Analytics and cricket have not always been the happiest of bed-fellows. Andy Flowers notoriously adopted the ‘Moneyball’ philosophy for the 2014 Ashes test series, pouring over every statistic and every bit of data he could get his hands on. He discovered trends which suggested that fast bowlers were the most likely to thrive in Australia and made the decision to select three of them, against all conventional wisdom. And it turned out conventional wisdom was right. England were taken apart at the seams by Australia, pardon the pun.
In other aspects of the game, however, analytics have proven to be a highly useful tool. One of the International Cricket Council's (ICC) main objectives is to deliver real-time, interesting, storytelling stats to fans through the Cricket World Cup app or website. Half the pleasure of watching sport is in the discussion afterwards, and as televisions strive to provide the best, most immersive viewing experience possible, it is increasingly turning to analytics as a means to do this.
Cricket is awash with statistics, from the total runs scored by each player to how quickly they scored the runs. People want to know how many 4s and 6s have been hit, and every aspects of a player’s historic performance data in comparison with what is currently happening in game. With so much data floating about, there are also a range of records to be broken, and fans want to know immediately if this has happened.
The ICC and SAP also looked at correlations in the data to find similarities between skilled players using seven different characteristics. In this year's World Cup, they found that countries not traditionally recognized for their cricket playing abilities, such as Ireland, had strong performing players with similar characteristics.
These statistics appear in real time, and can be put up on television, and tweeted as they happen so that fans feel part of the action even if they are unable to actually watch the game. Driving excitement in this way is central to getting people involved in the sport, and encourage discussion that permeates through everyday life, in pubs and around the dinner table.
Analyzing how successful such tweets are, and how the sport is being mentioned across social media, is also vital to establishing a marketing strategy for the sport. By looking at the sentiment being expressed and which of the tweeters focused on the sport are proving the most influential, they can target their marketing, adapt the tone of their posts, and select which stats are driving the most excitement.
These stats are also still useful for coaches and players. Predictive analytics enables coaches to better forecast the results that their decisions will create by looking at past patterns in the data and factoring in the different variables. The lack of success experienced by Andy Flowers does not negate the potential usefulness that analytics can have for sides, it just shows that there is a greater need for human experience to be used when leveraging the insights.