In the recent IPL auction people wondered why the world’s then number 1 leading ODI and T20 bowler Imran Tahir was not picked by any team. A relatively unknown K Gowtham was bought by the Mumbai Indians at 2 Cr. What drove this counterintuitive decision making by the cricket franchises?
Ever since ‘Moneyball’ made math glamourous in sports, teams worldwide are collecting and crunching numbers about everything including player statistics, ground conditions and details about followers. Teams are investing millions of dollars in players, and they use new medical and health analytics tools to maximize said investment. Franchises are mining information collected from fans when they make ticket purchases.
How does this drive decisions on the field? How is data helping teams build a fan base and generate profit? What does it take for a data science enthusiast to excel in the field of sports analytics?
This session will cover
– Sports analytics – an overview
– Applications of analytics in sports
– Case studies
– Skills required to succeed