Top 50 Test bowlers

Players must have participated in a Test match since 2019 and must have taken a minimum of 30 Test wickets to be ranked.

Last updated 1st December 2020 (updated rankings coming June 2021).

*incomplete data is available for players who made their Test debut prior to 2008.

The batting and bowling rankings are obtained from the models developed as part of my Masters and PhD degrees 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.

Batting rankings

When estimating a player’s current batting 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.

Thirdly, the ICC apply a rating decay system, whereby players who have not batted in a recent match, see their rating slowly
decline. While well intentioned to reflect the best current batsmen in the world, the decay system is inherently biased as it tends to affect smaller nations disproportionately, who are generally afforded fewer opportunities to play Test cricket.

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 99 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 runs in their next respective innings”, or “we expect Steve Smith has a 59.9% 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.

Bowling rankings

Like the batting rankings, the model used to predict current player bowling ability accounts for recent form, venue of matches played in (i.e. home, away or neutral) and whether the player was bowling in their team’s first or second innings of the match.The bowling model also adjusts for the historic strength of opposition batsmen they have bowled to.

In order to make this adjustment, a new metric, “adjusted bowling average”, has been derived, representing a player’s career bowling average if they had bowled to the “average” Test match batsman their entire career. Introducing this metric allows for a fairer comparison of bowling performances, as the relative strengths of batsmen bowled to is accounted for. The derivation of this metric can be found in my PhD thesis in the Publications section below (as of 18th December 2020).

The “predicted adjusted average” is the the number of runs we expect the player to concede before taking their next wicket, assuming they are bowling to the “average” Test match batsmen and the match is being played at a neutral venue. The official International Cricket Council (ICC) ratings (and world ranking #) are also provided for comparison.

Visualising player ability

Here you can find an application that allows users to visualise how these model estimate changes in player batting and bowling ability, on two scales:

1. Long-term changes in batting and bowling ability that occur between innings, over a playing career, providing an estimate of a player’s 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 and bowling rankings rankings.

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