24 November 2020 09:08:08 IST

A management and technology professional with 17 years of experience at Big-4 business consulting firms, and seven years of experience in high-technology manufacturing, Rajkamal Rao is a results-driven strategy expert. A US citizen with OCI (Overseas Citizen of India) privileges that allow him to live and work in India, he divides his time between the two countries. Rao heads Rao Advisors, a firm that counsels students aspiring to study in the United States on ways to maximise their return on investment. He lives with his wife and son in Texas. Rao has been a columnist for from the year the website was launched, in 2015, and writes regularly for BusinessLine as well. Twitter: @rajkamalrao

How the likes of Rajiv Maheswaran are using data to pull out winners in sport

Employing game analytics to pip rivals is an exciting area in the fields of data sciences, AI and ML

In 2011, a staid Hollywood movie, Moneyball , featuring Brad Pitt and Jonah Hill, became a huge hit. Sports-themed under-dog movies are nearly always successful but Moneyball was different. It brought to life a real story when the Oakland Athletics baseball team manager, Billy Beane, played by Brad Pitt, hired a young Yale economics graduate, played by Jonah Hill, to exploit player statistics and technology to take the team to a string of 20 wins, unheard of in baseball.

Employing game analytics to derive a minute advantage over rivals is an exciting area in the fields of data sciences, AI, and Machine Learning. Talent scouting and player evaluation, which help teams optimise their strategies, is unthinkable today without the use of technology. A study by the American Medical Association showed that a rule change for kickoffs in American football — based on extensive analysis of Ivy League game data by a team from Dartmouth – resulted in a dramatic drop in concussions. Head injuries have frustrated the National Football League (NFL) for years.








One of the pioneers of using advanced data analysis tools is an Indian-American researcher, Rajiv Maheswaran. An academic with a PhD from the University of Illinois at Urbana-Champaign, Maheswaran teaches at the University of Southern California and is a self-professed fanatic of the “science of moving dots.” He is also the founder of a company called Second Spectrum which uses Machine Learning to track the movements of dots in the sports world - that is, the movement of players during a game.


Rajiv Maheswaran, CEO, Second Spectrum



By having machines analyse thousands of basketball players and their shots in hundreds of games through millions of “dots” as players move around the court, Maheswaran is able to predict if a player is a good shooter who takes difficult shots or is a bad shooter who takes easy shots. This ability to analyse players is really important before a team signs n a player with a $100 million contract, he says.

Second-rate players the winners

Nearly 20 years ago, Billy Beane — as depicted in Moneyball — did something similar when he looked at baseball players’ batting averages, errors, walks, and steals to assemble a low-budget team with second-rate players but which goes on to win the 2002 American League West title. In cricket, this would be the equivalent of Afghanistan winning the ICC World Cup.

Maheswaran’s company is so successful that it is now the “Official Tracking Provider” for the American NBA and Major League Soccer, and the English Premier Soccer League. Its products are a blessing for managers, coaches, and players to combine intuition with new data about the minutiae of sports. It is no coincidence that in October, Billy Beane was reported to leave the Oakland Athletics and pursue a business venture with Boston Red Sox owner John Henry who has interests in European soccer.

In tennis, the data science pioneer is Craig O'Shaughnessy, the former strategy coach of Serbian tennis champion Novak Djokovic. He teaches players and coaches the patterns of play and winning percentages that dominate tennis by analysing every shot, and the position of the player, the ball, and the opponent. His graphical analysis is a common feature accompanying live ball-by-ball tennis commentary.



Craig O'Shaughnessy



In an interview with Techworld last year, O'Shaughnessy said, “It's about finding that out of 100 points, the 10 or 15 that matter the most, and explaining to [them] that these are the patterns of play that you want to repeat in these upcoming games to win those matches."



Down-the-line backhands

O'Shaughnessy says that by advising Indian-American player Rajiv Ram a few years ago to hit more down-the-line backhands, he helped Ram win sufficient matches to improve his ATP rankings from 270 to the top 100. Today, Ram is a veteran doubles player having won the 2019 Australian Open mixed-doubles with Barbora Krejčíková and the 2020 Australian Open men's doubles with Joe Salisbury.

Technology is revolutionising other parts of other sports too. Hawk-Eye is a computer vision system used in numerous sports such as cricket, tennis, badminton, rugby, and volleyball, to visually track the trajectory of the ball and display a profile of its statistically-most-likely path as a moving image.

Gone are the days when a batsman curses and walks when an umpire rules him out LBW. Today, they can appeal the decision to Hawk-Eye which predicts the likely path of the ball when it hit the pads. Cricket would never be the same but for reviews of LBW decisions.





In tennis, Hawk-Eye is about to retire a fixture of professional tennis for over 60 years — line judges. The ATP has already approved Hawk-Eye-only line calls this year to lower the risk of Covid-19 transmission on courts. The Grand Slams are clinging on to line judges but the trend is clear. Line judges will soon go the way of the typewriter and tennis fans won't miss them.