The chief villain of the summer was supposed to be Virat Kohli, but that changed following the Boxing Day Test, with focus instead turning to the Australian men’s selection panel.
Two washed-out days in Sydney and endless hours of talkback radio further intensified the critiques.
Most of the criticism has focused on the lack of communication and inconsistent decision-making. Why was Aaron Finch asked to open the batting despite not playing there for his state? And why was the request for players to score runs at Shield level despite many fringe players, particularly Glenn Maxwell and Marcus Stoinis, only having the opportunity to play two Shield games before the Big Bash League started?
However, communication issues and inconsistent decision-making are symptoms of the ingrained philosophical approach to selecting cricket teams. Why do we still pick teams based on opinion and not analysis?
Like James Silver was in his article yesterday, I’m reminded of the baseball film Moneyball. Billy Beane (Brad Pitt), the general manager of the Oakland A’s, walks into a room of old scouts, who inform him that they have found new players for the club based on their “swing technique” and “gut instinct”. Beane promptly dismisses their advice and begins the movement to sabermetrics and the use of advanced analytics in baseball.
As my mind envisages the selection meeting room at the MCG, the image of Trevor Hohns, Greg Chappell and Justin Langer sitting around the table doesn’t look too dissimilar from the one Beane saw in Oakland. Decisions made on ‘technique’ and ‘gut instinct’.
Like baseball 15 years ago, there is a desperate need for advanced analytics in cricket. The most commonly reported cricket statistics are still batting averages and number of hundreds or bowling averages and number of wickets. These statistics do not take into account the context of the match, the quality of the opposition or the pitch and ground conditions. What is the value of scoring 40 on a green seamer? Or taking a wicket to break a significant partnership? These performances are washed over in the reporting of averages.
In the recent Test series, Cheteshwar Pujara has been lauded for his three 100s, most notably his 193 in Sydney. But I hypothesise that advanced analytics would suggest that his two innings in Adelaide (123 and 71) were more valuable to India winning a match. Pujara scored 35 per cent of India’s total runs in Adelaide, including 50 per cent of India’s first-innings runs on a pitch that was difficult to bat on early.
The sabermetrics movement in baseball led to the development of new and improved analytics. One of the most important statistics is wins above replacement (WAR), which incorporates a huge range of batting, pitching and fielding data to assess a player’s contribution to winning matches. A similar measure could be developed for cricket.
Ultimately cricket teams seek to win matches; runs and wickets only enable that to happen. Therefore, a statistic or statistics that measure the average contribution of a player to enabling a win would be more important than the actual number of runs or wickets he or she contributes. In a low-scoring match, scores of 75 and 25 might be more valuable to winning a match than 250 not-out on a flat deck.
A cricket ‘wins above replacement’ statistic would enable more evidence-based and consistent decision-making at the selection table. The data could clearly articulate the contribution of a player to winning matches and thus his or her value of being selected.
With a transformation of the Australian men’s team selection process expected, now could be the opportunity for Cricket Australia to begin leading the advanced analytics movement in cricket.
We live in a digital age where data is everywhere. The challenge is to enable data to inform our decision-making, not just opinions.
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