Third and Six

By Max Kaplan

League-wide, the conversion rate on 4th down is 50% but it jumps to 65% on 4th and 1. Ever wonder how likely it is to convert in a given situation? I ventured to find out, armed with the entire season’s worth of data from 2012.

The goal of 1st down is to create a manageable 2nd down. The goal of 2nd down is to create a manageable 3rd down. But you have to convert, eventually. What are the percentages of converting on a given down and distance?

1st down conversion by situation
*2012 Season
Note: Bigger rectangles are ranges of situations – used when
there weren’t enough plays in a given down and distance.

Continue reading “Third and Six”

MIT Sloan Sports Analytics Discussion

HeaderLogo

Princeton Sports Analytics writers Max Kaplan and Philip Chang are stat nerds. They have just returned from the so-called Geekapalooza, MIT Sloan Sports Analytics Conference in Boston. The conference included many faces of the sports analytics world, including Daryl Morey, Nate Silver, Mark Cuban, and many others. However, there were also ex-coaches and GM’s who were also behind the learning curve. Here is a discussion between Max and Philip about the most interesting aspects of the conference.

Max Kaplan: Sup, Phil. 2 days. 2700 people. 1000 Students. Dozens of panels. Who did you find most interesting among the panels?

revengeofnerdPhilip Chang: Howdy, Max. And before I say anything, let me give a big thank you to the Princeton ORFE department for allowing us to go.

Anyway, while the opening panel Revenge of the Nerds (this year, featuring Mark Cuban, Nate Silver, Daryl Morey, Paraag Marathe, and Michael Lewis) in past years has been the conference’s highlight, it really featured a lot more fluff than I had expected. There was almost no observation of the analytical aspect itself; rather, it seemed to be more of a discussion of different applications of those statistics, and how the structure of player/team evaluation has changed throughout the years. Not that that’s a bad thing, but that steered me away in response to your question, and thus, *upset pick* I really gotta say that the “Predictive Sports Betting Analytics” panel seemed the most contentious and informative of the bunch.

MK: Upset pick? What was the spread? Or the least you could do is explain to me why you were interested in the gambling panel.

PC: Haha, good one. Gambling is typically seen as a “dirty” part of sports, but through the eyes of professional NBA bettor Haralabos Voulgaris, and his interaction with “21” star Jeff Ma, bettor blogger Chad Millman, and director of bookkeeping organization Matthew Holt, we were able to examine how practically sports games, and seasons, could be predicted based on a) the models one chooses, and b) how closely one follows that sports. Haralabos (Bob) described how he closely followed the NBA, which allowed him to place bets with winning strategies on particular games. For the gaming commission, however, it was much more difficult because Holt and his compadres have to place lines on nearly every sporting competition on the planet, with not nearly as much research on a particular competition as Bob has had. To me, it seemed to be a really interesting, practical, and eye-opening discussion that captured exactly what is possible to predict in sports with purely a model, and how those models sometimes don’t take into effect things like lockout seasons, personal issues, etc. Thoughts?

Continue reading “MIT Sloan Sports Analytics Discussion”