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My NBA fantasy league has defined the morning routine – watching highlights and browsing Reddit for half baked insights about the obvious: how statistics in the NBA have changed drastically in the last decade. In fact, how the game has changed. You don’t have to be a data scientist to observe that Steph Curry has changed the game and shooting 3 pointers is now the way to go. You don’t have to be a San Antonio Spurs fan to know that using the clock and passing the ball is a thing of the past (or is it not?)

The average 3-point shots taken per team has increased by 50% since 2012. But, it is indeed fascinating to see granular data on a player’s movements or even certain tells before some moves. The NBA is leveraging data analytics better than ever to design winning strategies by analyzing data using machine learning models. The use cases get stronger with predicting and avoiding player injuries, scouting players based on shooting statistics and changing the game to really ‘how many will sink those three pointers in’.

Not the biggest soccer fan but always interested to learn how sports teams leverage data to reduce the time to get to relevant insights, I spent the best hour this morning watching Alan Jacobson, Chief Data & Analytics officer at Alteryx talk to people with the most enviable job titles for any sports fan- Ravi Mistry, Senior Football Intelligence Manager at Manchester City Football Group and Richard Battle of Left Field football consulting. 

Fascinating yet comforting to see that the use of data across various different sports comes with similar themes – the necessity of human context, storytelling with data (translating data into decision making) and similar use cases: recruitment, scouting, player safety, how to tweak situations for using a player. Recruitment is a tailor made use case for Machine Learning and predictive analytics. 

Ravi gives an excellent analogy of driving a car but with rear view mirrors. You can do it without the mirrors, but it is much easier with it. A terrible sports pun but, it is worth having that assist with data. Alan Jacobson shared the use cases of Alteryx as a very powerful tool for sports beyond the obvious NBA, NFL, F1 to more unsuspecting organizations like WWE. He sites the truth about sports really being a business and teams need to find that competitive advantage using data and analytics. I loved his favorite use case of using analytics around augmenting human behavior: using computer vision of players to see the emotions of players and its positive/negative outcome in sports.

Soccer with the trendy ‘expected goals’ (a better way of counting shots on target) accounts for the obvious understanding of how close you are to the goal, the angle etc. But what we are able to do now with data is talk about these very obvious things but with greater precision starting from career trajectory, season outcomes to even player level outcomes. This is where the human element of storytelling of data becomes paramount, where one can not just show an outcome, but a process of arriving at that outcome. 

What the future of data & analytics in sports holds, only time will tell. However, ‘Only time will tell’ is a phrase of the past, as there is a lot of publicly available data in sports to jump into to make exciting predictions about the future.

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