Random Analytica: Introducing the First Chance Average to Cricket

by Shane Granger

In cricket statistics, a batters average is calculated by the amount of runs they have scored divided by their dismissals (i.e. 1000-runs/20-dismissals = Average of 50). The First Chance Average or FCA is determined by the number of Earned Runs divided by their dismissal OR chances. Chances are discretionary but they must be legitimate, i.e. a dropped catch, a legitimate missed stumping or a dismissal from a no-ball (so hitting a ball through the slips when there are no slippers doesn’t count as a chance). Runs coming after a chance are recorded as First Chance runs and are omitted from FCA calculations.


The metric was originally developed by myself (Shane Granger) over the 2013-2014 summer with inputs from two very smart colleagues, Adrian Storen and Daryn Webster using the Earned Run Average metric in baseball as a concept model. At the time it was trying to answer some questions about the batting of both Shane Watson and David Warner.

With David Warner giving up three very big chances during that summer the FCA was going to get a mention on the ABC sport show Offsiders BUT David Warner’s run of chances stopped at Johannesburg and the FCA missed its debut.

The First Chance Average for cricket was discarded… Or was it?

During the 2017-2018 Summer I decided to re-invigorate the First Chance Average by improving the metrics to exclude multiple chances and include volatility which answered the questions about David Warner back in 2014. These simple changes made the metric more robust while changing the graphic to look and feel like a standard average allowing for greater clarity. The new datasets used for the Australian tour of South Africa were also a ten-fold increase on what I developed in 2014 and were able to show a team picture rather than focus on anyone individual.

The Example (Adam Voges)


Adam Voges is a great example because over his short career he only had two chances but they were significant.

In his debut innings at Roseau against the West Indies Adam scored 130*, thus he didn’t qualify for an average as he did not have a dismissal. However his First Chance Average was 57 as he was dropped. Because he had 57 Earned Runs and 73 First Chance Runs his volatility was a very high 56.2%. As his first score was 130* the difference between Earned Runs and First Chance Runs are split between Green/Red in the chart.

In his second innings Adam was dismissed for 37 without a chance, so his score is in blue. His Standard Average is now 167, (167 runs with one dismissal) while his First Chance Average is now 47 (94 runs with two dismissals). His volatility dropped to 43.7%.

In his third innings Adam was dismissed for 31 without a chance, so his score is in blue. His Standard Average is now 99, (198 runs with two dismissals) while his First Chance Average is now 41.67 (125 runs with three dismissals) His volatility continues to decrease to 36.9%.

By the time Adam finished his short career he had just two chances but the difference between his Standard Average and First Chance Average was reasonably significant. To answer this question I introduced volatility, which is a measurement to see whether a batter First Chance Runs are increasing or decreasing. In the end Adam Voges volatility was decreasing but so was his Standard Average and his last big score was 7 Earned Runs compared to 232 First Chance Runs.