Running a professional sports club has always been a function of superior ownership, front offices, player selection, coaching, ensuring right coordination among the players and making the right decision in the field. And the fist two factors (that’s money) influenced all the other factors for long time in in sports. But in 2002 a man named Billy Beane changed the rules of this unfair game by experimenting with empirical analysis in baseball. With just a fraction of budget of teams like New York Yankees, the A’s won their division and made the playoffs.
While Billy Beane of Moneyball fame certainly did not invent sports analytics, he was the first to bet big on analytics in a professional management setting. And in the subsequent years sabermetrics (as baseball analytics is called today) evolved into the sophisticated sports analytics that almost every major clubs use today.
And due to this widespread acceptance of analytics, professional analysts are in huge demand across the sports industry. Role of a data scientist/ analyst in a sports club typically spans across 5 areas:
Strategy: Understand in-market competitive landscape, sports marketing trend and sports-related policy updates, with insightful proposals to optimize sales pitch strategies, hiring of players, achieving growth in sponsorships, retail & licensing, providing the right information to coaches that’s comprehensible and can be readily used
On ground actions: Gathering data from wearable technologiesand other IoT devices, data collection from videos etc., using it to derive insights and present it in a usable format to players and coaches
Commercial Process Planning: Managing operations, revenue from tours, player image rights research and business intelligence
Media: Social Media content strategy, impactful media coverages, brainstorming on daily communications etc.
Fan Engagement: Predicting the wants and needs of fans to serve them better and increase team revenues, selecting venues, match timings, incentives for season ticket holders etc.
A career in sports analytics requires you to be a jack of many trades. Some typical expectations from the role are:
1. Ability to apply statistical modeling and quantitative analysis to a variety of data sources, for the purpose of player evaluation, strategic decision-making, decision analysis, etc.
2. Ability to collect and process public and private data for additional analysis
3. Carrying out research with ad hoc queries and quantitative research in support of the sports (like general Baseball Operations tasks) and publishing the results in a format consumable by non-technical staffs
4. Various game-day duties, as applicable from time-to-time