On Thursday morning, I woke up at 3:45am, paid $40 for a taxi, spent an an hour in line at the airport, and checked in to a decrepit motel, but after that, the quality of my day rose at a meteoric rate. I had the privilege of attending the 2nd annual SABR Analytics Conference, which took place in Phoenix from Thursday to Saturday, and it was a truly phenomenal experience. I learned a ton and met a lot of really smart and talented people in the baseball industry. In fact, I learned so much that I can't talk about it all in one post, so I'm going to split it up by day. Here's day one:
Diamond Dollars Case Competition
Before the official beginning of the conference, YarcData hosted the Diamond Dollars Case Competition, in which undergraduate and graduate teams prepare a presentation in response to a hypothetical baseball operations decision. This year, the case was simple: what do you do with Mike Trout in the 2013-2014 offseason when he has one more year of elite performance under his belt?
Unfortunately I wasn’t able to attend most of these presentations, but I did catch the winning team – Pepperdine University. Pepperdine, after a fantastic analysis and presentation, decided that they would offer Trout a 7-year, $126 million contract. They came to this conclusion based on the performance of other players who were elite before the age of 21, as well as by evaluating the risk of injury, market values, and financial security for Trout. It was a fascinating case, and the winners were well-deserving.
Opening Remarks - Brian Kenny
The SABR Analytics Conference officially began with a talk by none other than the biggest face of sabermetrics on national television, Brian Kenny. And Kenny didn’t just talk vaguely about the concept of sabermetrics – he offered drastic, interesting, and research-worthy proposals regarding the future of baseball.
Kenny’s first major suggestion referred to the way in which teams use starters and relievers. Data show that starters perform worse when the hitter sees them for the second and third time and relievers prevent runs at a better rate than starters. Consequently, Kenny came to the intuitive conclusion that starters shouldn’t pitch more than three or four innings, at which point teams should throw everything they have at the opposing team, taking advantage of the increased effectiveness of relievers and platoon splits.
The next suggestion is more amusing, but possibly more likely and more beneficial to a team: a knuckleball academy. Knuckleball pitchers are historically more effective than other pitchers, as well as less susceptible to injury, so why aren’t teams taking advantage of that? Why aren’t teams, Kenny asked, creating schools or academies in which pitchers (and catchers) learn how to throw (and catch) knuckleballs? Kenny believes that this should happen, it will happen, and that MLB will respond by banning the knuckleball as they banned the spitball.
This is just a small portion of Kenny’s talk, but I've got a whole weekend of material to go over, so I’ll leave it at that.
Using Pitch f/x and Trackman for Novel Baseball Analysis - Alan Nathan
Up next, we were given the option of attending one of two research presentations; I chose Alan's over what sounded like a fascinating presentation by Graham Goldbeck on batted ball success by depth in the zone.*
*If you attended Graham's talk, I'd love to hear your thoughts in the comments section or in FanPost form.
Alan began by going over some of the fundamental differences between pitch f/x and Trackman; the former provides all the same information as the latter, but includes more points in the trajectory of the pitch and measures the actual release point and spin of the ball.
Using Trackman and pitch f/x data, Alan presented some interesting findings. Here are a few regarding knuckleballs:
- Trackman shows Tim Wakefield's knuckleball to have completely random horizontal movement.
- Higher speeds generally lead to less movement on the knuckleball. However, a mixture of speed increases effectiveness.
- Knuckleball trajectories are just as smooth as those of other pitches.
- In general, every additional 2 mph added to a pitch's speed is 0.5 mph added to the speed of the ball off the bat.
- For batted balls hit with a 25-35 degree angle, every extra mile per hour of batted ball speed leads to 5.4 extra feet in landing point distance.
- Initial velocity (speed and angle) isn't enough to predict the landing point distance of a batted ball.
- Hit f/x data + landing point are sufficient to determine the full trajectory of a batted ball.
- Balls are hit 2.5 feet longer per 10 degrees added, and about 5 feet added for every 1000 feet of elevation.
- About half of all balls hit with a speed of 90 mph or greater end up being hits.
- 80% of batted balls hit with an angle between 10 and 15 degrees are hits, regardless of speed off the bat.
The Romance of WAR (and FIP and DIPS and Runs Saved) – Joe Posnanski
Next up was Joe Posnanski of NBC Sports. Joe, unsurprisingly, spoke less about specific topics within sabermetrics, but about the appeal and importance of sabermetrics and analytics as a whole. As a writer, he explained that his goal is to "marry" the stories and the narratives, and to "bring a narrative to what we do." We should, explained Posnanski, "try to bring up these topics in a smart and interesting way that isn’t threatening", suggesting that WAR and FIP are examples of metrics that can produce fascinating stories when described in the right way.
Joe also spoke about the way in which the stats themselves can change the way that baseball is played. "When a number gets powerful enough, it affects the game – it changes the game," Joe explained, citing Saves as an example. Because the save stat was created, managers began to use their relievers in a different way, and front offices began to change the way that they valued players. Numbers, therefore, aren’t just a way that we explain a game, but are an integral part of how the game itself is played.
Player Panel – Brandon McCarthy and Javier Lopez
Brandon McCarthy of the Arizona Diamondbacks is known for being one of the few players who uses analytics to improve his performance as a pitcher. Unbenounced to me, Javier Lopez of the San Francisco Giants is similarly interested in using statistics as a pitcher, so it was fascinating to hear both of them speak. Instead of summarizing what they said, I’ll just give you some notable quotations and notes from the panel:
- Changed his pitching style in order to be more like Roy Halladay (also, "Roy Halladay has a nice beard").
- Wins and ERA are still used in arbitration hearings.
- "I’m tired of talking about wins."
- Talking about clubhouse chemistry: "If you take away Jonny Gomes and Brandon Inge, the A’s are a 70 win team." (Note: I personally don’t think he actually believes this. The point is that he believes clubhouse chemistry to be important in general.)
- Doesn’t change his pitching style or selection based on the score. He knows pitchers who claim that they do, but he doesn’t believe that they do so as much as they claim.
- "I wouldn’t be around if not for the numbers."
- "All the information was eye-opening for a guy who was just getting his feet wet in the league [with the Red Sox]."
- Jason Varitek was a great source of numbers and game-calling.
- "When you’re not an overpowering pitcher, you have to rely on [the numbers]."
- "The 9th inning is a different beast" because there is no safety net. In different bullpen setups (closer by committee for example), the 9th isn’t as important."
- "When a guy is locked in, I try to pitch inside and move feet."
Bloomberg: The Next Generation of Baseball Analytics - Bill Squadron and Jerry Dipoto
I won’t spend much time on this one, since I’ve already written a lot and much of the session was a demonstration of Bloomberg software. The most exciting part of the software was the ability to see video of every single pitch for the last few years right next to in-depth information about those pitches.
After the software demonstration, Angels’ general manager Jerry Dipoto spoke about how the Angels use analytics and Bloomberg software as a team. As expected, Dipoto repeatedly emphasized that the team combines statistics and scouting, and that every decision is made with both in mind. The most interesting thing he said, in my opinion, was this: "This most important thing I do every day is manage risk." I don’t usually think of a GM’s job in this way, but it makes sense; every decision in baseball centers around managing risk and reward.
There was so much more information and so much more that I learned in just one day than what I wrote here, but I can only take down so much notes. Check back for my recap of the other two days of the conference later this week.