Effectively Wild: A FanGraphs Baseball Podcast - Effectively Wild Episode 1475: Multisport Sabermetrics Exchange (Football and Basketball)

Episode Date: December 26, 2019

In the first installment of a special, seven-episode series on the past, present, and future of advanced analysis in non-baseball sports, Ben Lindbergh talks to ESPN’s Bill Barnwell about football (the American kind) and then ESPN’s Kevin Pelton about basketball (48:43), touching on the origins of sabermetrics-style analysis in each sport, the major challenges, big […]

Distract Me Winter Games The leaves are all along the rock wall You, you're cracking jokes And burning wood from the spot Hello and welcome to episode 1475 of Effectively Wild, a baseball podcast for the Fangraphs presented by our Patreon supporters. I am Ben Lindberg of The Ringer, flying solo as host today with the help of a couple guests. I just called this a baseball podcast, but for the next 10 days or so, it's going to be a podcast about other sports, too. And something a little new and different and hopefully exciting for us. We've been at this since the summer of 2012, so we don't want to get stuck in a rut trying to push the boundaries of what this podcast can be.

This is the first episode of a seven-episode series that I'm calling the Multisport Sabermetrics Exchange. This is an idea that Jeff Sullivan and I had last baseball offseason, but between my book and his job interviews, we never got around to it. I am rectifying that now. So the goal here is to provide a primer on the past, present, and future of advanced analysis in each sport. And what we hope to do is bring together some of the leading formative analysts from a variety of non-baseball sports and kind of compare notes.

Because on Effectively Wild, we tend to talk about baseball through an analytical lens, not exclusively, but often. We like to consider the sabermetric perspective. And of course, just about every sport these days has an analytical movement, often not as developed as baseball's. Because baseball is so well-suited to statistical analysis and has such a long history of it, and of course was the sport that spawned Moneyball. But even if sabermetric style analysis is not nearly as pervasive in every other sport, the analytical movements and many other athletic endeavors are proceeding along similar paths, often retracing some of the same steps, causing the same conflicts, and we thought this would be a fun way for fans of

these various sports to learn from each other and see what their sabermetric movements have in common, see what's different, where our paths have been parallel and where they've branched off in different directions. If you've ever wanted to know, well, is there a wins above replacement in this sport? Does that sport have win expectancy? How well quantified is it compared to baseball? Then this series will answer your questions and allow you to sound smart about all those other sports. So we're starting today with football and basketball.

If you click on the show page at Fangraphs, you can see all the sports that are slated for the rest of the series. Some more popular in the U.S., some more popular overseas, some team sports, some individual sports. We're not going to touch on every sport, but we'll get to quite a few of them. And if this is a success, maybe it's something we can continue as a holiday tradition. So if you're only in this for the baseball, we're running this during the holiday weeks when there's not much baseball to talk about. And many of you may have less listening time. Many podcasts take these weeks off entirely, but we will be giving you even more content than usual. And if you don't get to it until you return to work, that is fine because this will be evergreen.

So if you want to check out and return when we're back to business as usual, that's fine, but I'm hoping this will be a fun experiment for me too, because I am far from an expert about non-baseball sports, so I'm looking forward to learning along with you. We've brought together some of the analysts who've been involved in their sports sabermetric movements from the start or close to the start. Where we can, we've talked to people who have some knowledge of baseball too, so that we can compare and contrast. So we'll be touching on how well each sport is structured for advanced analysis, what some of the big breakthroughs have been, the major milestones, the overturned misconceptions, the state-of-the-art stats and technology, how thoroughly these insights have been accepted within the game, the effects that they've had on the spectator

experience. And at the end of this series, which we'll run through next week, we'll all be better educated about all sorts of sports. We'll be able to make comparisons and apply some of these breakthroughs to baseball. For scheduling reasons and holiday-related reasons, this will just be me steering the ship here, joined by different guests on each episode. And our regular co-hosts, Meg and Sam, will be back soon enough. So let's get started. And we'll lead off this series with a sport a lot of you like.

It's called football, the American kind. And to talk about its advanced stats, I am joined by a friend of the show and former colleague, Bill Barnwell. Bill is a writer for ESPN and the host, unsurprisingly, of The Bill Barnwell Show. He's also one of only two people on the ESPN press room site who has a hoodie in his headshot. It's just you and Stanford Steve Coughlin, although I'm sure if Sam had a headshot on there, he'd have a hoodie too. Hi, Bill. Hi, Ben. I love that you're representing the actual attire of a sports writer. And then I went to the site and it turns out a lot of people work for ESPN and a lot of them have headshots. So I was scrolling for a really long time, but it's just you and Stanford Steve. Stanford Steve and I worked together on the Scott Van Pelt show. So

there's a bit of a hoodie click at ESPN. No one else has joined us yet, but so far it's us two. It's a small click. So football. I'm going to start this series by, I think, framing the discussion with this question, which is where would you place football on the spectrum of ease of analysis? So if a 10 is baseball, that's the high end, and then a one on the low end is a sport that is just impenetrable to advanced statistical analysis. We can't figure out what's going on. Where would you put football and that spectrum? That's a good question. I would probably say somewhere between a three and a four. I think we're getting a lot better because we have player tracking data now, which we didn't have as recently as several years ago. But I mean, the example I would give is this. I mean, when you watch football on television,

not you specifically, but when your regular sport folk watches football on television. Sure. I don't do it that often. You don't, which is totally fine. But if you watch baseball, you're going to be able to see pretty much instantly the position of the vast majority of fielders when a ball is hit into play. In basketball, you will see just about everybody on the court at any given time, unless someone has fallen asleep or someone is just hanging on their side of the basket. Pretty much, you're going to see everything that's happening. In football, you're not going to be able to see the safeties when you're watching from the TV

angle. And if you're a quarterback, one of the things you do to figure out what the coverage is, is you look at the safeties, because the safeties have to determine what the run fits are going to be. They're going to determine where you're going to want to throw the football, where you're going to decide even before the snap where you're going to look to start throwing the football. So the fact that you can't see that makes it exceedingly difficult to get a sense of what is happening in terms of the strategy from play to play. And then on top of that, then what the takeaway should be quantitatively from that play. So I think we're still at such a point where gathering data is difficult. Having a big enough

sample to really do studies is difficult. It's much better than where it was 15 years ago and better than it was 10 years ago. And if the player tracking data continues to blossom, much better than where we were even five years ago. But I still think it's somewhere in the three to four range because the variety of interactions and the quantity of interactions in a sport where it's 11 on 11 is so dramatic versus any other sport that I really cover. I guess soccer is 11 on 11, but it's more compressed than soccer in most cases. Yeah. So you've been in this world for a while. Before Grantland, you were at Football Outsiders for years. And before that, you were at IGN, a hotbed of advanced football analysis. But you've seen all this grow and change. So can you give me a brief history of advanced football

analysis, when it started, how it really kind of caught on and some of the changes and the availability of data and some of the major breakthroughs along the way? Because I know that like the hidden game of football came out just a few years after the hidden game of baseball, but the sport as a whole and its sabermetric community seems to have lagged a bit behind. Yeah, I think absolutely. I mean, the hidden game of football was where I was actually going to start in terms of just, you know, it wasn't the same thing. There wasn't, you know, a linear weights model being built in the hidden game of football, but it was a sense of let's take these football questions and with the computer data we have at the time, the computer power to analyze data at the time, which was extremely limited in terms of the data that was available, let's answer some basic football questions. And then that was published. And then it sort of disappeared into the ether for about 17, 18 years or so. And then about 2003, in the, I guess, the glow, the post-glow of Moneyball, Aaron Schatz launched Football Outsiders. I think it was sort of in 2003, if I'm not mistaken. And sort of a similar

idea to BP in terms of just, let's try and answer some questions. Of course, BP was much further along very quickly, I think, than Football Outsiders was in terms of having data and then having the ability to answer questions with that data. But they were publishing a book, the Pro Football Prospectus through a partnership with BP. I actually got, I think I've told you this before, I don't know if I mentioned it on the air, but the initial way I caught on with Football Outsiders was through Baseball Primer, of all places. The baseball, not news group, but I guess the baseball discussion forum website, where Aaron was looking for a member for his fantasy baseball league. And I decided I'd love to join a 25-man NL-only baseball league with

people I don't know in the suburbs of Massachusetts as a very bored college student at Northeastern University. So why not go and join this league? And it ended up launching my career in some strange way. But Outsiders really was sort of the, I think for several years, the primary think tank for answering questions and building sort of the first real modern data analysis of the game. In 2009, I want to say maybe a little earlier, maybe 2008, Pro Football Focus came into shape. And I think for baseball fans and listeners to the show, probably the closest equivalent would be something like Project Scoresheet, where it is just, let's track who's on the field every single play, which was not available previously with any sort of public availability. Let's track who's on the field every single play, which was not available previously with any sort of public availability.

Let's track what we think they're doing. Let's track the basic events on the play of, you know, was there play action? Who, you know, who made a hit? Who was in coverage? And a lot of that is guesswork. But having that data, there had been some prior game tracking attempts. Football Outsiders did one as well. But PFF was sort of the first broad-scale analysis and broad-scale play-to-play, every single snap tracking attempt that was made.

And they've launched that into a whole – they certainly do more analysis now. Chris Collinsworth, the notable NFL commentator, former NFL wide receiver, purchased them. And they certainly put a lot of work into understanding the game. And they've done more harder quantitative analysis in recent years. I think they're doing a lot better work in terms of their research, but I think certainly they've gathered a ton of data. started to embed trackers in players' shoulder pads, which players can go years without changing their shoulder pads, which seems very strange to me and very sweaty to me. But they started to embed the trackers in the shoulder pads to get a sense of-

Is there machine washable trackers? I hope so. I really hope so. But so then once they started tracking those, we started to have a sense of where every player was in every play, that sense of, well, who was even on the field is automated. And over time, that's improved. Now it's to the point where, as someone who has access to what's now called the NFL's next-gen stats, within 10 seconds of a play being over, I can track where the ball was, where every player ran, what their speeds were, what routes were being run, and the chances of a pass being completed if there was a pass in play. It really is pretty tremendous in terms of normally, even as recently as three or four years ago, I wouldn't have access

to any of that on film until Tuesday if a game was played on Sunday. And now it's available 10 seconds later, which is an incredible leap forward. And that data was being handed out to teams on a one-to-one basis where they were only getting their own data in, I think, 2016 and 2017. But then as of 2018, teams are now getting every single team's data. So it's becoming a more prevalent usage or there's more prevalent usage of it within team offices and within teams sort of making their own decisions using that data. And teams are hiring people to actually wrangle that data, which was not the case several years ago. So what have been some of the primary causes of the football analytics movement? So the equivalent of, you know, sacrifice bunts are bad and walks are good, but for football.

I mean, there hasn't been that sort of the primary mover, which I think was the case in the late 90s, early aughts, which were the idea that the A's couldn't compete financially, right? You know, they had to either spend as much as the Red Sox and Yankees or there was no way they could keep up. So they had to find those alternatives. That just isn't the case in football. There isn't that sort of incentive to, what's the phrase I'm thinking of? Innovate or die. There just isn't that because teams are going to make money, whether they have 10,000 people in their stadium or 85,000 people. It's a centralized contract for TV.

You're going to make money and you don't have to innovate. You're going to make money, and you don't have to innovate. So we haven't really seen many teams be aggressive about pursuing it out of sheer necessity. Now, there are teams who want to find competitive advantages, and a few come to mind. I think there is that general idea that teams are overly conservative on fourth down in short yardage. That is the classic sort of the easiest tell of whether teams are using analytics or if they're even thinking about analytics is their aggressiveness on fourth down because history has said over and over again, teams are too conservative and cost themselves a point or

a couple points of winning expectancy every time they punt or kick a field goal in fourth and short when they should be going for it. On a financial side, I think you see that element of paying running backs, where running back has been a prestigious position for so many years. I guess the closest equivalent would be the first baseman or DH who has good power, but who you can probably replace. You can non-tender and replace with someone. There's just a lot of guys who can hit 25 to 30 home runs playing first base of DH and post an 800 OPS. I mean, there's that. The vast majority of running backs are just really fungible and really replaceable and have a track record of not delivering on long-term contracts.

And so I think over the last decade, we've really seen teams sort of react to that and not draft running backs quite as highly, not invest quite as much in them when it comes to their second contracts. And now we're seeing a bit of a reaction to that where we've seen actually teams in recent years be very aggressive about going out and paying running backs or drafting running backs highly, and it's almost always turned out to be a disappointment. So we're seeing those sort of two of the broader ones. When you combine them, you get sort of stuff that wouldn't make sense when I explain it, but for some reason has

been entrenched in football as like a thing teams have to do. Like there's that classic argument of even now you see sort of reporters or even coaches occasionally say, oh, when we run the ball 20 times, we're 21 and one. Or if we hand the ball to our star running back 25 times, we're 27 and two, which is sort of like looking at the Yankees record when Mariano Rivera pitched and saying, oh, if we just got him in the game every week, we'd go 162-0, when it's like, no, he's in the game because you're ahead or because you're tied or you're in an advantageous situation, and you wouldn't use him in that situation. But that sort of causation has not flipped for some

people as of yet. And my impression, my possibly uninformed impression, is that this has, if anything, improved football from a spectator perspective, whereas people in baseball will say that sabermetrics broke baseball, which is overblown. But there's some element of truth to it in that some of the optimal approaches in baseball are perhaps not the most spectator friendly. Approaches in baseball are perhaps not the most spectator-friendly, whereas in football, you get people going for it on forced down, which is more fun than punting. And you have a lot of passing because passing seems to be an analytically approved approach, and people like watching the ball fly through the air. So is that right? Do I have that right, or am I oversimplifying or mischaracterizing? Well, as you may have discovered on the internet, Ben, there is always a contrarian and always someone who does not like things. There was a game last year, a Chiefs-Rams game. The final score was a lot of points to a slightly fewer amount of points, but it was basically seen

as one of the most exciting games in football history. I think I wrote about it afterwards, and I said, I tried to find some way to quantify it and said, oh, this is actually one of the best regular season games in the history of football. But it was a ton of passing. It was a ton of being aggressive and chucking the ball downfield. And there was a legitimate backlash of people saying, no, I don't like this. What I like is running the football. I like defense. I like punting and field position, sort of in the same way that I think there's people who say, hey, I do like small ball. I do like punting. I do like double switches. And that's fine. I do like bunting. I like double switches. And that's fine. I think people should like whatever they like when it comes to sports.

It's a fun thing, and you should pursue the truth and the enjoyment you like from the sports you enjoy. I think they're all lying, and no one enjoys sacrifice bunts. There's nothing fun about a sackbunt. I mean, I don't like sacrifice bunts. I'm not going to lie. But I mean, people clap, right? Yeah, sure. Yeah. So is that not a way to express enjoyment, clapping? Well, yeah. I guess the clapping for a sackbunt is expressing support, I think, for the strategic value of it. I see. But I do generally approve of people enjoying sports however they like and in many ways. But those particular strategies that we've lost in baseball, now putting the ball in play and contact and base runners and base stealing,

all of that, for sure, that's fun and there's a lot less of that. But some of these strategies that have gone away, I don't miss. I have a hard time believing anyone really misses except for what they represent. I guess the self-sacrifice involved in a sack bunt is maybe admirable. What I would say, though, and I think this is sort of interesting now, is that we're seeing a lot of complaints. And I know this is true in baseball as well, but I think it's particularly notable in football this year. Everybody hates the officials and everybody hates the refereeing and everyone thinks the officiating is terrible. And I think one of the reasons why officiating is terrible or perceived as terrible has to do with

some of these analytical movements or some of these suggestions that analytics have made to how teams should play football, which is teams are playing faster than ever before. So we're seeing more plays than ever before. And we're seeing more passing plays than ever before, which means that when penalties occur, they tend to be out in the open. So they're more easily visible to us as fans. Referees have to cover more space than they did previously, which means that they're more likely to be out of position than they would be for a typical running play. If a penalty occurs on a running play, it's typically a hold or it's typically something along the offensive line, which is tough to see from the camera angles that we get to see as fans. And even if you're someone who watches the game as a layman, you're not going to necessarily pick

up on what's holding or not holding or what's a penalty, what's not a penalty in the running game in the same way you are with a pass interference call. So I think those benefits and sort of the best practices when it comes to how teams should play the game, which I agree with, has led to sort of a jadedness from fans about officiating because I think when calls are missed now, they are more significant and more obvious than they were perhaps 30 years ago. last year that was headlined for a passing league the nfl still doesn't pass enough and then there was a another article earlier this year that was headlined you called a run on first down you're already screwed which was about the idea of establishing the run that even as teams are passing more they still like to start a series with a run which sounds maybe not any better than establishing the fastball and baseball, which is something that teams have kind of gone away from. So is there still more room for teams to pass or have we

reached some kind of equilibrium here? Oh, 100%. I mean, there is absolutely, we have lived in decades of time when every single year, every single generation, every single era, we have seen writers and some coaches and some members of the NFL community saying, no, we have to start running the ball more. We're passing too much. That was the case in the 60s. It was the case in the 70s. It was the case in the 80s, the 90s, the 2000s, and now into 2019, that is the case. There are still teams, even smart teams, some teams that do have active analytics departments who say, hey, we need to run the football more frequently. We need to throw teams off. We need to establish play action. We need to do all these things that every bit of data we find is not optimal. And I mean, just on the simplest, dumbest possible level, the average NFL running play gains a little over four yards, and the average NFL running play gains a little over four yards and the average NFL passing play gains a little over seven, maybe a little more than seven, between seven and seven and a half yards. I'd have to look up the exact number.

And NFL teams complete passes now a little bit over 65% of the time. So, I mean, it's really hard to make the math work for running to be the optimal thing for any significant stretch of time, unless you have Lamar Jackson, who is the presumptive MVP this year, who is running what is likely a run-first offense because they are so effective running the football. But for your average team, which is maybe kind of okay at both things, it really does not make sense to run the ball as frequently as teams do. I don't know what the equilibrium number is, but I really don't think we have met it yet. And I don't think teams are in any danger of throwing the ball too frequently, which I mean, the good example would be the Andy Reid Eagles teams, which were right around the turn of the century, right around 2005 or so.

They threw the ball a ton and they were successful. But the moment they stopped being incredibly successful, it was, oh, we throw the ball too much or we're not optimizing our skill sets. And so Andy did not do that. But then the rest of the league caught up to him and started throwing the ball more frequently. So now we're seeing it with some other teams. But it really is not a – it's a garbage argument, I think is the nicest way to put it. Yeah. Presumably there's some game theory aspect there where you don't want to pass every time because

then teams will defend for that just like you don't want to throw your best pitch all the time because there's some value to the uncertainty but you're saying that maybe even so there's uh there's still some room to pass more frequently yes so is there an early adopter that is most associated with this movement in the nfl is there a football Billy Bean or Oakland A's? That's a good question. Is there a football Billy Bean? I don't think there really is in Oakland A's who I would say are the absolute obvious choice here. I think the Eagles were one of the teams who were most aggressively using analytics as early as probably around 2003, 2004, 2005. The shadowy example is the New England Patriots who have Phil Belichick and who seem to have a grasp for the dark arts of football and who, even though

Belichick is, you know, given all the stats are for losers, I don't use analytics, blah, blah, block quotes, has had a guy by the name of Ernie Adams on his staff for the entirety of the time he's been with the Patriots. I think he was also with the Browns as well. And Ernie Adams appears to be basically a football researcher and a quant. I mean, he is not someone who gives interviews. He is not a public figure. The way I would characterize him is he reminds me of the G-Man from Half-Life. If I ever talked to him, that's how he would talk, which is great. Having that figure is wonderful as a narrative piece for me as a writer. But the Patriots are also one of the smartest-run teams in the league. And they are incredibly, they do things that obviously make sense when it comes to using numbers and using data to make

their decisions, even if they're not saying that publicly. So Ernie Adams and the Patriots come to mind, the Eagles come to mind. In recent years, I think the obvious team, especially now in 2019, is John Harbaugh with the Baltimore Ravens, where they have hired publicly several people who were doing research into the player tracking data and into the NFL as a whole. And they have a quarterback in Lamar Jackson who is so effective running the football that they are very comfortable going for it on fourth and short and going for it in situations where other teams might consider punting, which has totally transformed their offense and has made it very, very successful. But what I found very interesting about all this is that when teams do have success with analytics, there is not that habit of copying

them. I look at Ron Rivera, for example, several years ago in Carolina, where every week in my column at Grantland, I would criticize Ron Rivera for not going forward on fourth down. And then he lost so many games by a close score that eventually he just started going for it. And it worked. They went from being a team that was totally out of the playoff race to making the playoffs, winning the division. Ron Rivera became Riverboat Ron, and he was aggressive on fourth down. And the arguments for, oh, well, we can't go for it because our fans are going to criticize us for not getting it, it proved, hey, if you go for it, your fans are going to support you. Nobody copied him. And not only did nobody copy him, Ron Rivera himself stopped being aggressive on fourth down and went back to being conservative over the course of the next several years. Doug Peterson

was very aggressive against the Patriots in the Super Bowl with the Eagles a couple years ago, where he was a massive underdog. His quarterback was Nick Foles. He was a backup, and he had to be aggressive, and he was. You would figure teams would have copied Doug Peterson. They have not. They have not copied their game plan. They've not copied their way of being aggressive when they're a massive underdog. They have not copied going for it more frequently on fourth down. And I think with Lamar Jackson and the Ravens, people are going to say, oh, well, they have Lamar Jackson, so they can go for it on fourth down. And I think with Lamar Jackson and the Ravens, people are going to say, oh, well, they have Lamar Jackson, so they can go forward on fourth down because their expected success rate is way higher than ours is. But I don't think you're

going to see teams try and emulate that because I think you see teams coming up with excuses for not being more aggressive way more frequently than the idea of doing something out of the box or being aggressive or using analytics more heavily. So in football, relative to baseball, the GM is less important. The head coach is more important than, say, a manager is at this point, at least. And head coaches in football have different backgrounds than baseball managers. Baseball managers, you know, they arguably don't do that much to affect the outcome of games. There are some strings they can pull, but not nearly as many. And so historically speaking, the GMs, the front offices got into this stuff sooner in baseball. And then

the coaching staff and the manager, they were kind of the bottlenecks sometimes that would be old school and prevent this information from actually being applied. And in football where you do have head coaches who really are calling a lot of the shots and controlling how the game goes, is that an obstacle or have head coaches kind of been the instigators for getting more analytically driven more so than the front office has? Oh, no. They're the obstacle. Yeah.

For sure. They are the obstacle. I mean, I think GMs are more likely to be open-minded to analytics. I think especially when you get to some of the younger GMs in the league. Now, I'm not saying they're using them correctly or using them in a way that makes a lot of sense. Let me give you actually an example of sort of how people think about analytics in the NFL in a modern way. And this is a couple of years old, but I think it's still pretty accurate. to give you actually an example of sort of how people think about analytics in the NFL in a modern way. And this is a couple years old, but I think it's still pretty accurate. So this was a story that I think Thomas Dmitrioff told, the Falcon's Jam Thomas Dmitrioff, who's a very smart,

progressive guy, told at Sloan a couple years ago on a panel I was on. And he talked about Logan Ryan, who is a cornerback who's pretty good, and he was with the Patriots when John Robinson, I realize I'm saying a lot of was with the Patriots when John Robinson, I realize I'm saying a lot of names, I apologize. But John Robinson, the Tennessee GM, was scouting Logan Ryan. And he saw that Logan Ryan broke up a lot of passes. And so he said, let me do a study and see who also broke up a lot of passes. And if those guys are good, it'll be more of an excuse to draft Logan Ryan. And we were like, well, that's kind of confirmation bias. I don't really know if that makes a lot of sense. And that's really a great way to do a study. That was sort of lost on the people who were there from the NFL. I don't know if they really had a sense of how to build a study

or how to conduct a study or how to do research into what might actually be predictive when it comes to sort of an NFL trade or a college trade that translated well to the NFL. I think you get a lot of people around the league who are interested in analytics. And there is a history going back 30 or 40 years of teams having a ton of data through the college draft on just how guys played in college, what their physical characteristics are, whether those characteristics do typically translate to success. So that's not the most complex or I don't know if it's the most robust analysis, but I do think you have that. On the coaching side, I think the coaches who are more comfortable dealing with personnel, maybe they're a little more aggressive with using data.

But I do think you have a lot of coaches who came up as players who don't use that data, who are not inclined to use that information to help them win. I think you see a lot of coaches and even more GMs who want to say publicly, hey, I'm comfortable using data. I'm comfortable using analytics. I think if you ask them what analytics were, I think you'd get a lot of different answers, some of which would be right, to be fair, but some of which would not make sense at all and would be totally at odds with what actual research, what actual quantitative analysis would be. Let me give you another great example from Tennessee. I feel like I'm picking on the Titans. I feel bad. But they hired Ken Wisenhunt several years ago, who was a

longtime coach with the Steelers and the Chargers, and he was the head coach of Tennessee at this time. And they asked him whether he was going to use analytics. The media asked him if he was going to use analytics. And he said something to the effect of, well, what if you're running a play on third down and it breaks down and the backup tight end gets open in the back of the end zone and you throw him a ball that you're not supposed to throw him

and it goes for a touchdown? How do you put an analytic on that? And I'm just like, well, that's not... Just because players improvise does not mean analytics are totally useless. The same way that just because players don't do what a play says on a play sheet doesn't mean that X's and O's are useless. But that conversation does not necessarily happen in the NFL. So I think you see a lot of teams, a lot of coaches, a lot of GMs who want to be open-minded, but when it comes to actual practice are not really all that open-minded

when it comes to using data. Right. So you're familiar with the discourse in baseball, the stats versus scout stuff, and then the old media versus sabermetric media stuff, which is mostly died down and everyone agrees that there's value in both and kumbaya and so forth. And that struggle repeats itself in every sport as it gets into its own analytics movement. And I'm wondering whether that has been even more pronounced in football because, you know, the old George Carlin baseball versus football and football is war and all of that. Although, on the other hand, he did say that baseball is a 19th century pastoral game and football is a 20th century technological struggle, but maybe not this kind of technology.

So because of that, is there even more spreadsheet nerds and what do they have to tell us about this or that? Or is the fact that this is coming along after this movement proved itself in baseball and has been embraced? Has that made adoption or at least the backlash a little bit smaller, quieter? Oh, I think so. I do think it's helped a lot. You know, I think that, hmm, how much trouble do I want to get myself in with this interview? I mean, I would be polite. I would say that a good chunk of the people who cover football for

a living don't know very much about football, or don't care to learn very much about football, which is fine. You know, there's plenty of ways to break a neck and cover a sport. And I think having access to that data, having access to information like that, even if they don't really believe in it, has helped them just fill columns on a day-to-day basis and give sort of a fan base that is more intelligent and more active and more desperate for information. It sort of fills that need. So I think they do appreciate that and like that. do appreciate that and like that. I think on the higher levels, we do see the occasional thing about, oh, well, what's the stack going to tell me about blah, blah, blah, blah, blah, which is, but I don't think it's anywhere near as bad as I remember it was for baseball. It doesn't mean that it might not be the case. My instinct is that sort of football is where baseball was right around 1998 or so. So I mean, it could still be coming. It just could be that it's not on people's radar and they don't care. And so there hasn't been that sort of football-heavy or analytics-heavy organization to inspire that sort of criticism.

to listen to effectively wild to sort of run their their football organization for a couple of years and they proceeded to tank and acquire a ton of draft picks and go one in 31 over the next two years so i think that you know the people who were being critical of them for being nerdy or for using sport science or all this stuff i think that did pop up but i think there is a there's a certain comfort level that comes with you know know, not understanding football on the whole and sort of trying to find ways to understand it. And then I think the inroads made by baseball, I think you can't deny that, you know, having a different perspective and using data has helped in baseball, it's helped in basketball. So I do think that that went a long way in terms of normalizing it to some extent and making it, you know, more obvious that if you do that in football

publicly, you're going to be seen as out of touch. You're going to be seen as not knowing what you're talking about. And certainly media members, and I think to some extent, football executives as well, don't want to be seen that way. So I know that you wrote last year about the history of NFL teams evaluating quarterbacks and blowing it over and over. And drafting is hard in every sport, but in football, it's maybe even more obvious when you have less success in the draft or the type of players or positions that get emphasized in the draft? Is it still really quarterbacks are far and above the best to go for? Or I just looked at my hot takedown feed on my phone and there's a new episode about the

fullback comeback. There's a resurgence of fullbacks. So is there any changing pattern or improvement there? Yes and no. I mean, I think there is a change in terms of positional scarcity. I think people are realizing how easy it is to find running backs. And again, there has been a blowback in prior years. There have been running backs taken in the top five. But I think in general, teams typically don't feel the need to go draft running backs as highly as they did previously. I think you're seeing more of an emphasis on the game as it's being played in 2019. So more wide receivers are being taken highly, more defensive backs are being taken highly. It's a league where even though the podcast that you mentioned is talking about the resurgence of the fullback, most teams don't use a fullback.

So you're seeing teams draft for the way they're playing, which is a heavy pass league, which is smart. I mean, obviously you want to draft for the game you're actually going to play. In terms of the quality of the actual draft picks, I don't think it's any better than it used to be. When I've done studies on this, not only have I seen no difference versus the past, but just no difference to even the smartest organizations, no difference between how they're going to pick and the effectiveness of their success rate of their picking versus similar teams who are not perceived as smart. The Patriots, for example. Bill Belichick is a genius.

Ernie Adams is there manipulating things from afar. They're pretty bad at drafting. What they're good at is amassing as many picks as possible. They trade down a bunch. They get a bunch of compensatory picks from free agency. They get as many shots as possible, and then they use those shots to pick players. Now, they drafted a ton of guys in the second round at defensive back. It's almost a meme amongst Patriots fans. Every year, they draft one defensive back in the second round who

is terrible and doesn't play. But you get enough cracks at guys in the second round, you're going to land on one Rob Gronkowski at tight end at some point. Just having those picks is incredibly valuable. But when it comes to even the Ravens, and I think the Ravens are probably the smartest team in football right now. They drafted Lamar Jackson last year. He is going to be the MVP this year. He was the fifth quarterback taken, which is bad. You want to be able to tell the difference between the MVP and the four other guys. They've been okay here or there. None of them are close to Lamar Jackson right now. So the best quarterback being taken at the very end of the first round, when almost every single team has passed on him is bad. But the Ravens themselves drafted someone else earlier in the first round, let Lamar Jackson slip, and then eventually decided,

okay, we should probably take Lamar Jackson. He's pretty good. So when even the team that was smart enough to take Lamar Jackson didn't take him with their first selection, that's a bad sign. And this goes back to, I mean, Tom Brady being drafted in the sixth round of the 2000 draft, or Russell Wilson being taken in the third round of the 2000 and I believe 11 draft, where it's just, I think teams are incredibly overconfident in their ability to scout out talent, and they're just not very good at it. So what's next? What's the next frontier, I guess?

Because I was at an event with Aaron Schatz recently, and he was talking about the forced down stuff and going for it. And that seems to me like that ship is not sailed, but it's sailing. It's moving in that direction. And once it starts moving, it will probably keep moving. And you can kind of project that it will get to where it should be, analytically speaking, eventually. So I was telling him, like, enjoy this while it lasts because once the low-hanging fruit is plucked, there won't be such easy targets. And in baseball, like, you can't really say, well, this team is dumb and that team is dumb because no teams are dumb. And the things that a quote unquote dumb team does today are, you know, what the smartest team would have done 20 years ago or whatever. So it gets harder to criticize and everything gets more complicated and it's, it's harder to just

take that snarky tone and say that, you happen and that should happen with any sort of certainty. So what will be next and how will the tracking technology change things? Is there a lot of sports science and training stuff on the horizon? Where's it all going? Yeah. I mean, there's definitely a lot of sports science stuff and that's not especially new. That has been going on for about 10 years. There's been a lot of emphasis on teams sort of importing people from Australia, which seems to be sports science central for some reason, which I think is fascinating to me. Did you write about this at some point? We touched on it in the book, but I think it's just like rugby was big in that and soccer and sports where you run around a lot. I think we're pretty proactive about conditioning and wearable technologies and trying to keep people on the field and not taxing them

too much. So I know baseball at least was kind of leaked to that and has now been hiring all of these people with extremely British names. So that keeps happening. But yeah, it came to football before baseball, I think, in a big way. Yeah, I think so. And I think that has grown more comfortable. I think when the most obvious, most public example of it going sort of south was chip kelly who you know came into philadelphia year one makes the playoffs everyone is excited about sports science and everyone's talking about how how different it is and how great it is and year two they had a disappointing second season they still were pretty good but they missed the playoffs and now oh sports science is awful and you know we gotta let players have you know some players have some snacks occasionally and just that sort of

classic, it's either the best thing ever or the worst thing ever. But I think it is more prevalent and more significant around the NFL. It's not as public, but I think it is something that teams are more comfortable with. The tracking data is inevitably going to be the biggest thing. And I think just at ESPN, for example, things I can say we're doing publicly, we're tracking the effectiveness of offensive linemen when it comes to their ability to hold blocks, which is something that we don't have any good data for offensive linemen. PFF has grades, and I think there are some issues with PFF grades. And the machine learning stuff that's happening with our player tracking data, I think even Brian Burke, who's working on it, would tell you it's not perfect either. But I think it's a great step in the right direction to figure out, well, how effective

is an offensive line or an offensive lineman? We didn't have data for that a couple of years ago. We're tracking what routes each player is running and how successful they are at running each route, which again, that data just did not exist as recently as a couple of years ago. And this is public stuff we're doing. So teams, I mean, I think this is a really exciting time. So I think there is a significant competitive advantage to be gained if you're willing to invest in research, invest in

analytics, and invest in the developers and the quants who can work with this data. It's not low hanging fruit anymore. And this is certainly not low hanging fruit. It's not low hanging data. It's significant, meaningful amounts of data that you need professionals to work with. And I think there is going to be advantages gained over the next few years that are going to be significant. It's going to be teams who go out and spend a million dollars on building an analytics department now, as opposed to doing it five years from now when teams are going to be behind. And I think we're just sort of starting to see what that data can do. I think that some of the results are not public yet. And I think that we're starting to see hackathons and things working with that data to sort of get a sense of how researchers might

approach it, how college students, how developers might approach that data. But I think it's going to be a really fascinating time over the next few years. And I think we're going to see advancements that we couldn't have fathomed 10 years ago that would not qualify as low hacking fruit because of people working with that data and teams investing in people who work with that data. And is some of that data and technology helping address injury issues too and figuring out what the riskiest type of plays are, which rules need to be changed to protect players? I think so. I mean, certainly the NFL is doing more research into tracking data. I think the problem is concussion data can be very difficult to gather because players aren't

necessarily inclined to self-report concussions. We've seen, I think there was one player in recent weeks who lied about having a concussion when he claimed, I think, a shoulder injury, if I'm not mistaken. And I think certainly the culture is changing when it comes to doing that and not being macho or not reporting a concussion. But I think there is sort of an emphasis on not really using this data, but more so just tracking what plays cause more injuries and tracking injuries in a way say the NFL is incredibly desperately trying to make things safer. I think there's a lot of criticisms that are fair to make about the NFL, and I don't think I'm qualified to say that what the NFL is doing is right or wrong or morally acceptable or reprehensible. I think that's another conversation. But in terms of what the NFL is doing, I do think there is an emphasis on using some sort of data to track what they can do to help kickoffs and help punts and help certain plays that are more likely to cause injuries. And did we talk about DVOA at all? Did we mention that? We did not. Should we explain what that is?

Because that's kind of a go-to thing. Sure. DVOA is the Football Outsiders Defense Adjusted Value Over Average Statistics. So essentially, just trying to track how many yards or how many points or how many points expressed in yards or yards expressed in points, depending on how you want to do it, a team should have gained versus the typical average spot in that situation after you adjust for down, distance, game situation, so how much a team is up or down, and the quality of the opposition. So that's not exactly a super advanced metric when it comes to what is being done in baseball,

but it's incredibly valuable. We don't have that sort of data in football to work with. And it really has been the best measure of team performance, I think, from year to year. And it's been pretty, it's certainly been a better predictor than win-loss record or point differential. And I think that, you know, at this point it's 15, 16 years old. So, I mean, it's certainly not a new piece of information, but I do think to this point it's probably still the best predictor of team performance we have.

And are there others that are kind of go-to, the Mount Rushmore of football stats? Because I know that there are certain football stats that get mocked a lot for how inscrutable they are or strangely calculated, but what else would be kind of your most cited stats? Oh man, I have to think about that. I do like the total QBRs that we have at ESPN, which I was a little skeptical of at first. It's essentially just an expected points framework. I guess I would say EPA for expected points added is also on that Mount Rushmore, but it's an EPA framework for judging a quarterback, and it is adjusted for opposition and for game situation, which I think is really valuable because the traditional stat is passer rating,

which is something I do regretfully use quite a bit. It's for some reason scaled to the classic 158.3 measure, the simple number that everyone understands. But it's also weighted based on what was successful and what mattered in the 1970s. So that is very different from the game today. So we see situations like Drew Brees had one of the best performances I think we've ever seen on Monday Night Football two weeks ago. And he was like 29 of 30 for four touchdowns or something. He was impeccable and somehow did not have a perfect passer rating for a chunk of that game. And it's just like, well, this doesn't pass whatever eye test you might have.

I don't love the eye test idea, but it just doesn't fit. And I use passer rating because it is familiar, I think, to a lot of people who cover football or read about football, but it is certainly not an ideal metric. So I think the expected points framework and the win expectancy frameworks that get built are up there as well. I do think that there's not a ton of individual stats that I think I would look at and say, oh man, this is the perfect stat. Something that really has advanced us all that much. I still think those stats are realistically still to come.

So football war is not ready for its closeup? realistically still to come. So football war is not ready for its close-up? PFF is building a version of war that I think is a step in the right direction, but I still think there's a lot of questions. I think it's just so difficult to separate how much a player contributed to a given play. On a 20-yard touchdown where there's great blocking, do you give a running back 5% of credit? Do you give them 50% of credit? I think it's so difficult to sort of parse that out in a way that it's certainly easier to do in baseball and even easier than – it's easier to do it in basketball than it is in football. Obviously, baseball is much easier than both when it comes to parsing out the impact of the individual on a given play, but all the passing cross-era comparisons are just pretty porked now. I mean, it's never easy in any sport to put players on the same playing field over decades, but it seems very difficult. Like, I'll constantly see that this wide receiver, that quarterback, you know, has the most yards ever in a season or whatever.

And it's like the, you like the mediocre quarterbacks now, it seems like have yardage totals that are all-time great in an earlier era. So is there any way to adjust for that? Or do you just kind of throw up your hands and say, it's a different game? I mean, I try. I do my best. I think just you compare it to the players of the era. I just did a thing on the Patriots defense that's incredible and you know people say oh they played an easy defense but you know you just look at you just standardize it and compare it to how what teams were allowing in the era and try and get as much context as possible it's never going to be perfect i mean the game is fundamentally different and that's fine it's okay i think you acknowledge that you just don't have to be you know you just

don't treat it as an absolute that that a who's 20% better than league average now is better than a guy who was 19% better than league average 30 years ago. It's relatively similar. And then you kind of try to adjust for the context and you do the best you can. But I think it's just you have to be fuzzier about things in football in a way that I think you don't have to be in other sports. And I think as long as you acknowledge that, I think that's fine. All right. Well, you can read Bill at ESPN. You can hear him on the Bill Barnwell Show.

And you can find him on Twitter at Bill Barnwell, although I noticed that on Twitter, your headshot, you have a jacket and tie, which seems somewhat inauthentic. You're putting on airs. I have some bad news for you, Ben. Those two photos were filmed or taken within 30 seconds of one another. It was literally, oh, here's a suit. Oh, why don't we try a hoodie instead? Okay, I guess that works too. Okay.

All right. That makes me feel better. So you didn't get dressed up for the photo shoot. No. Yeah. Okay. All right. Thanks, Bill.

Thanks, Ben. Okay. We'll take a quick break and we'll be back in a moment to talk about basketball with another ESPNer, Kevin Pelton. That is why you can. You're so strong, you've got to teach your son how to stand up straight when you want to run. That is why you can. How to care and love, how to be yourself, to be different, but the same. That is why you can.

All right, it is time for our basketball segment. And our guest has been intimately involved in basketball stats lo these many years, including a long stint at Basketball Prospectus, the sister site of Baseball Prospectus. Now, of course, he is at ESPN, where he warmed up for this interview by publishing a piece on the history and development of Real Plus Minus. He is Kevin Pelton. Hey, Kevin. Hey, thanks for having me. Happy to. So we are in the realm of sports and even analytical movements that people listening to this podcast are somewhat familiar with in contrast to some of the other sports we'll be

covering. But let's start the same way that I'm starting all of these, which is to ask you, on the spectrum of ease of analysis from 1 to 10, where 1 is a sport that's completely opaque and impenetrable, and 10 is baseball, basically, which is structured to lend itself to sabermetrics, where would you put basketball on that scale? I would say maybe a six or a seven. I mean, you know, the good thing about basketball is you already had a really well-developed box score, at least, you know, since they started tracking individual turnovers and blocks and steals, which was all done by the late 70s. So, you know, we have a 40-year sample of the box score, four-decade sample to work with now. The challenge, of course, with basketball is that there's so much more interplay between teammates and synergy and things like that than you have in a sport like baseball that's much more individual. But it's not probably as bad as a sport like football, where you've got that same thing, but across 11 teammates who are playing entirely different roles,

whereas in basketball, there's a little bit of all five players on the court for a team doing everything. So not quite that same degree of challenge. So can you give me a brief history of basketball sabermetrics, when it started, how it kind of caught on, maybe some of the major breakthroughs? Because it seems to me that a lot of sports are influenced by baseball sabermetric movement and maybe have borrowed some of those concepts. But with basketball, at least as I understand it, that was a very explicit connection to Bill James and early baseball research. Exactly. I mean, if you go back to what was being done in the 80s, it was really an attempt to recreate the baseball statistics in basketball. I mean, and this continued for a long period of time. Even the statistic that I developed in the early 2000s,

period of time. Even the statistic that I developed in the early 2000s, wins above replacement player is explicitly the same concept. The Cheney projection system that I did is a pretty direct homage, if not rip off of Pakoda from Nate Silver, all that sort of thing. So yeah, like you said, it really started with the Bill James books and the rise of this in baseball. And I think there were a lot of people, you know, this was later for me because it was the very late 90s, early 2000s. But, you know, I think a lot of people had the same thought, which is, hey, this is really cool to see how this is opening up my understanding of baseball, but I love basketball. How can I apply these same principles there? And, you know, among the first people to prominently do this, Dean Oliver, who's kind of, you know, probably our Bill James in basketball, wrote some stuff in as far back as I think the 80s and then more prominently in the 90s.

But then in 2002, I want to say, published Basketball on Paper, which remains kind of the seminal basketball statistics book and how to and a lot of what he developed. So the one thing we had to develop in basketball is obviously the framework is different than in baseball where you've got outs and innings and things like that. In basketball, it's all about the possession. And one of the big early breakthroughs, if you want to call it that, is figuring out that we should be rating teams by how well they score or prevent their opponents from scoring on a per possession basis. And then kind of also individuals, how well they score or prevent their opponents from scoring on a per possession basis. And then kind of also individuals, how well they factor into that. And that was kind of, Dean was a pioneer in that in the 90s. And then is with, I'm sure, all these sports you're talking about.

The publication of Moneyball was a huge moment. That drew a lot more attention to Dean Oliver's work. And then John Hollinger was the other person who was really prominent at that period of time, had written on his site, alleyoop.com in the 90s. Then after working at the Oregonians website for a while, came back to writing about basketball in the early 2000s, both at alleyoop.com and then his pro basketball prospectus series, licensing that name from baseball prospectus. and then eventually went on to write for Sports Illustrated, and around 2004, 2005, landed at ESPN and was kind of the face of the movement for a long period of time, a very similar role to what Rob Neier did in

baseball, who was really my entry point into baseball analytics. Right. And were the Rockets kind of the A's of the NBA, the early adapters, or were there other teams that were in on these things even earlier? Well, you know, this is what annoys me is that the Seattle Supersonics, for whom I worked and grew up rooting for, we like really had the opportunity to do this because Dean Oliver was working for us in Seattle and, full-time in 2004-05, the year the Sonics kind of unexpectedly won 52 games and reached the second round of the playoffs. And I think his analysis was definitely a factor in that. But then let him get away a couple of

years later to the Denver Nuggets where he was influential there as well. But yeah, Houston, in terms of publicly carrying the flag, for sure, after hiring Daryl Morey, at that point, someone who was not particularly well-known as a member of the Boston Celtics organization where a lot of his focus was on the business side and a visionary move by then Rockets owner, Leslie Alexander, to bring him over,

give him a year where he served as assistant GM and sort of learn from their veteran experience to GM, Carol Dawson, and then take over the next year and be completely empowered to make decisions using the statistics to build out what was at that time far and away the most robust department of analysts in the NBA. And the thing know, the thing about Daryl is some people, you know, are quiet about this, want to keep what they're doing a little bit hidden. That's never been the case with Daryl. He's always been very happy to, you know, kind of raise the visibility for the movement as a whole, including starting the Sloan Sports Analytics Conference, co-founding that. And yeah, so that definitely drew a lot of attention, especially once the

Rockets began being successful, even after, you know, the loss of their superstars at the time, Tracy and strategically and all of that. But are there just some old bits of wisdom that turned out to not so much be supported by the stats? So I think one thing that's generally true in basketball as compared to baseball is that a lot of what we did kind of reinforced actually a lot of the conventional wisdom for a long period of time, which, you know, is probably helpful in terms of adoption. You know, I think particular of, you know, plus minus based statistics that tried to capture what players were doing at both ends of the court. And those often showed that, hey, this role player who doesn't score very much, but is excellent defensively, is often in the right place, is more valuable than they look based on their traditional points, rebounds and assists per game. So, you know,

this was Shane Battier was kind of a figurehead or a totem of that. Michael Lewis wrote a piece valuable than they look based on their traditional points, rebounds, and assists per game. So this was Shane Battier was kind of a figurehead or a totem of that. Michael Lewis wrote a piece about him in 2008 involving because he was one of Daryl Morey's first big pickups in Houston. And that's the kind of guy who coaches had always valued, but hadn't necessarily had the statistical evidence to prove it. But when I think of things that were overturned, I mean, definitely, you know, despite that, there was within front offices, a huge overvaluing of points per game and particularly guys who were volume scorers, who, you know, used a lot of possessions, took a ton of shots, did not particularly score efficiently. Allen Iverson is probably the classic example of this. You know, his MVP season in 2001,

we can debate exactly where he ranked in the league, but it's pretty clear that statistically he was not the most valuable player in the league at that period of time. And then I think the shot selection is the other thing that particularly within the last decade has been a huge focus. And I think now to the point where when people hear statistics or analytics in basketball, they think, oh, all this means is hating mid-range jumpers, which is a little more complex than that. But definitely, you know, teams were shooting too many shots just inside the three-point line and not taking advantage of the fact that those shots just outside were worth 50% more. So it varies by sport, obviously, whether the strategies that fall out of favor or enter into vogue because of analytics are actually spectator friendly and make the game more or less entertaining. And I think the consensus is that in baseball, it's probably less.

There's more strikeouts, less contact. In football, let's say people like passing and there's a lot more passing than there used to be. football, let's say people like passing and there's a lot more passing than there used to be. And in basketball, with the rise of the three-pointer, I think certainly at first, it seemed from afar that people liked that, that it was fun to watch Steph Curry shoot a lot of threes and make so many of them, but it keeps going up and up and becoming more and more prevalent. So are we at the point now where people are starting to sour on this or worry that it at least is kind is like, basically, if you like the game as it is right now, you got to start thinking about changes because the history of this is, there's some school of thought that like, oh, eventually it's going to even out. And all of a sudden long twos are going to be undervalued because teams are putting so much focus on three.

And there's not really any evidence of this actually. If you look at the history, particularly in college basketball, where teams generally were quicker to adopt the three because it was a shorter three-point line, substantially so before they moved it back a couple of times. The three-point attempt rates just keep inexorably going up as players improve their ability to knock down those shots. And the only way that anyone has found to kind of reduce that over an extended period of time is to move the three-point line out. I think there's definitely still – it's a divide on whether it's a more aesthetically pleasing style of basketball now than 20 years ago. I think people that grew up with the 90s style of play now kind of lament some of those – the lost art of the mid-range,

the fact that you did see probably somewhat more stylistic diversity at that point, although I think that's an overplayed criticism of the role of analytics because there may be similar endpoints to how the Milwaukee Bucks and the Houston Rockets shoot a lot of threes, but they get there in wildly different ways, or even the Warriors who haven't shot as many threes, but play a similar style of play. And then besides for making this just about shot selection, the other thing that's happened, and this has been an interesting turn, when teams first started paying attention to this, kind of the cutting edge thinking, let's say on pace was that you need to play as slow as possible. That's better defensively. It's tough to defend while playing at a fast pace. And at some point, led largely by Houston, that flipped to all of a

sudden, no, if you're playing slowly, that's archaic, that's outdated. Now you should be playing as quickly as possible because of the fact that efficiency goes down the deeper end of the shot clock you get. So you want to be trying to create as many early opportunities as possible. And so pace has risen dramatically in conjunction with some rules changes. And I think people generally consider that a good thing. But again, you know, I think it's probably constantly subject to both, you know, your individual whims as a viewer and then, you know, how these trends continue to evolve over time. And can you give people who may not have been following this closely a sense of the magnitude of the change when it comes to three point shooting and scoring in general?

Yeah, I mean, the three scoring, it's interesting because of the fact that it's kind of gotten back the league back to where it was in the 1980s. And, you know, sometimes people kind of describe this as, you know, an outlier period in league history, because of the fact that you know, sometimes people kind of describe this as, you know, an outlier period in league history because of the fact that you see, you know, James Harden pushing 40 points per game and things like that. And it's like, well, no, actually, like if you look at the entire history of basketball, this is more like what it's looked like over the course of the NBA than it did in the 90s and early 2000s where it really slowed down. But three-point shooting is obviously dramatically different where we've gone from basically doubled the number of three-point attempts within the last 12 years here. It did seem to be leveling off a little bit for a period of time up through about the lockout in 2011. And then since then, the Rockets in particular pushing the limits in terms of how many three-point attempts you can take and the success that they've had has driven a lot of imitators. And it's increased more rapidly from year to year in raw terms in the past five or six seasons than we'd seen at any other point previously. And is there a point that has been calculated that says this is what the actual optimal percentage would be, and therefore,

this is how much more room there is to grow? Well, it's always kind of fluctuating because I think one thing that's overstated is people are like, oh, everybody, coaches finally realized that three is greater than two, and that's why we see all these additional three-point attempts. But if that were the case, what we would see is kind of what we do see to an extent in Houston where teams would tolerate the fact that their three-point percentages were going down because of the fact that, you know, they were acknowledging that those shots are just more valuable than twos. But that's not actually what you see at like the league level. The three-point shooting percentage has been virtually constant for that period of time. So it's just that guys have more ability to take more threes at the same percentage. And one of the big skill development things we've seen there is a lot more threes off the dribble,

James Harden and Steph Curry being more prominent among that group. And then deeper three-pointers like Damian Lillard and Steph Curry hitting a lot of shots out to almost 40 feet. Now Trey Young kind of picking up that mantle. And that's kind of been the evolution rather than just we're willing to take these suboptimal twos or threes. So how has this complicated and how have people tried to fix cross-era comparisons, which is always a challenge in sports because conditions are always changing. But in baseball, for instance, I mean, yes, maybe players who get on base and take walks are more valued today. And so we might retroactively say that so-and-so was more valuable than contemporary analysts believed him to be. But that is a case where even if you know that getting on base is good and

walking is valuable, you might still not be able to do it more. It seems to be more of an inherent skill that players can probably move a bit in one direction or another, but maybe it's not quite as subject to strategy and to the player's decision making as something like shot selection, where you can decide where you want to shoot from and rankings of who the best players in basketball are or that understanding of kind of who was great? Yeah, it's interesting. I mean, your boss at the ringer, Bill Simmons, is going through this exercise right now in the Book of Basketball podcast series that he's doing as a follow-up to the book that he published a decade ago. follow-up to the book that he published a decade ago. And that question has come up kind of repeatedly. If Michael Jordan had known how valuable three-pointers were, wouldn't he have practiced them all the time and gotten really good at them? Whereas they were kind of a below-average skill, one of the relative weaknesses of his game at the time, and it wasn't that big of a deal back then. So it's an interesting question to ponder. I think he probably would have ultimately gotten

it. But I think ultimately probably the way you have to do it is to just kind of look at, given our understanding of what wins, as you were sort of hitting at, what did players do during their era to help their teams win? And in the case of Michael Jordan, the answer is still a lot. Maybe the more interesting edge cases are someone like Isaiah Thomas, who was more of a volume scorer during his prime and was lionized for that and all of probably his softer skills in terms of his toughness, his ability to play at his best during the playoffs. But if you look at it, given our kind of modern understanding of what wins, his Pistons teams were never particularly good offensively. They won primarily the teams that did win two championships with their defense. And maybe that Thomas got a little bit too much of the credit that should have gone to

a number of their front court players, Dennis Rodman, Bill Lambert, should have gone to Joe Dumars, his backcourt mate, who was always regarded as an excellent defender. And maybe too much of it went to the guy who just happened to be the leading scorer on those teams. So that's an interesting one. I think one other thing that's important in these cross-era comparisons is when we first started doing statistical analysis, the team's paces were relatively similar. There were some differences. So the in vogue stat instead of points per game was to look at points per minute. And then generally, it started out kind of points per 48 minutes, that being the full game in the NBA. Then it morphed to 40 minutes because that was

about what the highest starters played. And then points per 36 because minutes had kind of gradually gone down. That's generally the standard if you see it these days. But better than that even is to do points or rebounds or assists on a per possession basis to normalize for the pace of the game. And that's especially now become important if you want to compare someone from 2004 when pace was at a nadir as compared to today or even in the 1980s where the paces were relatively much higher. And how involved are players at this sort of granular statistical level? Clearly they are doing the front offices and the coaches bidding to some extent as these changes happen league-wide, but was there a lot of resistance from players in

the early stages to changing the way that they took shots? And do players tend to actually dabble in some of these numbers themselves? Or is it all just distilled for them by someone with a team? And I guess, what kind of analytics departments are we talking about with the typical team today in terms of size? Yeah, I would say that, you know, to answer the last question first, I would say that the average is probably around three analysts per team, I would say. And there's still pretty wide variance among teams. Back in the day, the joke was that Houston had a basketball team of analysts because they had five while everybody else had at the most one or two. And now they don't dominate as much in that sheer numbers regard. Philadelphia has a particularly

large staff. Toronto is another team that stands out in that regard. And I'm probably forgetting a few who would be upset that I'm not mentioning them, but there's definitely a lot more investment at the team level. I think one of the interesting things is, as I think about the MVP machine and reading that and what you guys talked about in terms of the influencers, I forget what's the exact term for players who suddenly go and retire and start preaching the gospel. That doesn't really exist to the same degree in basketball, I wouldn't say at this point. Players who are really utilizing this for their own benefit and especially independent of what they're being provided by the teams. Battier is probably the closest to this and now leads the Miami Heat's analytics department

as part of his front office role in that organization. But he's very much the exception rather than the rule. I would say that kind of the big picture, like, yeah, players generally understand, okay, I'm supposed to be shooting a lot more threes and fewer mid-rangers. And their willingness to do that varies on the player, the level of success they've had,

playing a more traditional style of game. And also whether they idolized Kobe Bryant as a kid seems to be a big factor here because those guys tend to love to shoot long twos no matter what the numbers say and are kind of stubborn about it. DeMar DeRozan is an example of this. But part of it is it's understandable

because you look at the traditional metrics for success. These players are having a lot of it. And it's like, why do I need to do anything differently? Often my team is winning. I'm very successful. Why should I change my game? We saw this kind of this year with Zach Levine of the Chicago Bulls, who's someone who's oddly is a very good three-point shooter, but also liked to take a lot of these kind of like hero shots off the dribble from 18 feet and was pushed by the organization to turn those into three-pointers and kind of went public with some displeasure about that. And I guess another thing that baseball and basketball have in common is that they each have analytical organizations who really introduced the term process and then kind of went

to an extreme when it came to tanking. And I guess the Astros started it and the Sixers kind of took it from them. But in basketball, I think it's my perception is that tanking is seen as a bigger problem, if only because draft picks in basketball are more valuable and can really change your franchise around. So is there an analytical component of that when it comes to either valuing draft picks or deciding when to stop trying or how to try, I guess, less aggressively? Yeah, I think so. I mean, I think the number one thing that stands out in basketball is the value of a superstar player. Because first off, you've only got five guys on the court. The influence that a single player can have is much larger. You get to determine who has the ball at any given time. It's as if the Angels could bring up Mike Trout every single time if they wanted to. That sort of thing, that's a huge difference. And then the other element of this that's specific to the NBA is that as part of the salary cap, there's a maximum individual salary. So the very best values in the league tend to be, first off, draft picks on their first rookie

contract because that's limited by a scale set by where you're drafted over your first four years in the league. And then it's superstars who are worth more than the maximum salary. And if you look at what those two things have in common, it's high draft picks because those are very likely to yield superstars. They're likely to players who are influential right away because of the fact that there's not the extended minor league odyssey that you see in baseball. And there's more certainty about kind of how those players are going to translate from the amateur game to the pro game in basketball. And for all those reasons, it is extremely critical to get top draft picks. And, you know, basically kind of,

there's always been, this incentive has always existed. And, you know, when Sam Hickey went to the Philadelphia 76ers from Houston where he'd been the assistant GM under Morey, you know, he was kind of the first to aggressively plan an entire franchise around this strategy and make it the core of everything they're doing and, you know, sell off their entire roster, much like Houston did, tear down for a period of time, set an NBA record for futility in the third year. And then also, you know, not only accumulate their own draft picks, which ended up going, you know, third, first, third, I think over a three year span, but then also, you know, pick up extra draft picks from other teams as part of these trades and very long-term thinking. And that was definitely a concern for the NBA because it was like, okay, it's okay if teams are doing this for one season or particularly if like what we see with the Golden State Warriors this year where, all right, you came into the year trying to compete for a playoff spot. You had a bunch of injuries. Then you decided to go south for a season. Not that big of a deal.

But if teams are kind of just being this open about trying, not trying to lose, but being willing to lose, not trying to improve your roster in the short term, all of that, then that became kind of a black eye for the league. And definitely there was some level of involvement from Adam Silver in the Sixers' decision to eventually hire veteran executive Jerry Colangelo over Hinckley and Hinckley's decision then to step away and ultimately be replaced by Colangelo's son Brian as the Sixers GM. But I think probably it's valuable for the league that the toll was so high for Hinckley individually in terms of losing his job and not being able to see this through because of the fact that ultimately that process did yield a pair of star players in Joel Embiid and Ben Simmons.

Could have yielded a third had things not gone so horribly wrong for Markel Fultz, their other number one pick they made. And now the Sixers are championship contenders despite, you know, mixed reviews of their decision making since Hinckley's departure. So we should talk about rest and load management. And I guess this sort of has a precedent in baseball when it comes to pitch counts for starting pitchers, let's say, which sort of hit a wall when people started studying what the effects of this were and heavy workloads were. The science around that is and always has been sort of hazy. But I think teams have chosen to be cautious when it comes to pitchers, at least. And now we're seeing the same sort of thing with NBA stars and

taking days off and games off to rest up. And of course, that has to do with the different emphases on the playoffs versus the regular season in basketball and baseball too. But has that been seen as a spectator unfriendly development in that it may mean that you see less of your team superstar if you're showing up during the regular season? And how solid is the science that all of that is based on and what sort of technology has been brought to bear to say that, yes, this is how we should manage these players' workloads. So for sure to your first question, I mean, definitely, you know, the other element of stars being so important is, you know, the ticket prices for a single game against the Lakers to see LeBron and Anthony Davis or the Bucs to see Giannis Adetokounmpo are going to be dramatically higher than, you know, at least on the resale markets. But sometimes even, you know, if teams have variable pricing,

what they're charging themselves, then to see the Sacramento Kings or whoever else it might be that might not have a player of that caliber, which is kind of a different problem than baseball faces. And then the other element of it is the TV ratings, having those teams featured in national TV games, and when now Kawhi Leonard in particular has missed a number of those games to manage his injury and the fact that he's not playing back to backs at all at this point. Yeah. It's become a huge talking point. I would say one difference is it probably isn't as strongly associated with analytics per se in basketball. And it hasn't really necessarily been driven by, by that side. It's not necessarily outside studies in the same case as baseball prospectus and going beyond 100 pitches and the effects of that. There's not

like a metric that people look at in terms of the workload. It's anything more advanced than just the total minutes or minutes per game you're playing. So it's not the same level of statistical analysis, but there's probably some commonality in terms of organizations that are kind of looking to push every edge philosophically because of their interest in maximizing these small edges probably have something in common in terms of they're using statistics and their interest in this sort of thing. And that difference in structure where baseball's playoffs are just so random and very based on just unpredictable outcomes that aren't even that related to true talent, whereas basketball is you're quite likely to win if you are the better team over the course of a

series. Does that change very much how teams approach, I guess, A, the regular season, which we just sort of talked about, but also just like strategies or the type of players that you acquire? I mean, I guess also the fact that so many teams make the playoffs in basketball, it just seems like as much as there's an emphasis on the playoffs in baseball, the sabermetricians just sort of, up their hands when we get to that point and say, who knows anything? Whereas I assume it's sort of the opposite in basketball. Well, that's Billy Beans. His stuff doesn't work in the playoffs, right?

Daryl Moore has never claimed that, suffice it to say, even though they have not been able to break through yet in the championship. suffice it to say, even though they have not been able to break through yet in the championship. Yeah, I think the fact that so many teams make the playoffs is a big element of this because very few teams come into the baseball season guaranteed of a spot. And there is certainly a substantial difference if you end up in one of those wildcard spots as opposed to winning your division now. So there's a lot for teams to play for in the regular season. In basketball, you know, you come in, if you're Milwaukee this season, you are certain you're going to make the playoffs unless things go horribly wrong in terms of injuries. And what matters is mostly your seating, whether you're going to have home court advantage in these series,

which is valuable because of the fact that, you know that another factor is home court means more in basketball than home field does in baseball, particularly having game seven at home if a series comes to that. So that is one reason to play for it, but definitely teams have seen that it's better to kind of conserve some energy over the long regular season, maintain some of it for the slog that is four rounds of the playoffs at a minimum of four and up to seven games apiece. Then you mentioned the types of players. That has become an interesting question. Draymond Green of the Warriors famously framed this a few years ago

when he was talking about the team's draft. This was repeated secondhand through their front office, that there are 82 game players and there are 16 game players, referring to the number of games you need to win to win the championship. And there are some skills that don't translate as well. Guys who are kind of weak at any one area of the game, you can probably get away with that during the regular season, but you get in the playoffs where they're scouted much more.

You're seeing the same opponent game after game. Teams can adjust and take advantage of this. The Warriors famously did this a few years ago in a playoff series en route to their first title against the Memphis Grizzlies who had Tony Allen, who's one of the best perimeter defenders of his generation, but an incredibly poor outside shooter. And they basically decided to stop guarding him almost entirely at that end of the court. They put their center on him, let him just stay in the middle, you know, stay towards

the paint and deter and, you know, mess up everything else. The other four players on the court were doing, and Tony Allen also had a hamstring injury at this point. And basically they played him off the court, the combination of those two factors, and it changed the series pretty dramatically. Most of the time, it's not that extreme, but definitely the value of a superstar player or a well-rounded player becomes greater in the playoffs and those players are highly coveted as a result. One of the aspects of basketball analysis that fascinates me most because there isn't much of a parallel in baseball is the way that players work together and that synergy between teammates. And how have people tried to quantify or project how a player will perform with a certain set of teammates as opposed to another?

Because in baseball, it's basically plug and play. You know, you just kind of put them in and they do what they're going to do. Right. I mean, it's interesting to see that changing a little bit. It's more at the organizational level where they kind of have influence on what the player is doing than, you know, regards to their teammates. It's probably more one of those things that remains art than science, I would say at this point. There's definitely been, you know, attempts to quantify it. And we've seen a little bit that there are some skills that, you know, provide synergies and others where they're diminishing marginal returns.

Rebounding in particular is something like this where because of the fact that such a high percentage of defensive rebounds are uncontested, basically if you bring in a great rebounder, the odds are they're not just getting rebounds that would have gone to the opposition, they're also taking away rebounds from your other teammates. And then you have to kind of adjust for that to some extent. But also it's a reason to just not go out and, you know, focus on, all right, we need to get all the best rebounders we can, because you're only going to derive a slight benefit from that. And you're probably going to have a cost in other areas of the game. There's not a lot of good metrics though, that deal with this, I would say, you know, it's again, more just kind of knowing the game and, and understanding some of

these relationships of how things work together. But I think one thing that does reflect this is the use of statistics that are not based on what's the individual stats in the box score, but based on plus minus and how the team does with you on and off the court, which is probably the one area where basketball is really drawn from hockey, where there was that tradition of plus minus. And it existed even before the analytics era, like some teams would track it themselves, but it's definitely become way more prominent. And then using advanced statistical techniques to adjust for who your teammates are, who your opponents are, you know, analysts have tried to use those methods to kind of understand. So

if you're, Brooke Lopez is maybe the best example of this right now with the Milwaukee Bucks. He's someone who has an incredibly low rebound rate for a seven footer, like historically low rebound rate for a seven footer. But it's not that he's a bad rebounder. The reason is he's constantly blocking out his man and giving a teammate a chance to go grab that rebound because of the fact that the best offensive rebounding threat on the other team is neutralized. And so his teams tend to rebound better with him on the court, even though he doesn't get many rebounds. And that's the kind of thing you sort of have to go to those on-off stats plus minus to value as opposed to doing it from the box score. All right. And lastly, how much precision have tracking cameras

added to this whole conversation and what's next, whether it's more tracking or wearables or sports science stuff, sleep monitoring and so forth? Yeah. I mean, so what's interesting about the tracking data in basketball as compared to baseball is that it has not been released publicly to the same degree. Like so much of the advancement we've seen in our understanding in baseball from the tracking cameras has been some specialist figures out what to take from spin rate or whatever, the speed of the ball off the bat, that sort of thing, and then eventually gets hired by a team.

But in basketball, it's generally been mostly only available to the teams. When Stats Inc. first installed the cameras, they made a lot of the data available to researchers, several of whom presented it at the Sloan conference. And there was definitely some valuable gains from that. But since Second Spectrum took that over, it's not been the same degree of access, although we are fortunate to have access to it at ESPN. A lot of the tracking data that above and beyond, there is a lot of it that exists on the NBA.com stats pages. And that's been a really awesome addition and improvement over the past five or six years here. But it's a little tougher kind of to answer the fundamental questions of the game from what's

available publicly. That's probably something that teams are ahead of the public sphere in terms of that level of understanding and maybe the biggest difference between those two. How much has it added? It's interesting. I think it's been very useful for valuing some granular skills. One of the first of those papers that was really influential was Kurt Goldsberry working with a group to look at the percentage that opponents shoot when a given defender is in position around the basket. And that proved to be a much better way to value rim protection, the skill we know is important and valuable, than just looking at blocks or no matter how you're handling them per game, even per shot attempt, that sort of thing. Because of the fact that you can alter a shot without necessarily blocking it, or you can just deter it from even

being taken in the first place. So that sort of influenced how much we valued rim protectors, had played a role in the defensive player of the year voting a number of years, I think, since that was introduced. And that's been really important in terms of like necessarily being able to say player A is better than player B. I don't know that the tracking data has added a whole lot because it's one of the challenges in basketball is really like, even if we can evaluate the skill particularly well, understanding how important this skill is compared to this other skill is more difficult, I think, than it is in baseball, where you can sort of plug those into a linear weights type formula and understand what the effect is, partially because of the fact that,

you know, for all the teamwork reasons we've talked about, in baseball, you can assume that one player's performance is reflect, how a team wins is reflective of how a player helps his team win. In basketball, that's not necessarily the case. So it makes it a bit more difficult. You have to use some of those adjusted plus minus techniques, I think, often to understand the value of certain skills. What's next, I think, probably is largely on the health and performance side is, I'm sure, is somewhat similar to baseball. You know, trying to take the growing data sets in terms of wearables are allowed in practice.

They are not allowed during games. So teams have access to that data about what their team is doing during practice and then can kind of map it on to the tracking data for how their players are moving during games and understand what that means. From the outside, very difficult to do anything with that data. But I'm sure on the inside, they're really looking at that in terms of how to maximize performance. Beyond that, I think kind of continuing to refine

and understand what we are getting from the tracking data. And like I said, understand which skills are most important. And then deal with just kind of the evolution of the game because in part because of the analytics movement and in part because of just the evolution of the game because in part because of the analytics movement and in part because of just you know the the evolution of skills uh the game is played in a very different way than it was even five or ten years ago both in terms of the rise of threes and pace but also interchangeability of players at different positions the versatility there's kind of the positional revolution uh or positions don't matter which, which is taking it a bit too far,

but understanding how that continues to affect the value of players. One thing that happened is for a long period of time, I was very resistant to the idea of having different replacement levels for different positions because those things are much more fluid than they are in baseball. What a center does on one team is not the same as what a center does on another team. But one thing we saw is that the skills that are typical of these seven footers, these centers, suddenly it became much easier for them to shoot a high percentage. They're grabbing a lot more rebounds because of the way that teams have kind of given up on offensive rebounding, have put less emphasis on that. And all of a sudden, the stats were kind of out of whack where if you did things

the traditional way, centers dominated your leaderboard, even though we know that if centers were actually just so valuable, we could just play a bunch of centers, but you can't really do that without diminishing returns. So there was something wrong with how we were valuing those players. And I had to adopt positional replacement levels and other people have kind of used other techniques to deal with that. But that's a case where kind of the statistics catching up with what's happening on the court rather than the other way around. And with the sports science stuff, is the league and the union, are they still sort of negotiating what is and isn't allowable and trying to protect players' privacy? Have they hammered that out or is that still very much an open question? of protect players' privacy? Have they hammered that out or is that still very much an open question? Very much an open question and probably not one that will come up again until they do the next round of collective bargaining, I would say. It's one thing that tends to get tabled until then. There have been cases of players, I think, wearing watches that had wearables on the

quarters and then suddenly that being discovered and them being prevented from doing so. I think sometimes even the players themselves might like to go a little bit farther than the Players Association as a whole would him on the fabulous Pelton cast where he talks about Seattle sports and food. And I should also say you can find him on Twitter at KPelton. Thank you. This has been a great overview. I learned a lot. Appreciate it. Yeah, it's a lot of fun. Thanks for having me.

All right. That will do it for today and for the first installment in this series. Hope you liked it. If so, there's much more on the way very soon. Next time we'll be tackling hockey and cricket. You can support the podcast on Patreon by going to patreon.com slash effectivelywild. Maybe you

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Why should I compare? I have problems on my own Each heart stops like the other billion Scars are whiter from the million Rubberneck, the freak show, you're already there There's just your little time And now there's even less