When more than 2 billion people tune into the men’s Cricket World Cup, they will see one of two games. One is the cricket on the field; the other is between teams using data to get the winning bowl.
Cricket is no stranger to data and statistics. Strategists have always pored over batting and bowling averages. It has expanded in more recent times to obscure statistical insights, the relevance of which is lost on most viewers and fans.
This data set is historical and has only ever been used as a potential guide to future form and outcomes. It has primarily been more about building a player’s reputation in the game.
The rise of more sophisticated data analysis, however, has given birth to a whole new science of prediction and forecasting. It is also driving many of the team decisions behind the scenes. This is the emerging “big data” approach of “criclytics.”
The Rise of the Cricket Analyst
Working very closely with each of the ten national teams competing at the tournament is someone with a relatively new job description: the video analyst.
In today's game, in-depth data analysis overlay historical statistics. This helps players to understand their faults. It also offers insights into how to defeat their opponents.
As in business, cricket analysts harness data and sophisticated algorithms to provide their teams with real-time inputs. These can influence decisions, such as which bowler to use, which batsman should be next in, and what field placings should be used.
For the Australian team, which is now on top of the tournament table after being earlier written off by pundits, the big focus is on using data for team selection. All 15 members of Australia’s squad have appeared in at least one of the team’s nine games so far. In comparison, the New Zealand team remains unchanged.
“My past analyst became one of my most important selectors,” Australian coach Justin Langer told cricket journalists last week.
“It’s the same with my current analyst. When they say, 'This is what you should consider when we play whoever, you might need to play the off-spinner, you might need to play the leg spinner or the left armer, you look at it.”
Note: for those mystified by cricket, these are different types of bowlers.
The basis for these decisions comes from an analysis of an opponent’s weaknesses. What does the data say about a batsman’s weakness against particular kinds of bowling? Is there a weakness against short-pitched bowling which is borne out in the analysis? If so, it might be a good idea to drop the slow bowler for a particular game and bring in the pace bowler who can pitch short.
“As a coach, you’ve got to find the gold nuggets in the data, and then I can sell that to the players,” said Langer.
An Analysts’ Battleground
Host nation England, who was widely tipped to take out the World Cup title in the run-up to the event, also uses data analysis for selection.
The data department of the England and Wales Cricket Board (ECB) has been working for two years analyzing all previous World Cups. They are looking for insights on the characteristics common to past winners.
The key metrics which emerged were team strike rates (the scoring rate) at various points in the contest, the win-loss percentage, and more importantly, the experience of the teams.
ECB analysis showed that the players in winning World Cup teams had each played between 70 and 80 matches for their countries.
Two years ago, when they identified these metrics, the average for the England team was lower. So, a policy was brought in to commit individual players on more international games. This increased the team average for the World Cup.
England also came up with a data-driven approach to the scoring tempo of their innings. Historically, in one day, cricket teams begin scoring at a slower rate and build that throughout their innings. But England’s analysis identified that the early stages of the innings as an opportunity for rapid scoring. It showed that it would get harder to score the longer the innings progressed.
The result is England’s scoring in the first 10 overs (the first 20% of the innings) is higher than any other international team over the past two years.
The Winner is Data
England’s data analyst also delivers a very detailed analysis of individual opponents. England players can view scouting reports on opponents on their iPads, where strengths and weaknesses are dissected and rated.
If an opposing batsman is better than his career average against a particular type of bowler, he is given a plus score. If he is worse than his average, it’s a minus score, while nought is the career average.
This data influences the captain’s decisions on choosing bowlers against particular batsmen.
In the past, these decisions relied on the captain’s intuition and feel for the game as it was being played. In the 2019 World Cup, there is every chance there is detailed data analysis behind them.
When the World Cup is held aloft by the winning captain later this month, the successful use of “criclytics” may have proved its worth.