Jeet RavalNZ35109832.327.4499 (57)
73Vernon PhilanderSA86161924.226.6403 (79)
74Regis ChakabvaZIM2867826.126.5NA
75Ravichandran AshwinIND96238528.726.4400 (83)
76James VinceENG2254824.926.4NA
77Kaushal SilvaSL74209928.426.2338 (100)
78Imam ul-HaqPAK2148525.526.2389 (89)
79Shai HopeWI58149827.225.9479 (60)
80Chris WoakesENG53114527.325.8376 (93)
81Liton DasBAN3174424.825.7417 (76)
82Mitchell MarshAUS55126025.225.6382 (90)
83Keaton JenningsENG3278125.225.5402 (80)
84Kieron PowellWI76201126.825.3396 (87)
85Mitchell StarcAUS81143922.524.8378 (92)
86Sabbir RahmanBAN2248124.124.0NA
87Ish SodhiNZ2544821.323.9NA
88Moeen AliENG104278229.023.6395 (88)
89Bhuvneshwar KumarIND2955222.123.0NA
90Lahiru ThirimanneSL68140422.623.0NA
91Devon SmithWI76176023.822.5NA
92Adil RashidENG3354019.322.4NA
93Theunis de BruynSA2342819.521.6NA
94Tim SoutheeNZ100163818.219.9NA
95Pat CumminsAUS4060617.819.7NA
96Trent BoultNZ7960714.819.6NA
97Imrul KayesBAN76179724.319.6341 (99)
98Mehidy HasanBAN4263817.719.4NA
99Dinesh KarthikIND42102525.019.0NA
100Abdur RazzakBAN2224815.518.8NA

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.

Publications

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.

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

Contact

o.stevenson@auckland.ac.nz

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

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