What is the Moneyball effect? The term refers to methods adopted by American professional baseball team Oakland A's general manager Billy Beane and assistant general manager Paul DePodesta in 2002 — methods that were a departure from the norm, when key player-based decisions were made based on sabermetrics or key baseball statistics like slugging percentages and on-base and not on tried and tested methods like a scout’s perspective or individual star power. As a result of these revolutionary methods, the team became competitive, despite having a smaller budget, reaching the playoffs in 2002 and 2003.
Measurable data is the buzzword today. Whether it is crime, government spending, healthcare, music or even HR, smart statistics are being used across various sectors across the world. And while cricket is yet to have its “Moneyball” moment, data and data analytics are one of the key drivers in the sport today.
How did the Moneyball effect kicked off in Cricket?
One of the revolutionaries in the Cricket Coaching space was former Australia coach John Buchanan. In 2006 he came up with the “Cricket Athlete Management System” – which looks at all aspects from player prep to performance. Today it is a treasure trove of data, from their strength & fitness tests to match grabs of players.
While the success of new-age metrics in the longer formats of the game may still be debatable, there is no denying its impact on the shortest form of the game – Twenty20. With the IPL, data in cricket has taken on a whole new meaning. When clubs bid for players, it’s not only about the big names anymore. Smaller unknown names too have made it big. And this based on the potential they promise, based on the data available on them, perhaps?
And this data has helped the teams make key decisions as well. In 2019, at the launch event for ESPNCrinfo’s Superstats, former India captain Rahul Dravid, who was part of the Rajasthan Royals (RR) team in the early years, spoke about how they zeroed in Australian batsman Brad Hodge, even though he had had limited success in the IPL thus far. RR needed a budget-friendly player in the end overs and they realised Hodge would fit the bill. Why? They saw that his prior failures came mostly playing on spin-friendly wickets, and falling prey to left-arm spinners, and that his past records against pace bowling were stellar. They picked Hodge to bat in the last five overs. In 33 games he ended up scoring 639 runs for them at a strike rate of 137.41 and played at No 6 for Australia in the T20 World Cup.
Dravid opined about how as a coach, relying on data to assess a player’s performance, makes getting across the point much easier. What makes data such a key aspect in the shorter format is that while a two-over spell may not always make or break a game in the longer formats, it is far more critical in the shorter form of the game.
And thanks to technology, there are manifold ways of getting this data – it’s not just about stats anymore. From smart sensors to computer vision solutions, the options are aplenty. Smart wearables and vests are key tools in providing details on various aspects related to player wellness that play a key role in injury management. For example: England players wear micro-sensors on the top of their backs when at training or at a game. The related algorithms provide key data that helps in managing, among other things, a player’s return from injury.
While technologies like ball tracking, pitch map, etc. provide great data driven insights into matches, technologies related to smart cricket bats like Spektacom PowerBat focus on the efficacies of shot making
Data though, as Dravid pointed out, doesn’t necessarily provide the whole picture, the feel for the game is equally imperative. But there’s no denying that data as a key driver in cricket is here to stay.