DHONI’S VIRTUAL TRIUMPH
A dedicated team of 45 technologically accomplished backroom boys helps Team India hit troublesome opponents for a six
Right through the sixth edition of the Indian Premier League ( IPL) this April, Chris Gayle was blurring the lines of regional rivalry that the league had worked so hard to create. As he blasted his way to 175 not out against Pune Warriors on April 23, the chorus of appreciation led by his Bangalore skipper Virat Kohli was joined by some of the opposition players and fans. But even as he was turning out to be the toast of the season, Team India’s analytics team was concentrating on the problem that lay ahead. In less than two weeks after IPL, Team India would have to face Gayle in the Champions Trophy in England, and they wanted to be ready for him.
With fingers flying on their laptops, the techies, who work as the national team’s brain trust, put together a plan. Bhuvneshwar Kumar was the only bowler who had escaped Gayle’s wrath in IPL, and Team India’s data scientists began looking for, through patterns and visual data modules, why Gayle had been unable to get him away. Their finding was that Gayle had problems hitting in the air against bowlers who were quicker than 140 kmph, compared to mediumpacers who bowled in the late 120s and mid- 130s. They also found that he had a problem early in his innings against pitched- up deliveries that swung away. When the teams finally met at the Oval on June 11, Gayle was dismissed for an 18- ball 21. Bhuvneshwar had the left- hander caught at slip with an away-going ball. Back in the dressing room, the team’s video analyst Dhananjaya pumped his fist. Mission accomplished.
There may be several reasons behind Team India’s Champions Trophy victory, and their ODI World Champions tag. One of them is the insight they get from a group of 45 techies working for Sports-Mechanics, which functions from a nondescript singlestorey office in Chennai’s Besant Nagar area. These analysts have given M. S. Dhoni’s team exclusive use of a unique predictive engine, the Real- time Decision Support System ( RDSS), which allows them to forecast a winning
score in any given situation, and provides five different ways to get to that target. In simple terms, it tells the team how they should tackle each bowler by studying patterns and predicting how they are likely to behave in the rest of the match.
The researchers have found, for instance, that Pakistan offspinner Saeed Ajmal bowls three doosras in almost every over, and if no boundary has been conceded in the first five deliveries, the last delivery is almost certainly the one that goes away. They have also discovered that Sri Lankan paceman Lasith Malinga bowls three full deliveries every over, and have advised the Indian batsmen that a short back- lift helps while dealing with his toe- crushing yorkers. Malinga’s career stats tell the tale. The fast bowler has an ODI career economy rate of 5.07 and an average of 26.3 runs per wicket, but against India, Malinga’s economy rate shoots up to 6.02 and average to 42.47. Kohli and Dhoni have a strike rate of 111 and 117 runs per 100 balls respectively against him.
Dhananjaya, who took over as analyst just when Dhoni was appointed skipper in 2007, says the Indian captain keenly uses technological inputs in all his decisions. “He listens to whatever I have to tell him. He then takes a call based on that,” Dhananjaya says. Subramanian Ramakrishnan, the founder of Sports-Mechanics— the firm that also works with the International Cricket Council, Asian Cricket Council, and the Sri Lankan and Bangladesh cricket boards— says: “Videos and data analysis was earlier used in post- mortem meetings but now it’s used in realtime. We have also started to marry a players’ gut feeling with an analytical layer.”
“For example,” he adds, “If Shikhar Dhawan feels that 150 runs is a par score and our engine says it’s 180, we ask the team which option they want to go for. Then we give the team five ways to approach both par scores. Given the bowler and the opposition, we tell them what our system thinks is the best way to approach the target. Video analyst is an old term. We are performance facilitators.”
Video analysis first started in the late 1990s, when footage of the first and last day of a training camp was compared to monitor improvements. Soon it developed into a day- to- day affair where live matches were analysed to find the weakness of opposition players. It was in 2007 that the analytics started to become more tactical than technical. Team India’s backend staff started coming up with complex algorithms that could gauge how an opponent would react to a given situation.
But interpreting visuals and data alone is not sufficient. The most important part is delivering it to players in a manner that is accessible. This is done through apps delivered through a fiercely protected gateway to which each player is given a login and password that is changed frequently. “Some players like to try out what we tell them in the nets before agreeing, and some review our suggestions with their personal coaches,” says Ramakrishnan.
Video analysis has come a long way since 2003, when Sachin Tendulkar had asked, “What is the guy with a laptop doing in the dressing room?” Work has already begun on the all- important tour of South Africa in November. The ongoing India A series in South Africa is likely to add crucial clues pertaining to conditions. Ask Ramakrishnan about how Team India is planning to tackle pace spearheads Dale Steyn and Mornie Morkel, and he gives you nothing more than a smile: “It’s classified.”