"Dick Vitale, already emotional from having delivered a eulogy at a funeral, started crying over the phone late Thursday afternoon when he heard Gary was retiring.
'I got to call him,' Dickie V. said. 'I got to call him. The guy poured his heart out. I’m totally stunned. He has incredible passion for the game. He left everything on the floor. He coached every possession like it was his last.'”
Now, he’s moving on, and winter nights in this town won’t be the same without his players and him lighting up Comcast Center for two riveting hours.
If you can put aside the potentially scary intimidating $14.99 price point, what you’ll find is one of the deepest stat apps you’ve ever seen. You can chart the three-year statistics for every team or player in Major League Baseball in an insane number of categories.
I think wins are not one of the most telling stats of how a season is going for a pitcher. I think even ERA is skewed on a team-by-team basis.
Last Friday (April 8th) I attended the Innovation in Sports Conference at the University of Pennsylvania’s Wharton School of Business. Though all panels were interesting in their own right, the one I was looking forward to was, “Quant Jocks: Innovations in Sports Analytics”. Among the panelists were Chis Marinak (Senior Director Labor Economics, MLB), Sean Forman (Founder and President, Sports Reference LLC), Sam Hinkie (Executive VP of Basketball Ops, Houston Rockets, and Steve Flatow (Head of Marketing, Bloomberg Sports).
Relative to the famed MIT Sloan Sports Conference, the topics covered in this specific panel were more macro level, centered around the role of analytics in commerce as well as the job opportunities available in sports analytics. However, this synopsis by Ben Alamar, founding editor of the Journal of Quantitative Analysis in Sports, on the Sloan Sports Conference contains most of the information that was presented during this panel at Wharton.
One of the hottest topic on this panel was field f/x. Chris Marinak stated that this is one of the recent breakthroughs analytics has made for the game of baseball and will allow a more precise, and accurate, measure of player’s defensive abilities and contributions. The problem with field f/x is the deluge of data it generates – there is a couple of terrabytes of information in one ball game. Trying to make sense of these play-by-play stats over a full season, then, is certainly a mammoth task. However, this is the direction analytics is heading; teams no longer the need to hire innovative Bill James types, but rather those with exemplary data management skills.
Speaking to landing jobs on sports teams, well, fugetaboutit!!
Just kidding. Sort of.
To get an upper hand in analytics, teams are definitely moving towards leveraging academia, and industry experts in other fields. Teams actually keep rather small in-house staff dedicated to analytics, which seems a bit paradoxical since this tool can generate more wins and revenue for the team. So why, when other large businesses have a much larger in-house staff dedicated to analytics, so do sports teams have such a small group of “quants”? Well, sports teams’ revenue fall in the lower hundreds of millions, while corporation’s revenues are usually in the billions. This discrepancy allows teams to really only hire five or ten dedicated quants as in-house staff, thereby putting them in a position to rely on outside help/expertise in other industries.
Overall, it was a great conference. For some stat geeks that dream to work in the world of sports, there was certainly a gloomy picture painted of a diminished demand at the organization level for them. If these trends continue and it continues to become impossibly difficult to work in sports analytics, I think it will be interesting to see how much more interest this field will have.