Data games

Science and mathematics have had an intimate relationship for at least the past 400 years, ever since they began to develop rapidly from the 17th century. Mathematical advances laid the foundation for scientists to express natural laws cogently and quantitatively. Beginning with calculus, advances in physics gave mathematicians the intuition to explore new branches, which in turn took on lives of their own and developed independently. This symbiotic relationship continued for a long time.

The problem of light bending upon entering water inspired the development of the calculus of variations, a powerful branch of mathematics with applications in classical and quantum mechanics, as well as other fields of science. The problem of vibrating strings inspired the development of partial differential equations. The study of electric and magnetic fields inspired the development of vector calculus. On the other hand, mathematicians had developed the tools to describe curved space before Einstein used them to develop his theory of gravity. Such examples abound in the history of science and mathematics. They remain present in the 21st century.

In the early 19th century, mathematics began to make inroads into economic theory, but many economists initially resisted the use of maths in their field. Statistics began to be used to record and analyse birth rates, crime rates, and other aspects of collective social behaviour. In the 20th century, especially after the Second World War, the use of mathematics exploded in almost all areas of human endeavour. Game theory became a part of strategy. Operations research was used in several industries, ranging from airlines to supply chains. The finance industry became heavily mathematical. Epidemics began to be studied using graph theory and other branches of mathematics. By the late 20th century, data analysis became central to business. By then, most branches of human activity had become quantitative.

Our Cover Story this time describes one such field – sports – where human skill and intuition reigned supreme. Readers of Shaastra may remember cricket in the 1980s and 1990s, when traditional technique and aesthetics drove decision-making. For a batter, cricket pundits would look for the correct position of the feet, how close the bat and pad were together, how softly the batter held the bat, and so on. For bowlers, they looked for where the feet landed, the wrist position of the fast bowler, the seam position for the fast bowler as well as the spinner, and so on. The elements that went into correcting mistakes and playing good cricket were straightforward and noticeable from the vantage point of the pavilion. All of this changed in the 21st century, when video analysis came in, followed by deep data analysis.

Several factors drove this transformation. The quality of the videos made it possible to extract detailed information about the game that was not discernible to the naked eye, even for experts. How does the angle of the bat matter in different situations? How does it change when a batter is out of form? Does bat speed change similarly? How does the path of the bat change with the bowler and during the course of the game? How do the bowlers adjust to different situations? These questions could now be answered with precision and the findings interpreted with detailed analysis. The data analyst generated more strategic decision points than coaches and captains had earlier. And researchers entered the field with their complex mathematical and data-analytic skills.

As Pallab Roygupta's Cover Story shows, sports analytics is expected to grow from $5.6 billion now to over $23 billion by the early 2030s. It is a richly mathematical field that attracts academicians, who find new applications for the mathematics that they have been using in other fields. Sports analytics may not be as mathematically deep as physics, but it develops new methods and tests new ideas under real-world conditions. Many of these ideas are easily transferable to scientific disciplines such as drug discovery and disease modelling, and risk modelling in finance. As sports analytics increases in sophistication, its methods are moving into other areas of human activity and enriching them. Time and again, modern research is showing that the world of ideas has a remarkable unity. It is a theme that we have dealt with in Shaastra before. It is one we will return to repeatedly in our future stories.

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A whole new ball game