Artificial intelligence and data science are already creating a huge impact on various industries. Now these technologies are extensively being used to analyse the game of cricket as well. Sport media firm ESPNcricinfo, in partnership with the Indian Insitute of Technology Madras, unveiled a tech platform called ‘Superstats’, which helps analyse the sport.
The platform is a combination of stats metrics - luck index, forecaster and smart stats -- that use data science for the first time to give a context to every event in a game.
IIT-Madras Researchers and Gyan Data Pvt Ltd, a company incubated at the institute, worked with cricket experts from ESPNcricinfo to create Superstats with the help of ESPNcricinfo’s rich ball-by-ball database. The database has more than ten years of detailed data, and scientific methods, processes and complex algorithms based on machine learning. The algorithms process accurate, fast data, quantify the impact of luck and analyze the real value of a player’s performance in the game of cricket in real time, according to IIT Madras.
“While there might be many interpretations for luck, these algorithms rationalize and consistently quantify luck events so that a whole tournament with matches that occurred in disparate circumstances could be compared in an ‘apples-to-apples’ fashion,” said Prof. Raghunathan Rengaswamy from the department of chemical engineering at IIT Madras. Rengaswamy and Prof Mahesh Panchagnula of IIT Madras led this project along with the ESPN team.
So far, the impact of luck on match results have only been spoken about in qualitative terms. For instance, a bowler can beat the batsman numerous times but still have zero wickets or an edge from the bat can still concede a boundary. There is no way to assess how lucky a team or player has been. The ‘Luck Index’ a metrics that measure the quantum of luck by factoring the occurrence of several events including toss, umpiring errors, dismissal of a no-ball and dropped catches. The core of the algorithm is in scoring various luck events that occur in a match. Data science algorithms play out different scenarios to evaluate the impact of luck events. Generalization of the impact of the luck events through an equivalent “runs potential” brings all these events to a common baseline. This makes it possible to answer questions related to luckiest team and players in the context of an entire tournament.
Rahul Dravid, former Indian captain and head coach for the Under-19, said that luck played a huge role in his career in 2009. “I was on the verge of being dropped and was given an extra opportunity in Mohali against England. I was batting at No. 3 and Stuart Broad bounces me. It was a top edge and as soon as I hit it, I was 'Oh god, I’m out again'. It just falls (short) of Matt Prior and the fine-leg fielder running in. I get a hundred in that game and go on to have a couple of good years, including three hundreds in England,” said Dravid.
The platform has ‘forecaster’ metric which functions as a prediction tool. It provides win probability for the chasing team, likely score for the team that bats first, runs and wicket probability in the following over, for each bowler and so forth.
The data science algorithms use forecasting methods that train on past data to uncover trends and patterns during different periods of play. They then adapt based on actual match data resulting in highly accurate predictive models. Smart Stats has added metrics such as smart wickets, player quality index and pressure indices, to cover all facets of the game. These metrics determine most impactful batsman or bowler in a match in terms of runs scored or saved and in each phase of the game such as powerplay and death overs.
Rengaswamy said the AI work at IIT Madras was targeted towards several verticals such as fintech, manufacturing, smart city, biological systems engineering, and healthcare. “Through this project, we have now forayed into sports analytics as well,” he added.