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 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.

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.