How analytics could have saved team India the blushes in Australia!


Recently a good article in economic times compared the performance of the Indian cricket team to Nokia. The used data from the 1990’s to show how Nokia’s slide had begun well in the late 90’s but the implication of that slide was felt much later. Team India on the other hand just had a 2 year stint on the top, and since then has been on a merry road to bottom. It got me thinking on an interesting topic.

Can analytics and the knowledge of analytics be applied to sports?

Technology recently has made an entry into sports. Now there are replay actions, Snicko-meter and stump cameras that give us enough insights into the cricketing action. The world of lawn tennis has the Cyclops (The line calling machine) to identify faults and other errors in the game.

But can technology be used to predict the outcome of a game or how the players are likely to perform in a particular match.

The first recorded use of analytics in sports was not surprisingly in baseball. The Major league realized the importance of understanding how past performance data could not be relied upon and they needed new technology to find potential signings and also judge the composition of the team. So every team on the major league spends millions on technology to gather data and run analytics on it to determine the strategy of the team. This is also used in future team selection and player salaries.

The other sport when analytics has made its presence felt is lawn tennis. All the four major slams (Australian, French, Wimbledon and US open) have used analytics run by IBM to generate statistics and insights. A good example of this is the dashboard between a match that shows data like the number of first serves in and the percentage points won on it. Every player is given a copy of the match analytics which has data in three forms the video, statistics and the scores. This is probably a good way to learn from a match and to apply those learning’s into a new match.

Baseball is another sport that runs the same way with analytics helping in running the team.

But as usual, the humble game of cricket has miles to go before coming close to technology adoption of either baseball or tennis. Today we use very basic data to show which team batting first wins and the average score on a particular pitch.


As I was doing some research, I came across this interesting blog post that talks about how predictive analytics could be used in a game of cricket. Again this is very basic data mining nothing compared to the smarter analytics that run the major league or the Grand slam competitions.

I think the important learning from the Australia series so far is the need for analytics in critical aspects like team selection. Currently we are using historical data to make the predictions. A good example is the selection of Dravid, Sachin and Laxman who all failed to score in four test matches. If predictive analytics would have used, I am sure none of the three would have been selected.

I think the BCCI should take a cue from baseball and invest in some analytics to drive the cricket game plan forward in the nation.

Would love to hear your thoughts on this…

  1. Balaji yadhav says

    What if the analytics say that you should drop Sachin. Based on the analytics probably Ponting and Michael Clarke should also be dropped before the India Series. Both of them had terrible averages for the past two years still they were the highest run getters in this Series. India would have probably won if Australia had actually followed this analytics approach.

    1. Dr Vikram says

      Hi Balaji
      The example you are giving is how teams are selected. This is a good example of using historical data. Predictive analytics is something that can help team selection keeping in mind the future.

  2. Technology Centric says

    “But as usual, the humble game of cricket has miles to go before coming close to technology adoption of either baseball or tennis.”

    Cricket is extremely tough and complex, compared to other faster games. I think there already is a lot of statistical data already available about players, and I won’t be surprised if international coaches today use data about opponent team members’ playing styles to chalk out strategies.

    I’m not sure if the use of statistics in cricket can be compared to equivalent strategies in lawn tennis and other games, particularly because key performers have only one chance – a single loose stroke by a legendary batsman can define the difference between victory and defeat.

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