As I have been tweeting, blogging, and updating Facebook about the Sloan Sports Analytics Conference, I keep hearing comments like: “I wish I could go, but I will never be able to” and “this is definitely on my bucket list now.” A few things about this. First, at $500 for non-student admission, it’s a bargain; even with hotel and airfare I’ll bet most of you could do this conference for well under $1,500, and it will be a truly memorable experience. So start saving a few dollars a week in a cookie jar today. Second—and I don’t think people realize this—anyone can attend. You don’t need to work for ESPN or an NBA franchise. My sister has a friend who does Account Management for Google, but he loves sports and analytics so he pays his own way to fly out every year from the Bay Area. All are welcome.
The other thing which pleasantly surprised me (I touched on it yesterday) is that you do not need to be a Ph.D. candidate in advanced statistical modeling or econometrics to thoroughly enjoy the conference. There were some sessions that really stretched that area of my brain, and others that were accessible even to non-sports fans, let alone non-academics. So don’t let that scare you away.
Highlights and thoughts from day two:
- There was a request on Facebook to hear more about Bill James. As the godfather of the Sloan Sports Analytics Conference, he was definitely one of the stars of the show. Unfortunately, I didn’t feel like he shared much new information about himself or his work. Simmons did a live BS Report with James at the end of day one, during which they rehashed his rise from unheralded part-time stats geek to patron saint of sports nerds, but it’s all stuff we read about in Moneyball. Day two featured a “Boxscore Rebooted” panel with James, John Thorn (MLB historian), and John Dewan (baseball info solutions), but most of it is so widely accepted in these circles at this point (“Wins for a pitcher is too arbitrary! The Internet makes analytics easier!”) that I barely took notes. To be honest, it felt really strange to be so nonplussed by this guy who quite literally invented advanced baseball analytics. In talking to a few other attendees, I got the sense that I’m not the only one. We all have tremendous deference for what Bill James has given the world, but he does not seem to be on the bleeding edge of sports analytics anymore.
- The conference (at least this year) leaned heavily toward basketball, probably because the millennials who dominated the conference are in a demographic where the NBA is excelling, whereas I believe I heard that the average baseball fan is between 45 and 55 years old.
- I reviewed three basketball-themed research papers in yesterday’s post, but the best was yet to come. The two best research papers by far (in my mind) dealt with “spacial analytics” in the NBA, meaning the study of where the 10 players and the basketball are placed on the floor (or in the air) when key events occur, as opposed to pouring over isolated numbers to obtain insight. Jared Dubin of Hardwood Paroxysm did an excellent job reviewing these two presentations (he even has screen captures), so I won’t go into too much detail.
- I will add that I thought the rebounding study was not fully matured—it presented a ton of potential to help teams understand how to position players in rebounding situations, but it wasn’t quite there yet. Key Insight: Teams’ offensive rebounding percentage decreases significantly the farther the shooter is from the basket, until you get to the three point line. Behind the three point line, offensive rebounds are more common than for mid-range jumpers. Especially considering that neither a mid-range jumper nor a three is likely to generate a lot of free throws, it stands to reason that mid-range jumpers are the least effective shot on the floor (which we’ve all kind of known for some time, but it’s nice to have data to back up the theory).
- My absolute favorite research paper was Kirk Goldsberry’s creation of the “Range %” number, a statistic which tells us the percentage of spots on the floor where players are effective scorers (defining “effective scorer” as “one point per FGA”). The average NBA player is effective from 17.2% of the 1,284 spots on the floor that Goldsberry measured by breaking the floor into a grid. Even though Tyson Chandler leads the NBA in FG%, he is far below average in Range %, scoring effectively from just 4.3% of the floor (not that anybody thinks Chandler’s high FG% means he is a great shooter). Dubin recaps the top few players in Range %. I was mildly surprised that Steve Nash beat out Ray Allen for the top spot (using data from 2006-2011), but mostly the data confirmed what you would expect in terms of the most comfortable shooters and least comfortable shooters. Key Insight: I can’t express this in terms of a specific recommendation, but Goldsberry’s most immediately applicable contributions are “heat maps” which show exactly where players are effective scores, as well as where they are less effective but still love to try. If I were a coach, I would buy Goldsberry’s technology (he did have a chance to share his methods with Mark Cuban at the end of the conference, so I assume the Mavs will be employing it shortly) and try to get my defense to force opposing scorers to the spots on the floor where they can be coaxed into shooting despite low effectiveness. Similarly, I would design plays that put my players in the best position to score from spots on the floor where they shoot well. It’s not rocket science, but I believe it would work. Isn’t this the kind of thing that Shane Battier has been doing for years? (And where has he been getting his data? Presumably just from video scouting. Goldsberry’s method is more complete.)
- Best panel of the entire conference in my mind was Saturday’s “Fanalytics” featuring Bill Simmons, Jonathan Kraft, Tim Brosnan (EVP Business, MLB), Nathan Hubbard (CEO of Ticketmaster), and John Walsh (EVP, ESPN). It was supposed to have Mark Cuban on it as well, which would have been even better, but Cubes was running late. The whole thing was about improving the fan experience through technology. This one deserves sub-bullets:
- The NFL is improving in every possible metric except for fan attendance. Today’s fan needs to be able to use the Internet on mobile devices (for Twitter, fantasy football, live video of other games, etc.) or they won’t come to the game. Mark Cuban doesn’t want people using their cell phones at NBA games, but the NFL recognizes that its whole fan experience model is different because of the pace of the game (frequent stops and starts) as well as the nature of the NFL (everything is happening all at once, on the same day of the week).
- Kraft: “We’ve spent $2-3 million [to upgrade WiFi infrastructure at Gillette Stadium] in the last couple of years.” He continued, saying that to allow 70,000 people to stream video over WiFi at Gillette would cost “literally tens of millions of dollars.”
- Simmons asked whether it would be feasible to charge different prices for tickets not just by section, but by individual seat. For example, one section at an NBA game might stretch from the baseline to almost mid-court, and why are those tickets priced the same? I had wondered this, since obviously the technology to price tickets on a seat-by-seat level exists. The answer is that if a fan sees that the guy next to him has a different face value on his ticket, he is likely to get resentful and angry. So they do it by section and live with the fact that this isn’t fully optimized pricing.
- On the night of the AFC Championship Game, when Kraft wanted to relive Billy Cundiff’s missed FG, he went not to NFL.com, but to YouTube. This is surprising since the NFL maintains strict media rights, and the video was available on NFL.com. Why YouTube? Because a guy sitting in the endzone had the perfect angle to film the kick sailing wide left, and had uploaded the video. It was the best angle Kraft had seen. The lesson regarding NFL media rights and fan-shot video? “You can’t stop it, so you better start learning how to use it.”
- TicketMaster operates both a primary ticket vendor and a secondary market vendor (TicketsNow), so they can use Omniture (nice shout out for my friends) to analyze ticket re-selling and compare with original sales. According to Hubbard, “Technology is showing us that our tickets are worth more than what we’re selling them for.”
- Weird recurring theme of the conference was presenters’ inability to pronounce player names. The professionals did not have this problem, but the student researchers did. The two most egregious (and there were others) were the old classic “Da-RON” Williams instead of “DARE-in” and the even-less-excusable Kevin “Dur-ONT” instead of “Dur-ANT.” I mean, Kevin Durant is a top-three player. How can you be presenting on the NBA at a conference of sports nerds and not pronounce his name correctly?
I could keep going, but I need to stop somewhere. Suffice it to say, SSAC was an absolute blast. I can already see myself looking at certain aspects of game action and the sports world at large a little differently, in a good way. As I said to people numerous times during the conference, I will definitely be coming back, even if I have to plunk down my own money to do it. Sports and data, together at last. I think it’s a beautiful thing.