Speaker 1: Pushkin.
Speaker 2: Because I didn't know that sports biomechanics could be a career.
Speaker 2: I decided to go to grad school and initially start
Speaker 2: working on prosthetic limbs. So that was the first two
Speaker 2: years of grad school. Then about two years in I
Speaker 2: discovered baseball pitching biomechanics research. The funny thing that happened
Speaker 2: there was I gave a PhD committee meeting where I
Speaker 2: spent most of the time talking about prosthetic limbs, and
Speaker 2: then I spent the last few minutes as an aside
Speaker 2: on the baseball pitching research I found. And my committee
Speaker 2: was like, Jimmy, the last few minutes were way better
Speaker 2: than the first forty fives.
Speaker 3: So I was like, all right.
Speaker 2: So then they were like, we'll let you do baseball
Speaker 2: pitching biomechanics as your PhD work, but just so you know,
Speaker 2: it might be really hard to have a career doing that.
Speaker 2: But you know, they were like, if you want to
Speaker 2: go for it, you can go for it. And so
Speaker 2: I was like, you know what, Sure, I'll go for it.
Speaker 1: I'm Jacob Goldstein, and this is what's your problem. Today
Speaker 1: we have the second episode in our series about people
Speaker 1: who are working at the frontiers of technology to help
Speaker 1: a lead athletes perform better. My guest today is Jimmy Buffy,
Speaker 1: and as it happened, the concerns of his grad school
Speaker 1: advisors were unfounded. Jimmy has in fact made a career
Speaker 1: out of the biomechanics of pitching in baseball and sports
Speaker 1: biomechanics more broadly. When he finished grad school, he got
Speaker 1: a job with the Los Angeles Dodgers, and he went
Speaker 1: on to co found a company called Reboot Motion that
Speaker 1: works with teams in Major League Baseball and the NBA.
Speaker 1: Jimmy's problem is this, how do you take massive amounts
Speaker 1: of data about how professional athletes move and turn all
Speaker 1: that data into information that actually helps those athletes perform better.
Speaker 1: You end up doing your dissertation research on the on
Speaker 1: the biomechanics of pitching, Yes, of baseball, pitching and baseball. Yeah,
Speaker 1: and and then you get hired by the Dodgers.
Speaker 3: Yeah.
Speaker 2: Yeah, that was That was awesome because I originally didn't again,
Speaker 2: didn't realize that that could be a thing that could happen.
Speaker 1: Well, and it kind of wasn't right, Like, you're kind
Speaker 1: of just coming into this field as it's becoming a
Speaker 1: field where you can get a job where it's a.
Speaker 3: Field, right exactly. Yeah, there.
Speaker 2: I mean the challenge then was there wasn't a lot
Speaker 2: of options for actually even getting the data that you
Speaker 2: need to analyze.
Speaker 1: So ten years ago, like what is the state of
Speaker 1: play in this sort of nasson field that you're in
Speaker 1: helping to create?
Speaker 2: So the field is, I would say, is like sports biomechanic,
Speaker 2: and what that is is being able to analyze the
Speaker 2: movement of athletes for lots of purposes, help them reduce
Speaker 2: injury risk, help them improve performance.
Speaker 1: And to be clear, like folk, sports biomechanics has been
Speaker 1: around forever, right, that's what coaches do. They stay out
Speaker 1: there and they watch it. Yeah, And so it's kind
Speaker 1: of becoming it's becoming more technical, right, the field is
Speaker 1: becoming more technical.
Speaker 3: Yeah.
Speaker 2: So the state of the art relied on what is
Speaker 2: called marker based motion capture, which is where you literally
Speaker 2: put reflective markers like little balls, you stick them all
Speaker 2: over somebody's body. Usually the person has to like strip
Speaker 2: their clothes off because you want the markers like literally
Speaker 2: like on the skin, on the joints, and then you
Speaker 2: have these special cameras that track those markers.
Speaker 1: So you've got like a picture in his underwear with
Speaker 1: a bunch of little metal balls, takes them and they're like,
Speaker 1: just pitch like you always pitch.
Speaker 3: Right, And that's the challenge.
Speaker 2: That's why that wasn't That's why it wasn't very wide
Speaker 2: SPA is a thing people did because it was so
Speaker 2: hard to collect the data because ultimately, what you would
Speaker 2: need is you need the data on how someone is moving.
Speaker 3: You need to track.
Speaker 2: Where their elbow is, where their wrist is, where their
Speaker 2: knees are so that you can analyze it. And the
Speaker 2: state of the art for tracking it was an awful
Speaker 2: experience for the people you were trying to track.
Speaker 1: That presumably would mean they didn't pitch like they usually exactly,
Speaker 1: because they don't usually stand there in their underwear with
Speaker 1: met Were they really in their underwear by the way,
Speaker 1: I'm saying it because it sounds absurd, but is that
Speaker 1: actually what they were doing.
Speaker 2: That's actually you strip down to your your boxers, your
Speaker 2: boxer briefs, and that's it, that's all you're wearing.
Speaker 1: So it basically didn't work, and it basically wasn't very
Speaker 1: widely used as a result.
Speaker 2: Right exactly, Yeah, you yeah, you look at studies in
Speaker 2: that field, and people would be throwing like several miles
Speaker 2: an hour slower than they would be throwing when they
Speaker 2: weren't wearing all that stuff. So what changes computer vision?
Speaker 2: That was the big That was the big inflection point. Now,
Speaker 2: to be fair, like, even when I was finishing my PhD,
Speaker 2: and I'll give them, I'll give them a shout out,
Speaker 2: there was a company that was already like working really
Speaker 2: hard to solve this problem for baseball teams that I
Speaker 2: was getting that I got to be familiar with, called Kinnetrax.
Speaker 2: But yeah, the big inflection point was computer vision, basically
Speaker 2: using artificial intelligence to identify where those joints are in
Speaker 2: a camera image rather than needing to paste those reflective markers.
Speaker 1: So computer vision takes off. You're working at the Dodgers,
Speaker 1: and then eventually in twenty nineteen, right, you leave the
Speaker 1: Dodgers and you start your company a reboot motion, what
Speaker 1: does your company do?
Speaker 2: We do what we call biomechanics as a service, So
Speaker 2: we try to analyze this computer vision data at a
Speaker 2: very large scale to help teams and coaches make use
Speaker 2: of it to help athletes get better. Bas Yeah, that says.
Speaker 1: But bess Yah and and who are your customers?
Speaker 2: Our customers are Major League Baseball teams actually NBA teams,
Speaker 2: so we've gotten into basketball also sort of like league
Speaker 2: wide data providers. So yeah, leagues teams is basically our
Speaker 2: sweet spot.
Speaker 1: So let's talk. Let's talk in like a little more
Speaker 1: detail about about what you actually do, right, tell me
Speaker 1: the story of what you do.
Speaker 2: So, Evan, my co founder, Evan Demchik, he likes to
Speaker 2: call this the biomechanics Trainkay.
Speaker 1: Let's take a ride on the biomechanics trend.
Speaker 2: And we call it that because with the way our
Speaker 2: product works is we let people get on the train
Speaker 2: at whatever stop works for them, and get off the
Speaker 2: train at whatever stop works for them.
Speaker 1: How far are we going to go with this metaphor
Speaker 1: of a little o'b nervous about it?
Speaker 3: That might be as far as we go.
Speaker 1: Okay, good, good, So just tell it to me. Start
Speaker 1: at whatever seems like the beginning of a you know,
Speaker 1: a of an encounter, and so I'd like to understand
Speaker 1: how that works. That's really kind of a way to
Speaker 1: think about it. So let's start with the data, right,
Speaker 1: what is a basic what is the basic thing that's.
Speaker 2: Happening So the very first thing that happens is you
Speaker 2: record videos of the athlete doing the athletic motion. So
Speaker 2: we'll talk about pitching, So you record videos of a
Speaker 2: pitcher pitching. Our product, we actually have implemented our own
Speaker 2: computer vision models, so we can do that if people want.
Speaker 2: But generally speaking, people have systems like Kinetrax is one
Speaker 2: I've mentioned that, Hawkeye is another popular one where there
Speaker 2: is a system in place that has the cameras that
Speaker 2: record the videos and then runs those computer vision models.
Speaker 2: What those computer vision models do is they extract the
Speaker 2: locations in three dimensional space of all of like the
Speaker 2: joint centers. So where's my elbow, where's my wrist, where's
Speaker 2: my knee? In three dimensional space? That's what comes out
Speaker 2: of these computer vision systems.
Speaker 1: Okay, so pretty much everybody has that at this point,
Speaker 1: Like every professional baseball team has that for every pitch
Speaker 1: in every game at this point.
Speaker 3: Yes, exactly.
Speaker 2: Okay, So it's a ton a ton of data. So
Speaker 2: that's another challenge that we've solved, is not only how
Speaker 2: do you do this, but how do you do this
Speaker 2: at a very large scale?
Speaker 1: Okay, so this data, everybody's got it now, and you're not.
Speaker 1: You can sort of process the data, but that's not
Speaker 1: your special sauce. That's not your secret sauce, right, So
Speaker 1: typically they'll send you that that data of like, here's
Speaker 1: all the body points. Here is how they're moving in
Speaker 1: physical space. Then what do you do with it?
Speaker 2: So, yeah, this is where the special sauce comes in.
Speaker 2: So the first step to that is how do you
Speaker 2: turn those key points into a human skeleton. So you've
Speaker 2: got to figure out, like where do the bones connect,
Speaker 2: what sort of degrees of freedom do those bones have,
Speaker 2: So then you can figure out how do those key
Speaker 2: points animate a human skeleton.
Speaker 1: So you're sort of rebuilding it. It's like you start
Speaker 1: with the picture of a person and then you turn
Speaker 1: it into a bunch of data points, and now you've
Speaker 1: got to kind of build the person back up again from.
Speaker 2: The day exactly exactly. So now you have an actual
Speaker 2: human skeleton where the shoulder is rotating, the elbow is flexing,
Speaker 2: the knee is flexing, the hips are rotating. And now
Speaker 2: once you do that, now you can understand that data
Speaker 2: in the context of how the body works. Okay, so
Speaker 2: once we've done that, then we calculate how energy flows
Speaker 2: through the body, we calculate how momentum flows through the body,
Speaker 2: and once we've done that, we can analyze how efficiently
Speaker 2: the athlete is moving. Are they generating energy and momentum
Speaker 2: in the direction that they want to generate in? What
Speaker 2: is that desired direction? So we then we calculate all
Speaker 2: sorts of metrics around movement efficiency and direction. Then once
Speaker 2: we calculate all those metrics, now we can understand how
Speaker 2: those relate to what you're trying to do. Throw the
Speaker 2: ball as hard as possible.
Speaker 1: So so presumably with the picture, what you want to
Speaker 1: optimize for is is having as much of the picture's
Speaker 1: energy of their body go toward making the ball go
Speaker 1: toward home plate. Exactly right, I mean that is that? Yeah,
Speaker 1: they mail optimization problem mail it.
Speaker 2: Yeah, Yeah, that's exactly it. And that's the problem that
Speaker 2: we try to understand. So when we build our sort
Speaker 2: of models regarding like how does a pitcher create efficient
Speaker 2: fastball velocity, one of the most important things that comes
Speaker 2: out of those models is lining up the direction of
Speaker 2: your torsore rotation with the direction of your arm rotation.
Speaker 1: The pitching bution is crazy complex, right, Like they're they
Speaker 1: kick their leg up and they got their front arms
Speaker 1: doing something and their back arms going back like a
Speaker 1: lot is happening. Yeah, the sort of platonic ideal is
Speaker 1: every little millimeter of every motion is going toward maximizing
Speaker 1: the energy of the ball going toward home plate.
Speaker 2: Yes, and not just not just maximizing like in a vacuum,
Speaker 2: but doing it in the most efficient way, because if
Speaker 2: you just sort of maximize in a vacuum, maybe you're
Speaker 2: transferring that energy in a way that hurts your elbow.
Speaker 2: You're transferring that energy in a way that hurts your shoulder.
Speaker 2: So not only do we figure out how can a
Speaker 2: pitcher maximize it, but we try to figure out how
Speaker 2: they can have that energy and momentum go in a
Speaker 2: direction that doesn't hurt their joints, So try to have
Speaker 2: them throw the ball a little harder while also reducing
Speaker 2: their injury risk a little bit.
Speaker 1: So you're whatever doing the math at reboot HQ, and
Speaker 1: then what are you sending back to the.
Speaker 2: Team, So some teams so it sort of so again,
Speaker 2: all right, I told you that was the end of
Speaker 2: the biomechanics train analogy.
Speaker 3: I'm going to bring it back for a brief second.
Speaker 1: Okay, I'm ready.
Speaker 2: So we go all the way to building a report
Speaker 2: that has a bunch of suggestions. So there's a report
Speaker 2: that's like, this is how efficient you are. You can
Speaker 2: like tilt your torso a little bit to be a
Speaker 2: little bit more efficient. You can tilt your arm a
Speaker 2: little bit to be a little bit more efficient. So
Speaker 2: we go all the way to generating a report that's
Speaker 2: like the final stop on the train.
Speaker 1: And that's a report for for one pitcher.
Speaker 3: Yeah, that's a report on a pitcher for whatever.
Speaker 1: And and in a way that's like the nerdiest it's
Speaker 1: what a pitching coach does, but just in a way
Speaker 1: nerdier way.
Speaker 3: Yeah.
Speaker 2: Well, so we try to sort of like give the
Speaker 2: coaches superpowers, you know, even though that you know, the
Speaker 2: coaches like can look at an athlete and understand a
Speaker 2: lot about the athlete just by looking at them. We
Speaker 2: try to make a report that can really amplify what
Speaker 2: the coach is already doing, maybe help them discover some
Speaker 2: things they weren't thinking about, or measure some things that
Speaker 2: they were thinking about, but now they can track those
Speaker 2: a little bit easier. So that's you know, that's the
Speaker 2: ultimate thing that we produces a report that can sort
Speaker 2: of like we say, give a coach superpowers.
Speaker 1: Where was the trained metaphor doing that? Is there an
Speaker 1: earlier station of disembarkation exactly?
Speaker 2: So a lot of teams now are hiring people with
Speaker 2: biomechanics and analytics backgrounds, so rather than just use our
Speaker 2: reports out of the box, they want to build their
Speaker 2: own reports and their own statistical models and their own
Speaker 2: AI models. So we also get let people get off
Speaker 2: the train a little bit earlier and build whatever they
Speaker 2: want on top of the data that we're generating.
Speaker 1: Are they not going to disintermediate you once those people
Speaker 1: are there? Does that reduce the value you provide to
Speaker 1: the team.
Speaker 2: Now because we still have to process all that data.
Speaker 1: Do you have some like IP or like why can't
Speaker 1: somebody like you who works for a team just do
Speaker 1: that without you?
Speaker 2: That is a great question. We answered this question all
Speaker 2: the time. Is because it's a very complex engineering problem.
Speaker 2: Not only to do all the math that the physics
Speaker 2: based math, to calculate the energy, calculate the momentum, like
Speaker 2: all of that math is really hard, but also to
Speaker 2: do it at a very large scale. So someone like
Speaker 2: me in grad school learned how to do that on
Speaker 2: a sample size. You know, my PhD was actually really
Speaker 2: just one pitch, but lots of people do it on
Speaker 2: like ten pitches or maybe one hundred pitches, So we
Speaker 2: do it on several thousand pitches like and swings like
Speaker 2: every morning.
Speaker 3: You know.
Speaker 2: There's just every team has like seven affiliates, so there's
Speaker 2: you know, one hundred and fifty games every day that
Speaker 2: need to be.
Speaker 1: So you're doing this on farm teams as well.
Speaker 3: Yeah, exactly.
Speaker 2: So not only did we solve the problem of doing
Speaker 2: it for like one pitcher in a way that's really actionable,
Speaker 2: but we solved the problem doing it for every game
Speaker 2: every day, you know, so that you have the data
Speaker 2: when you wake up.
Speaker 1: So you guys are doing it at scale. The answer
Speaker 1: to why it is that that there is in fact
Speaker 1: an economy of scale, a benefit of scale.
Speaker 3: Yes, which you have, Yes, exactly, Yeah. Yeah.
Speaker 1: What's what's a specific example of a thing that a
Speaker 1: coach might tell a pitcher in response to your report
Speaker 1: to try and get them to throw whatever differently?
Speaker 3: A really.
Speaker 2: Common low hanging fruit type of piece of feedback that
Speaker 2: often comes out of the reports is how a pitcher
Speaker 2: is using their lead arm in a typical pitching motion,
Speaker 2: the pitcher will reach forward with their lead arm.
Speaker 1: So, just to be clear, the lead arm is the
Speaker 1: arm that is not holding the ball.
Speaker 3: Right right, yeah, the arm in front of you.
Speaker 1: Yeah.
Speaker 3: Yeah.
Speaker 2: A pitcher will reach forward with that lead arm while
Speaker 2: the rear arm is holding the baseball, and they'll rotate
Speaker 2: that lead arm really hard, and that's the thing that
Speaker 2: kind of initiates the torso rotation. So a very common
Speaker 2: flaw that we see is if a pitcher has a
Speaker 2: very vertical pitching arm, they're pitching the ball way over
Speaker 2: the top of their head, but their lead arm when
Speaker 2: they pull it through, when they swing it through, they
Speaker 2: swing it in a very flat.
Speaker 1: Plane as horizontal.
Speaker 2: Horizontal, right, yeah, That is not a very efficient plane
Speaker 2: to use when you're throwing the ball on a very
Speaker 2: vertical plane. So a very common low hang piece of
Speaker 2: fruit feedback that comes out of the reports is having
Speaker 2: pitchers just try to rotate their lead arm pull with
Speaker 2: their lead arm in a more vertical plane to better
Speaker 2: match what their torso is doing, a better match with
Speaker 2: their pitching arm is doing.
Speaker 1: That's a good one. I feel like that one's so
Speaker 1: simple that you don't want it to get out that
Speaker 1: everybody ell to start looking at it.
Speaker 2: No, I mean really, I mean like it's this has
Speaker 2: happened when I went to talk to a team and
Speaker 2: we talked about some pieces, you know, some low hanging fruit,
Speaker 2: and they're like, okay, great, we'll take the lead arm thing,
Speaker 2: will implement it everywhere. And I'm like, well, what about
Speaker 2: like the other ten pages of the report.
Speaker 3: Good with the lead arm thing.
Speaker 1: Thanks by so for you. The end of the train
Speaker 1: is the report. But that report goes to the coach, right,
Speaker 1: and so presumably the meaningful change hasn't happened yet, right,
Speaker 1: It has to somehow get from the coach to the picture.
Speaker 1: And like, I know that piece of it is not
Speaker 1: your business now, but it was kind of your business
Speaker 1: when you were at the Dodgers. Presumably you're familiar with
Speaker 1: it now, Like, how does that piece of it work?
Speaker 1: Is it like the pitching coach is like reading from
Speaker 1: the report to the picture. I imagine not, but I
Speaker 1: don't know.
Speaker 2: No, definitely, definite, definitely not. Even at the Dodgers, my
Speaker 2: role was not being the one to coach the players.
Speaker 1: It's like, whatever you do, don't talk to the pictures. Man,
Speaker 1: go back to your computer.
Speaker 2: Yeah no, no, I mean thankfully I got to be
Speaker 2: in the in the room as you know, dark Walld,
Speaker 2: the interaction is happening. But I think that is the
Speaker 2: the art of coaching that is so important is understanding
Speaker 2: the picture and how the pitcher thinks about themselves and
Speaker 2: giving the right feedback to have the picture do the
Speaker 2: thing that you that you want them to do.
Speaker 1: Uh huh. Knowing how to talk to a player in
Speaker 1: a way that is not generic. Presumably different pitchers need
Speaker 1: to hear different things, even if the outcome is the same, right.
Speaker 2: I mean, there's a there's a classic like debate in
Speaker 2: baseball of like do you swing down on the ball
Speaker 2: or do you swing with an uppercut? And in reality,
Speaker 2: like the batpath is an arc, the path goes down
Speaker 2: and then the path goes up. But some coach, some
Speaker 2: players respond better when a coach will say swing down
Speaker 2: on the ball, and some players spawn better when a
Speaker 2: coach will say, you know, have get a little bit
Speaker 2: more uppercut, do your swing. But in reality, you know,
Speaker 2: the bat goes down and the bat goes up. So
Speaker 2: it's like really understanding what is the player what helps
Speaker 2: the player the most?
Speaker 1: So I heard you use this phrase in another interview
Speaker 1: that I think is kind of what you're talking about here,
Speaker 1: And it's feel versus real? What is that? What is
Speaker 1: feel versus real?
Speaker 2: So it's exactly what we're talking about. Is sometimes with
Speaker 2: the apt let feels like they're doing is not.
Speaker 3: What's actually happening.
Speaker 2: But if you understand what the athlete feels like they're doing,
Speaker 2: you can give feedback that interacts with how they're feeling.
Speaker 2: So if they feel like they're swinging down on the
Speaker 2: ball and you give them feedback related to that, even
Speaker 2: if they're actually swinging with an uppercut, you know, the
Speaker 2: important thing is like how do they feel and how
Speaker 2: do you give them feedback related to how they feel,
Speaker 2: which then impacts is real. But it's understanding the interplay
Speaker 2: between the feel and the real.
Speaker 1: Yeah, it's wild that, like there is there is a
Speaker 1: human being doing a thing, and then there's this huge
Speaker 1: industrial machinery of your company and the team and all
Speaker 1: of these scientists and all these cameras and computers that
Speaker 1: are trying to get this human being to change their
Speaker 1: behavior in a very subtle way.
Speaker 3: Yeah, exactly.
Speaker 1: It's a behavior that is in many ways intuitive right,
Speaker 1: it's sort of partly conscious but partly intuitive him like
Speaker 1: there's a really interesting human being at the center.
Speaker 3: Of all of this.
Speaker 2: Yeah, And I think that's also why it's so important
Speaker 2: to have the coach in the loop, because they understand
Speaker 2: the human being even beyond just like the feel versus
Speaker 2: real aspect of like did the athlete not get a
Speaker 2: good night's sleep last night, then maybe today is not
Speaker 2: a good day to give them feedback. Are they going
Speaker 2: through challenges, you know, with a significant other, are they
Speaker 2: going through challenges in other ways? Or today are they
Speaker 2: really fired up? So today is a really good day
Speaker 2: to give them feedback. It's like understanding the human being.
Speaker 2: I'll give him a shout out. Connor McGuinness is the
Speaker 2: assistant pitching coach right now for the Dodgers, and he
Speaker 2: was so good at this. He would always talk about
Speaker 2: he never felt comfortable truly coaching a player until the
Speaker 2: player trusted him, until he felt like he had a
Speaker 2: good enough relationship with the player with the player would
Speaker 2: feel comfortable taking his feedback.
Speaker 1: Like start with the player as a human beings, Start
Speaker 1: with the player and get to get to.
Speaker 2: And this is why I think it's going to be
Speaker 2: really really difficult to have a product that goes direct
Speaker 2: to a player. We've dabbled with that, you know, and
Speaker 2: lots of companies have dabbled with film yourself with your
Speaker 2: iPhone and you get some feedback, you know, on your movement.
Speaker 2: But I think there's so much subjective stuff that goes
Speaker 2: into what we're talking about, how does the athlete move
Speaker 2: and how do they feel like they're moving, that I
Speaker 2: think it's going to be really hard to solve the
Speaker 2: challenge of giving an athlete feedback without a coach.
Speaker 1: In a minute, Jimmy's work in the NBA and why
Speaker 1: figuring out how to help NBA players shoot better is
Speaker 1: actually a really hard problem. What are you doing in basketball?
Speaker 2: Basketball is very very cool because it's a slightly different
Speaker 2: channe In baseball, like we've been talking about, the challenge
Speaker 2: for a pitcher is mostly just maximizing efficiency, or even
Speaker 2: for a hitter, you know, they're reacting to the pitcher,
Speaker 2: but they're still trying to swing as hard as they
Speaker 2: can and hit the ball as far as they can.
Speaker 2: So basketball is very different because you got a target.
Speaker 2: You're not trying to do this thing as hard as
Speaker 2: humanly possible.
Speaker 1: The target is the hoop. The targets to be clear, the.
Speaker 3: Target is the hoop.
Speaker 2: So that's a really interesting kind of like motor control
Speaker 2: problem of no matter where you are on the court,
Speaker 2: how good are you at getting the ball to do
Speaker 2: what you want it to do. We've been finding that
Speaker 2: there seems to be lots of trade offs that good
Speaker 2: shooters are making regarding like when do they release the
Speaker 2: ball in the course of their jump, how high of
Speaker 2: an arc do they use when they release the ball,
Speaker 2: how high they have their release point when they.
Speaker 3: Release the ball.
Speaker 2: So there's all sorts of trade offs that these shooters
Speaker 2: seem to be making related to how good they are
Speaker 2: at controlling their own jump, controlling their own velocity, where
Speaker 2: they are on the court, how close the nearest defender is.
Speaker 2: It's it's a very different problem.
Speaker 1: Sounds way harder, sounds way way harder, is that right
Speaker 1: for you? Harder for you to sort of solve to
Speaker 1: write a report that says, do this different thing and
Speaker 1: you'll hit a higher percentage of your shots.
Speaker 2: Right exactly? Yeah, And which makes it which makes it fun.
Speaker 2: I love solving hard podcasts.
Speaker 1: What have you solved in basketball so far?
Speaker 2: The big the first challenge that we had to solve
Speaker 2: was the scale challenge in basketball. There isn't quite as
Speaker 2: much there aren't quite as many games in basketball, but
Speaker 2: the data is a lot harder to parse because the
Speaker 2: events aren't as distinct. It's not like it's not like
Speaker 2: this is a shot, I mean only for free throws.
Speaker 1: It's a more continuous.
Speaker 2: Continuous motion. So the first big challenge was how do
Speaker 2: we even just isolate the events that we care about.
Speaker 1: Like what is time? When does a shot begin? When
Speaker 1: does a time equals zero for shot?
Speaker 2: But kind of debatable, right exactly. So that, yeah, that
Speaker 2: was the first big challenge that we had to solve.
Speaker 2: Was just sort of like the data engineering challenge. And now,
Speaker 2: like I said, our reports are more than telling you,
Speaker 2: you know, how to be more or less efficient. It's
Speaker 2: trying to surface the trade offs that you're making. Where
Speaker 2: in your jump are you are you releasing the ball,
Speaker 2: how high is your release point? What kind of arc
Speaker 2: are you using? And how does that compare to other
Speaker 2: arcs you could be using?
Speaker 1: What like what what are the trade offs?
Speaker 2: A really really interesting trade off to me is how
Speaker 2: much arc are you putting on the basketball? Not just
Speaker 2: to like evade a defender. But there's a trade off
Speaker 2: where if you shoot the ball at a highhigher arc,
Speaker 2: you have to use more velocity to get the ball
Speaker 2: to go all the way to the rim.
Speaker 1: Right, because it's going to travel farther in total in space.
Speaker 2: Right, And if you are not the most coordinated human,
Speaker 2: it might be harder for you to add more velocity
Speaker 2: in a really in a really precise way. So the
Speaker 2: more arc you have, the more prone you can be
Speaker 2: to what we would call like velocity errors overshooting undershooting.
Speaker 2: The advantage you get when you create more arc is
Speaker 2: if you if you imagine the ball coming down from
Speaker 2: that arc and the angle the angle with which it
Speaker 2: approaches the rim, it approaches at a steeper angle, which
Speaker 2: means you literally have more rim to aim at.
Speaker 3: So if you're hot, so if you shoot.
Speaker 2: Higher, you know this is a steph Curry thing. He
Speaker 2: has a really high arc. Presumably this is the hypothesis,
Speaker 2: because he's one of the most coordinated humans on the.
Speaker 1: Planet, so he sea.
Speaker 2: So he can use a really high arc because he's
Speaker 2: really good at controlling his velocity output. Oh huh, so
Speaker 2: he can really dial in how hard he releases the ball,
Speaker 2: which means he gets the advantage of having more rimmed
Speaker 2: to aim at, where as somebody who's bad at controlling
Speaker 2: their velocity output when they try to aim higher, they'll
Speaker 2: just overshoot an undershoot.
Speaker 1: So is the notion, and this is something of an oversimplification,
Speaker 1: but that for any given level of coordination, there is
Speaker 1: some optimal arc, and the more coordinated you are, the
Speaker 1: higher the optimal arc would be for you setting aside defense.
Speaker 3: Maybe that's the hypothesis.
Speaker 1: Yeah, I mean that seems like where what you were
Speaker 1: saying goes exactly.
Speaker 2: Yeah, that's the hypothesis. So that's what we're trying to
Speaker 2: look into.
Speaker 1: Okay, I'll be curious to see what you figure out.
Speaker 1: Are free throws easier? Did you think of starting with
Speaker 1: free throws?
Speaker 3: Yeah?
Speaker 2: Right, right now. Honestly, the phase we're at in basketball
Speaker 2: is mostly just collecting a lot of data. So you know,
Speaker 2: a starting pitcher will you know, throw ninety pitches in
Speaker 2: a game, and now you have a sample size of
Speaker 2: ninety pitches that are all the same, whereas a basketball
Speaker 2: shooter only has a couple of free throws in a game.
Speaker 2: You know, we're working on ways with teams of collecting
Speaker 2: data in a practice setting, so getting a lot of
Speaker 2: this data in a bigger chunks. But really at this
Speaker 2: point it's collecting a lot of data so we can
Speaker 2: do some of this research to explore some of these hypotheses.
Speaker 1: Huh. So it seems like in basketball you're where you
Speaker 1: were ten years ago or something in.
Speaker 3: Baseball, right exactly.
Speaker 1: So basketball is sort of one kind of frontier. It
Speaker 1: seems like one thing you're trying to figure out and
Speaker 1: haven't really cracked yet. What are some of the other
Speaker 1: frontiers the other things you're figuring out, whether it's in
Speaker 1: baseball or in the fundamental technology or whatever. What are
Speaker 1: you working on?
Speaker 2: We want to try to have a computer vision be
Speaker 2: even more accessible. You know, there's been a lot and
Speaker 2: a lot, a lot a lot of improvements over the
Speaker 2: last ten years in computer vision where you can do
Speaker 2: really good motion capture with just your iPhone, but still
Speaker 2: for certain specialized movements, pitching, shooting, things like that, there's
Speaker 2: still have little ways to go to get really really
Speaker 2: good data straight from your iPhone. So that's one of
Speaker 2: the frontiers, is like continuing to try to help understand
Speaker 2: how to make computer vision more accessible. Another one is
Speaker 2: more fitness based analysis. You know, it's interesting to think
Speaker 2: about companies like Mirror and companies that have tried to
Speaker 2: give people feedback and like a fitness environment, But how
Speaker 2: do you give someone or how do you give like
Speaker 2: a strength and conditioning coach good feedback that can be
Speaker 2: used in a weight room setting. Yeah, and then continue
Speaker 2: to explore other emotions and other sports, Like football is
Speaker 2: a really interesting one because a lot of football is
Speaker 2: interacting with other humans. Indeed, yeah, I mean that's obvious,
Speaker 2: but like, how do you get a takeaway from like
Speaker 2: two linemen interacting?
Speaker 1: Ye, things, but that one I wonder. I mean, it
Speaker 1: seems like if you think of the line, you know,
Speaker 1: in football, it's so they're so on top of each other,
Speaker 1: the defensive line, of the defensive line, that I feel
Speaker 1: like vision might not be what you want. You might
Speaker 1: want sensors, right, you might want pressure sensors in the
Speaker 1: lineman's clothes or something. I don't know, I'm just making
Speaker 1: that up, but like it seems like that might be
Speaker 1: more useful, just because it's hard to see what's going
Speaker 1: on in the line.
Speaker 3: Yeah.
Speaker 2: Yeah, yeah, except professional athletes don't like wearing random.
Speaker 1: Things, well are aren't people trying to make sensors like
Speaker 1: woven into the clothes. I mean, I feel like there
Speaker 1: are ways you would just be putting on your jersey
Speaker 1: or putting on your paths or whatever and they would
Speaker 1: have the sensors built in.
Speaker 3: Yeah. Yeah, one hundred percent.
Speaker 2: There's lots of really cool technology of that is you know,
Speaker 2: you know, microscopic sensors that are just woven, woven into
Speaker 2: clothing for sure.
Speaker 1: So so let's talk for a second more about the
Speaker 1: consumer side. You sort of touched on it and moved on.
Speaker 1: I mean, is that are you actively working on that
Speaker 1: or is that just like yeah, it kind of seems interesting,
Speaker 1: but not for now. We're too busy, like what's what's
Speaker 1: happening on the consumer side.
Speaker 2: We're not actively tackling in the consumer side right now.
Speaker 2: And that the reason why we started with professional sports
Speaker 2: is one it's because like it's what I know, you know,
Speaker 2: I work for the Dodgers, but also because the value
Speaker 2: proposition of what we're doing is so impactful. You know, yeah,
Speaker 2: you know, you understand, you try to understand, like the
Speaker 2: relationship between people have tried to put numbers on this,
Speaker 2: it's the people have estimated, it's in the millions of dollars.
Speaker 2: But the value of adding one mile an hour of
Speaker 2: fastball velocity to a picture, you know, people have valued that,
Speaker 2: We have valued that in millions of dollars.
Speaker 1: Well, sure you will tell you millions of dollars, But
Speaker 1: what is the like, I don't know what's a what's
Speaker 1: a what's a top picture make? These days? I don't
Speaker 1: even know anymore.
Speaker 3: I mean, you want to talk about show.
Speaker 1: Half a billion, hundreds of millions, half a billion? Yeah,
Speaker 1: so so right, so so the marginal benefit has a
Speaker 1: very large value. Yah a million dollars against one hundred
Speaker 1: million dollars, like one percent better. If they're making a
Speaker 1: hundred million dollars, it's worth a million dollars presumably.
Speaker 2: Yeah, And we start to get the pro sports teams
Speaker 2: to believe.
Speaker 1: That, well, how many how many pro sports teams are
Speaker 1: paying you at this point, about uh.
Speaker 2: Close to ten in Major League Baseball and a couple
Speaker 2: in the NBA.
Speaker 3: The NBA is a lot newer.
Speaker 1: And how big is the field? Like, what's sort of
Speaker 1: the state of play in the field of I don't
Speaker 1: even know what to say. Biomechanics as a service seems
Speaker 1: like a niche construction of it. But what would you
Speaker 1: say sports analytics? I mean, I guess that's not quite right.
Speaker 1: What how do you construct the broader field?
Speaker 2: Sports analytics is definitely a part of it. The data
Speaker 2: that we provide is novel or is different than like
Speaker 2: traditional sports analytics. So we don't really have a lot
Speaker 2: of companies as competitors. Honestly, our biggest competitors are teams
Speaker 2: wanting to try to do this type of thing internally.
Speaker 2: How you know, hire a bunch of data engineers, hire
Speaker 2: a bunch of software engineers, hire people with biomechanics backgrounds,
Speaker 2: and try to build out these pipeline processing pipelines themselves.
Speaker 1: Does every team have like a somebody with a PhD
Speaker 1: in biomechanics working for them now?
Speaker 3: In baseball? Yeah?
Speaker 1: Wow?
Speaker 2: In basketball and not Yeah, but they're starting to.
Speaker 1: So I know people, I mean, I think in general
Speaker 1: baseball fans like to complain, but so one of the
Speaker 1: things they've complained about lately is the way analytics more
Speaker 1: broadly made the game more boring, right, like the shift
Speaker 1: and changing pictures more frequently and whatever else people complain about.
Speaker 1: Are you do you fit into that at all? Uh?
Speaker 3: That's a good that's a good question. Some people.
Speaker 2: Some people complain about how hard pictures are throwing these
Speaker 2: days because it creates more strikeouts, and people think strikeouts
Speaker 2: are boring.
Speaker 3: So maybe, yeah, so.
Speaker 1: Maybe you need to get better at helping hitters. Really
Speaker 1: you're out that way, you can even at back up, all.
Speaker 2: Right, And that's what we talked about takeaways four Hitters
Speaker 2: are harder because they're reacting to the picture.
Speaker 1: So if you think about the field, what your company
Speaker 1: say in five years, yeah, whatever is your kind of
Speaker 1: medium term future that you think about, what is the
Speaker 1: company in the sort of the world, the sports world
Speaker 1: that you're interacting with look like at that time and
Speaker 1: say whatever, five years, ten years.
Speaker 2: What I hope is that what we are trying to
Speaker 2: foster is an environment where coaches have really incredible tools
Speaker 2: at their disposal to understand how an athlete moves. So,
Speaker 2: you know, we talk about the very beginning where sort
Speaker 2: of the still more or less the state of the
Speaker 2: art is a coach just looks at an athlete, watches
Speaker 2: video of an athlete, and tries to give the athlete
Speaker 2: feedback regarding what they see on the video, what they
Speaker 2: see with their eyes. We hope that the standard becomes
Speaker 2: you use an analytical tool to help you understand how
Speaker 2: the athlete is moving and to really like level up
Speaker 2: your coaching because now you have this objective information about
Speaker 2: how the athlete is moving. I kind of I kind
Speaker 2: of make the analogy related to just radar guns. Before
Speaker 2: radar guns were a thing, a coach would just like
Speaker 2: look at a picture and be like, oh, that looks
Speaker 2: pretty fast, you know, make an adjustment, and I think
Speaker 2: that looks a little faster. But then radar guns came out,
Speaker 2: and you could actually measure how fast the picture was throwing,
Speaker 2: and you could actually measure if the picture is getting
Speaker 2: throwing the ball harder based on your feedback.
Speaker 1: Yeah, and now you're you're doing that but in a
Speaker 1: in a way more complex way.
Speaker 3: Exactly.
Speaker 1: Yeah, we'll be back in a minute with the lightning round.
Speaker 1: Let's do the lightning rounds. Let's start with a few
Speaker 1: baseball questions. Who's the most underrated picture of all time?
Speaker 2: Whoa underrated picture of all time? Oh my goodness, I
Speaker 2: don't know if I can give you one that's like
Speaker 2: all time, because I don't know if that's fair. I'm
Speaker 2: sure I'm not thinking of everybody of.
Speaker 1: Your lifetime, of your lifetime.
Speaker 3: I grew up a hardcore Red Sox fan.
Speaker 2: I grew up in Rhode Island, and the first one
Speaker 2: that comes to mind mostly in the Red Sox atmosphere,
Speaker 2: but I wonder if you could make a broader argument
Speaker 2: was Tim Wakefield, who was a knuckleball pitcher for the
Speaker 2: Red Sox and what made amazing physics amazing physics and aerodynamics,
Speaker 2: And the reason being is he just did so many
Speaker 2: things for the Red Sox, ate up so many innings
Speaker 2: and was so effective closing, starting, whatever, But he never
Speaker 2: got incredible recognition because it was a knuckleball that was going,
Speaker 2: you know, fifty five sixty miles an hour.
Speaker 1: What's one thing you would change about baseball to make
Speaker 1: it more popular?
Speaker 2: I do feel like actually the changes that are being
Speaker 2: made are good ones, in like reducing the amount of downtime.
Speaker 1: A pitch clock, it's one, is that the way you're
Speaker 1: thinking of?
Speaker 2: Or yeah, yeah, yeah, exactly, yeah, a pitch clock. The
Speaker 2: thing that's challenging for me as a biomechanist is when
Speaker 2: you reduced the amount of time that a pitcher has
Speaker 2: to throw, you theoretically could introduce more fatigue, which theoretically
Speaker 2: also introduces more injury risk. So this is something that
Speaker 2: we've been thinking about. Is like, So for me, while
Speaker 2: I like that change that baseball is making to make
Speaker 2: the game, to speed the game up, I think the
Speaker 2: pitchers also need to train a little bit differently. Oh,
Speaker 2: that's to be able to better withstand the shorter rest time.
Speaker 1: Are there aspects of your work and the changes you've
Speaker 1: seen over the course of your career that illuminate sort
Speaker 1: of broader changes in computer vision and AI more generally.
Speaker 2: Yeah, In particular, the most important one has been the
Speaker 2: improvement in computer vision, which because computer vision at its
Speaker 2: core is artificial and intelligence neural networks, and as those
Speaker 2: as that that technology has gotten better and better, you know,
Speaker 2: the latest and greatest people always talk about like the
Speaker 2: Transformer model really changed AI. I mean that changed computer
Speaker 2: vision too, Like lots of the modern more modern like
Speaker 2: computer vision models are based on transformers.
Speaker 1: And which, to be clear to the Transformer model is
Speaker 1: what gives us chat GPT, right, The T in GPT
Speaker 1: is transformed, right, So how has it affected the computer vision.
Speaker 2: Side making the models more accurate and more efficient? It
Speaker 2: used to be you know, a couple of years ago
Speaker 2: when I try to run a computer vision model on
Speaker 2: my laptop, like it could take an hour just to
Speaker 2: like analyze one pitch, one video, and now it takes
Speaker 2: a matter of seconds.
Speaker 1: And so what is the what is the bigger implication
Speaker 1: of that? Beyond your beyond your work?
Speaker 2: More and more data is available regarding how people move,
Speaker 2: you know, like when when I first started, it was
Speaker 2: really hard to have data in a baseball game. Now
Speaker 2: every major league game, every minor league game, every NBA game,
Speaker 2: maybe every G League game, every w NBA game, you know,
Speaker 2: every single uh basketball and baseball game, you know, more
Speaker 2: or less is now like being recorded with computer vision
Speaker 2: to get the three dimensional data about how people are moving.
Speaker 2: Just lots and lots of data on how people move.
Speaker 2: And this is really impacting. I think lots of fields.
Speaker 2: In particular, I think like self driving cars, robots that
Speaker 2: are meant to like interact with the world, all rely
Speaker 2: on computer vision models. I mean, I think one of
Speaker 2: the coolest things about how cars do this sort of
Speaker 2: thing is not only do they have to understand where
Speaker 2: a person is now, but they're really cool models that
Speaker 2: they take where a person is now and the last
Speaker 2: like ten seconds of what that person did and try
Speaker 2: to predict all the different things that the person might do.
Speaker 2: They might run across the street, they might jump out
Speaker 2: of the way, they might jump forward, you know, they
Speaker 2: might run and chase a soccer ball.
Speaker 1: I mean, my sense is all of the hard edge
Speaker 1: cases in self driving cars. Basically, the reason we don't
Speaker 1: truly have self driving cars yet is because people are
Speaker 1: so hard to understate. Yeah, right, Like if the world
Speaker 1: was all self driving cars, then it would be a
Speaker 1: solved problem, right, Like the machine could understand what other
Speaker 1: machines are going to do. But people, human drivers, human
Speaker 1: pedestrians are strange and very hard for machines to understand.
Speaker 3: Yeah.
Speaker 1: Yeah, it's like they need a coach. It's like the
Speaker 1: coaching problem again. We found your SoundCloud. Oh yeah, so,
Speaker 1: Jimmy Buffett fans are called parrot heads. What are Jimmy
Speaker 1: Buffy fans called?
Speaker 3: I never thought about it.
Speaker 1: You have a song called Let's have some Fruit parentheses
Speaker 1: the fruit song is fruit a metaphor.
Speaker 3: Leave it up to your imagination. Fair.
Speaker 1: Jimmy Buffy is the co founder and CEO of Reboot
Speaker 1: Motion Swanson swe Today's show was produced by Gabriel Hunter Cheng.
Speaker 1: It was edited by Lyddy Jean Kott and engineered by
Speaker 1: Sarah Bruguer. You can email us at problem at Pushkin
Speaker 1: dot Fm. I'm Jacob Goldstein and we'll be back next
Speaker 1: week with another episode of What's Your Problems