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Analytics and the World of Sports: The Empirical Facts

The latest buzz words, ‘data analytics’, has swept most of the Industries, as far as I have researched, and sports industry is no exception. Just like other industries, data analytics has found wide variety of applications within the sports industry. This includes players’ performance analysis, opposition strength/weakness analysis, in-game strategy analysis and a lot more. In this article, I have tried to cover some empirical evidence from different sports that will strengthen the fact that data analytics can significantly influence the way sports are played and watched. This will also highlight the strategic importance of data analytics in sports industry. The other phrase for data analytics in sports industry is sports analytics.


1. Long Jump

In Long Jump, when we talk about distance, it is measured from the take-off line. There is a trade-off between taking off close to the mark (take-off line) and the risk of disqualification. This significantly affects an athlete’s jumping strategies and whoever jumps furthest does not necessarily win the competition.

In Berlin Olympics (1936), Jesse Owen fouled his first two jumps during the qualifiers – a third foul would have resulted in his elimination. Analysis pointed out that Owen’s world record was a meter longer than the required qualifying distance. Owen took-off from a check mark well short of the take-off board, qualified and won the gold medal in the finals.

In Long Jump, the margin between success and failure is very small. Quantitative approach can indicate how much difference is made by the choice of take-off point. In Athens (2004), Russians took all the three medals in the women’s event (with distances of 707 cm, 705 cm, and 705 cm).


2. Cricket

In 1992 world cup, rain interrupted the semi-final match between England and South Africa. The rain delayed the match and each team was restricted to 45 over. Batting first, England scored 6/252 and South Africa in reply were 6/231 after 42.5 overs, before rain interrupted the play with South Africa requiring 22 runs from 13 balls with 4 wickets remaining. When the game resumed, the Most Productive Overs (MPO) method, controversially, revised South Africa’s target to 21 runs from 1 ball, an impossible position, and England advanced to the World Cup Final.

This match led to the development of Duckworth-Lewis method for calculating revised targets in one-day matches. Extensive research and statistical methods were the foundation of this method which has been the official method used in one-day international matches since 1997.


3. Baseball

The best empirical evidence to site under this sub-section is the account of Oakland Athletics baseball team’s 2002 season and their general manager Billy Beane’s attempt to assemble a competitive team.


~Ashish Soni



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Tags: Analytics, Data, Sports


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