Effectively Wild: A FanGraphs Baseball Podcast - Effectively Wild Episode 1479: Multisport Sabermetrics Exchange (Esports and Volleyball)

Episode Date: December 30, 2019

In the fifth installment of a special, seven-episode series on the past, present, and future of advanced analysis in non-baseball sports, Ben Lindbergh talks to ESports One Head of Esports Data Science Tim Sevenhuysen about esports and then Volleymetrics founder Giuseppe Vinci about volleyball (43:25), touching on the origins of sabermetrics-style analysis in each sport, […]

A color invasion is showing on the screen You take control now of this war machine You play the role of a video warrior You play to win but the game got control of ya You play the role, you play to win Video Warrior You play the role, you play to win, video warrior. Supermetrics Exchange Series, in which we provide a primer on the state of advanced analytics and a dozen different non-baseball sports. If you're just joining us now, we've already discussed eight of them, American football, basketball, hockey, cricket, tennis, golf, soccer, rugby, and today we are tackling esports and volleyball, which some of our regular listeners may be a

little less familiar with, but that just means there's more to learn. They're both really interesting analytically. So let's begin with esports. And I am joined now by Tim Sevenhusen. He is the head of esports data science at the esports stats company Esports One. He previously led the esports analysis company Shadow, and he's also the founder of the League of Legends stat site, Oracle's Elixir. Hey, Tim, how's it going? Hey, going great. Thanks for having me on. So I was just looking at your LinkedIn, and I'm curious about how you got into this world, because at a certain point, it shifts from non-esports stuff to esports stuff. So it's accounts representative at BC Hydro, and then a research analyst and a market research analyst for the government of Alberta, and then suddenly you're diving deep into esports.

So how did you make this transition and become sort of a pioneer in this world? Yeah, so from the perspective of my job titles, it is definitely a big jump. Behind the scenes, you know, there's definitely a more gradual transition going on there. So, you know, a quick version of kind of my backstory in esports is starting as a gamer growing up just playing specifically online multiplayer games uh competitive you know way back as as a pre-teen and you know a little before that watching starcraft which is the first real esport watching that online back before online video was really a thing you know it's starting to come in and and there's you know the emerging arms race on how do you broadcast video on the internet and

and that's kind of happening around the same time as starcraft is getting big on tv in south korea so you know i definitely had some late nights uh as a teenager watching that online and just getting so excited about this idea is that video games can be a sport and you know this is a real thing that people are doing professionally and you can watch it with you know professional broadcasts and all of that and so you fast forward a bit to League of Legends coming out and a bunch of friends who were playing that and invited me to play it a little bit.

So I played a bit. It didn't hook me that much. But then in 2012, the second League of Legends World Championships, which was, people who know League will tell you, that was, in a way, the first real World Championships. The first ones were pretty low-key, kind of just attached to some other gaming event. And then the second time around in 2012 when they did it, they had their own real big standalone tournament. It was a much bigger deal.

And I came across that and it kind of brought back those StarCraft feelings of watching esports emerge. And that's what hooked me in. I started playing more, go a few a few more years forward to 2015 i founded oracle's elixir as a way to take one of my passions or ways of engaging with sports which was through statistics and player and team performance i started gathering some of that data i saw that nobody else was really doing much with it in terms of the public and the community and kind of the discourse around it. So I made a website. I started presenting player and team stats, making content with it, sharing that with a broader audience.

Turned into some freelance writing, which turned into some consulting work with pro teams. And then eventually kind of the big jump into full-time esports with the Shadow Analytics platform. So there was a very long gradual path to it. But as anybody who works in esports will tell you, the jump between being an esports fan or community participant or whatever you want to call it into actually working in esports and making it your livelihood, that's a big transition and a big switch to flip this early in the history of the industry. Right. So in some ways, you have the most challenging job of any of my guests in the history of the industry. So in some ways, you have the most challenging job of any of my guests in the series because

I've been bringing on people to talk about one sport apiece, and I'm bringing you on to talk about esports, which is really sort of an unfair assignment because it's a blanket term for many games that vary completely in every possible way and certainly in their conduciveness to analysis and their use of stats so we could do a whole series on analysis just in dota and overwatch and league and starcraft and counter-strike and super smash brothers and street fighter and on and on but the way i've been starting these is by asking my guests to sort of position their sport on a spectrum of ease of analysis where, say, one is impenetrable, just completely opaque to analysis, and 10 is maybe baseball, which lends itself very much to

sabermetric-style analysis. So if you were to try to place the various different varieties of esport on that spectrum, roughly where would you group them how would they be ordered yeah so maybe we just go through a few of the kind of the biggest most popular esports titles so league of legends being the one that that is you know the world's largest esport it's the one that i have the most direct background in myself i'd probably put it somewhere around a six i think there is there's a lot of data that can be worked with and a lot of things about the game are measurable and concrete and that you can afford with but there's also there are also some really big disadvantages with the game around you know comparing one one action to a similar action of the same type but at different times the context

around those actions can be entirely different so you you can't compare you know an at bat to an-bat yeah you know we don't we don't have that kind of thing in league of legends you can't really compare one attack to another attack they're completely different so i tend to i try to lean a little more on the optimistic side and say that there are a lot of advantages to it but there are some really big challenges you look at counter-strike a different game it's probably more seven or an 8 somewhere in that range it's a lot more segmented so you play, you know, one game would be the first team to win 16 rounds

and so you can actually segment the data into rounds and you can measure and compare each round and the data access is great in fact, there's pretty much no limitation on the data access for the game so yeah, I'd probably put that one around an 8 Dota, probably a 7. So Dota 2 has the same very open data as Counter-Strike,

where if you know how, you can pretty much get any data points out of that without having to have infrastructure or much technology or anything involved. But it has a lot of the same challenges as League of Legends, of the internal context of the game is very difficult to penetrate. So putting that down, I guess I would probably have to bump my League of Legends score down a little bit, maybe put Dota at a six and League at a five, something like that. But it's definitely different game to game on what the data access looks like, what the nature of the game is like, and how that kind of affects your ability to measure and move

forward with it. Yeah. So I don't know if it's possible to give me sort of a brief history of esports analysis or sabermetrics. I don't know whether you can do a broad general one or whether you'd have to go sport by sport. But obviously, when you're talking about esports, you're not talking about things that have their origins in the 60s or 70s or 80s. So this is all more recent events. when did this get started i guess how has it kind of caught on to the extent that it has and maybe what have been some of the

big breakthroughs or demonstrations of the potential yeah it's really interesting i think talking about those kinds of things because in esports we're still really in in a lot of ways in the birth of esports as professional kind of spectator sports different games have their own length of history, League of Legends has been around as an esport for 9-10 years Counter-Strike is going on getting close to 20 I believe

Starcraft, right now we're calling it Starcraft 2 but before that it was Starcraft 1 that game's been around for over 20 years so esports as a whole even with a 20 year lifespan to a certain game, that's not very old. And when you compare that to traditional sports, which have had this whole arc of the game gets invented, the game gets perfected, the game gains kind of mainstream status. And then eventually you come up with all this other infrastructure. And what does it mean to be a world-class coach of baseball or hockey? infrastructure on that. What does it mean to be a world-class coach of baseball or hockey?

And what does it mean to then have a team of staff behind that coach helping them and helping the players, sports science, analytics, all of those things. But you have esports. So if we can find it to League of Legends as an example, you had the first real professional kind of leagues were coming around in 2013, 14. I believe it was 2015 that coaches became kind of a mandated part of the league structure that there was actual financial support and you could have a coach on stage with you at the start of the game when you're kind of preparing and these kind of infrastructure aspects came around. So if you give it, you know, a somewhat generous timeline of four or five years that a team would even have a coach, then the time span for getting analytics involved is much much shorter yeah but at the same time the history of when these games are coming about is at you know at a point in overall sports culture

history where analytics is becoming more and more of a thing you know right you started with the big oh hey moneyball everybody loves analytics and then you go into the basketball and more of a thing. You started with the big, oh, hey, Moneyball, everybody loves analytics. And then you go into basketball and you get a similar movement, and that starts to kind of percolate out into all the other sports. And esports is being born in the height of this push. So you know that there's not going to be 40 years of League of Legends, and now analytics suddenly becomes a big thing. Analytics is a big thing culturally,

and also League of Legends is becoming a big thing culturally. So it's almost that the runway has been significantly shortened for analytics to come in. But you also are still in the process of creating that infrastructure or that understanding of what does it mean to staff a League of Legends team? What does it mean to have a coach, an assistant coach, an analyst, a data analyst? Does that even make sense? Is that going to work for that person to do? All these other things are happening. So there's a lot of really interesting kind of things happening around what analytics means in the esports space. So you definitely have from game to game different levels of maturity. Some teams don't really do any kind of data analytics. Other teams are really embracing

and trying hard. Some are maybe arguably spending too much money on it, thinking, oh, there's got to be something valuable here. If we put three data scientists on it for a year, we're going to be way ahead of everyone else. But maybe it's not there yet maybe that's a waste of money at this point so it really does depend on team to team who's trying what what's working for them what happens to be the landscape of data in that game counter-strike is probably the most mature in the sense of if you have a data scientist on your team they've got access to good data they can do some very cool things with it you have a game like Overwatch, which has become a very big esport, largely by virtue of being invested in with a lot of money by the publisher. But it doesn't really have any data access. So they don't really have anybody doing analytics around it, very minimally.

So, yeah, it is very difficult to speak to this across kind of the entire esports segment. It's quite different game to game. Right. I wrote about Overwatch earlier this year when they introduced their stat player impact rating, which was sort of a baseball style stat, an attempt to kind of put players on the same scale where, you know, average is a certain number is 100 and then above or below is better or worse. And as you said, it's sort of a black box stat because a lot of the components aren't public. And so it's hard to exactly figure out how it works. But is that something that many esports have made progress in when it comes to isolating a particular player's value to a team because of course you have different roles and you have players who maybe gel well with other players on a certain team and not with others and so it can be complicated to isolate that performance and say that this person is worth

that much yeah so i'll say on the overwatch side like i mentioned a minute ago there's there's really not much data access on Overwatch. And so the only work being done in analytics and Overwatch really is either people who are building computer vision solutions that try to scrape a very minimal amount of data off of a video, off of a broadcast video. There's a little bit of that, and I think there are

a couple of teams that have been working on that side, but nothing public. And then there's the League itself. So the company that owns the game, and owns the the league and owns the broadcast and they do everything themselves and they have a couple of people who they have access to their own internal data, which is, you know, it's a decent data stream. It's just they don't make that data available to anybody other than themselves.

And so they're working on some things. But a real challenge when that's the situation is you don't have the ability to put this out. Here's what I built. Here's how it works. And here's the output. And then have other people come in, in,

you know, it doesn't even necessarily have to be critiquing it, but maybe iterating on it or saying, Oh, you did that. I think I can do this way. I built mine. Now you take ideas from each other,

whatever it is. You don't really have that interplay of, of kind of ideas and growth. It all happens behind closed doors. You come out with you know a final a final product and then people either take over take it or leave it so i think that's kind of why i say that in overwatch there's not really much happening because you you never get to go under the hood counter-strike is definitely the game that's gotten the furthest along kind of what

you're talking about like a you know some kind of player rating some kind of you know basically a wins above replacement style metric and that's partly because counter-strike is possibly the only esport that really lends itself to that type of analysis where you can kind of isolate a player's influence on a game and say these are the actions they contributed here's their team's performance those kinds of things So there are some player ratings that come out on certain community sites. HLTV.org is really the hub of kind of community analysis for Counter-Strike. And they've got a system, I believe they're on a 2.0 of that system right now that kind of rates players. I'm not sure how well-known the algorithms are. I'm calling algorithms as generous, to be honest.

But I'm not sure how well-known the systems they use are, I'm calling them algorithms, is generous to be honest, but I'm not sure how well known the systems they use are, the way they weight different inputs, but there is at least some movement in that direction. When you look at something like League of Legends or Dota 2, the games themselves are just so inherently complex that I'm not sure that style of metric is really feasible. It can be done, but can it be done in a way that's actually useful or meaningful. I've done a little bit of work in that direction myself.

But the big challenges are that really when you're doing something analytically, you want to boil data down to the smallest unit of analysis. So you want to be able to take, you know, every step or every swing of the bat or every shot, whatever it's going to be, and evaluate the outcome of that shot. Where did they shoot from? Was it contested or not? Did it score? How many points was it worth? All of those things.

In a game like League of Legends or Dota, those actions, you'd never repeat the same action under the same circumstances. Something you do at the five-minute mark of a game is completely non-comparable to something that happens to the 25 minute mark of a game because for a whole variety of reasons but the the biggest one is that the each player's character increases in power over time but they increase at different rates partly by the design of the characters and partly by the fact that when you have a successful action in the game, you're rewarded, and when you have an unsuccessful action, you're punished for it.

So you can actually become stronger, and you have an inherent advantage over your opponent. So it would be kind of like every time you get a hit in baseball, your back gets a little bigger. Well, if you get a hit and now you get another hit, was the second hit as good as the first hit? Well, it was a little easier. So how do you evaluate how to compare those two hits?

Can you now talk about somebody's batting average without having to account for, yeah, but what was the average size of their bat during each hit? And now add 10 more of those variables into every piece of analysis. And then even leaving aside the question of whether the data is there to do so so you have those kinds of kind of contextual complications i usually call the snowball effect of just kind of denying the analyst the opportunity to to break things down into a very granular level so you end up having to analyze things with an entire game as your data point and just kind of like hand waving away some of the context that that goes underneath that that. And I think that's why kind of overall kind of player impact ratings or anything

like that hasn't really emerged outside of Counter-Strike, at least not in a way that I view as very robust. So when I wrote about esports stats a few years ago, at the time, people, particularly in MOBA games, multiplayer, online battle arena games like Dota and League, were talking about trying to port over concepts and frameworks from other sports. So things like plus minus or win expectancy. So have there been some concepts that have been helpful that have transferred over despite the differences in the games? helpful that have transferred over despite the the differences in the games yeah i think there are definitely there are definitely concepts that have been helpful to kind of borrow from or be inspired by in traditional sports and there are also other areas where really you you need to kind of break yourself out of that thinking and say no we're dealing with something completely

new we need to come up with a completely new way to kind of measure evaluate it so something like a like a win probability that's one, that is definitely useful and very interesting. It's something that I've done some of my own work in over the past couple of years. And especially recently being able to look at a game and say, what are the important kind of factors in this game? Therefore, who do we think is going to,

you know, what's, what's the chance of each team winning at this given point in time, or being able to do that before a match and say, you know, what are the probabilities of each team winning before? And then you can carry that through into other forms of analysis. This action changed the win probabilities by this much,

so it kind of had that much value, and you can compare the value of different actions under different controlled circumstances, things like that. So that's one that, I mean, it's fairly general. It's a little hard to say that you are kind of borrowing that from other sports but but generally speaking that is that is a kind of a useful area but areas where i think people have tried a little too hard to imitate what's been done in other sports is something like kind of the wins above replacement style stat kind of the the all-encompassing single number that measures the player's value uh it's it's so it's a it's such a great kind of idea to chase after uh and for you know understandable reasons it's kind of a holy grail but i mean

just kind of spoke to some of the reasons why i think it's actually not not necessarily feasible or not in the way that that we might think it is so it's more useful to say well what what is this you know what is this data able to tell us? And what's useful from that? And let's come up with something new. So something that we would do in MOBAs, for example, is kind of basing some comparative stats on specific time points. That's something that has been kind of natural to MOBAs from the very start. So what is the difference in creep score,

the number of kind of neutral or enemy minions I killed at the 10-minute mark or the 15-minute mark. That's something that I don't think has a really direct comparison in traditional sports because typically the time in the game, it matters, but it doesn't actually affect the game itself. You're not more likely to score a basket at 10 minutes than you are at 14. So that doesn't necessarily have the same kind of comparison point. So yeah, they're just things that are more unique to the games themselves so for people who don't follow esports there are many teams that compete in various esports so

have there been particular teams or even particular players who have become most associated with some sort of analytics or you know has been has been sort of like the Oakland days of esports essentially? Yeah, I would say there have definitely been teams who tried to become that. I don't know that anyone's really taken that mantle up. There was a team that came in and just recently actually was acquired and rebranded called Clutch Gaming and they were owned by the Houston Rockets. And so understandably they came in and they said, Hey, we want to, we want to carry on kind of the owned by the Houston Rockets. And so understandably, they came in and they said, hey, we want to carry on kind of the legacy of the Houston Rockets

and become an analytics-focused team. And they incorporated that into some of their initial branding and so on. They hired some staff in that direction. But that never really came to fruition in a way that was, you know, the public never really latched onto that as this is our impression of that team. That's the analytics team. And they never really had the greatest results for a while. You don't get a brand like that without winning and proving that it actually mattered or made a difference.

So, yeah, they tried it a little bit. There have definitely been organizations that invested more money into having data science staff that might bridge across multiple games. You have, in some cases, teams that did that just because they felt there was value in it. into having data science staff that might bridge across multiple games. You have, in some cases, teams that did that just because they felt there was value in it. In other cases, teams that had commercial partnerships that pushed them in that direction. So Team Liquid is one of the biggest global esports brands that they formed a relationship with SAP, effectively a sponsorship where SAP is providing both financial resources and also technical resources to create analytics tools. And a similar thing has happened more recently between an organization called Cloud9 and Microsoft,

where Microsoft is providing both financial and technical expertise to try to build analytics solutions for Cloud9 and League of Legends and Counter-Strike and other things. Those still, I would say, haven't broken through to the level of what you're referencing because they are mostly commercial. They are there to tell a story to an audience. Microsoft and SAP are both going out there saying like, hey, look, you guys love esports. We do too. We're investing into it and we're doing cool stuff.

And look what our technology is able to do. They aren't really, there hasn't been any connection drawn between the analytics work they've done and outcomes you know here are the players we scouted here are the games we've helped to win they try very hard to tell that story to kind of their marketing materials and so on but there isn't anything concrete to it so far there's a lot of work still happening behind the scenes of course but so yeah so they're definitely people trying to become kind of that open days or that that houston brand, but it's still early and it hasn't really happened yet. And I don't know if esports was around long enough before this sort of analysis started for misconceptions that weren't backed up by the data to form and be cemented. But has any sort of advanced analysis overturned any beliefs about any particular games that just weren't

really supported by the numbers there aren't really huge home runs in that in that direction but there are some yeah there are some there are some cases where where that has happened to a certain extent the the best case i'm aware of is is a piece of my own work you know which i don't bring up just because it's my own work but because I think it is the best example so in League of Legends there are, for the past few years at least because they change the game very regularly and alter things about it they brought out a system

of elemental dragons, so these are neutral monsters on the map, if your team kills that neutral monster, you get a certain reward for it, it makes your team stronger in a certain way, so there are four different types of dragons. If you kill this one type, it'll make your team do more damage. If you kill another type, it'll make

you move faster. Things like that. When you've got very different types of benefits you can gain, there's a perception of which ones are more desirable than others, which one actually makes a difference. Right away, when these four came out, there was an idea that these two are useful and these two are not useful. If that one if that one is the one that comes up because

they they come up at a random when one is about to spawn or appear on the map it you don't know which one is going to be next until you've killed that one and then it shows you oh the next one will be this one in five minutes so you know if it's going to be a cloud drake or an ocean drake i ignore those they are they are invaluable but if it's going to be a fire drake or a mountain drake those ones are really great so do those. They aren't valuable. But if it's going to be a fire drake or a mountain drake, those ones are really great. So do those. So that was the perception. And of course, they tweaked the strength of them over time. So you're always kind of reevaluating which ones are

worth fighting for and which ones let the other team have it. We'll do something else. We'll gain something elsewhere. And so there was, yeah, I've worked on some analysis for that in different times and different iterations. But there was always this perception specifically that the mountain drake was, you know, if not the most powerful, it was always seen as the second most.

And I've done a few different projects over the past couple of years that have showed pretty consistently that it's not as valuable as it's perceived to be and that one of the other types of drakes or dragons, it's just an alternate name for it, that it was a lot more valuable than it was seen, that a lot of its power was kind of hidden beneath the surface. So to the extent that it actually changed the minds of people in the pro space, you know, coaches and players and so on, you know, I've seen some evidence of that.

People like to play their cards close to the chest when they can. So if you've actually changed their mind, they're not going to go out on social media and say, oh, I saw this article. I'm going to start changing on social media and say oh i saw this article i'm going to start changing the way i coach right so it's a little hard to say how much influence it's actually had but i but i've seen some evidence that that the attitudes did shift as those narratives came out a couple of broadcasters kind of highlighting it or talking about it directly or indirectly so there are definitely opportunities for that and there's there's there's been some

some traction for sure so you alluded alluded to the patches, the updates. That's a hallmark of esports, of course, that kind of complicates things from an analytical perspective. And all sports are changing to some degree all the time, of course. But in baseball, if a rule changes, it's huge news and it takes years. It's huge news and it takes years. And in esports, it's just a regular feature that certain classes, characters, heroes get nerfed or buffed. They get weakened or strengthened and the beta changes the kind of conditions that are prevailing, the tactics that are favored at that time. So how much harder does that make the job of you and people like you? Oh, it makes things incredibly harder

this is one of those pieces of context that i that i mentioned before about you know comparing two data points they're not the same because in league of legends you get a game patch roughly every two weeks uh in counter-strike or dota it's going to come maybe every every two months is a you know is a substantial change even at a two-month time frame, that's pretty quick. And you have to decide, kind of, you know, can I even use these two data points from different game versions in the same model or not? And you have to kind of draw your own cutoffs on that, which obviously has huge effects on sample size.

So depending on the type of analysis you're doing, that may matter, it may not matter. If you want to go beyond the pro matches, because obviously there are a lot of people playing this game online not professionally and so you could potentially go to the top levels of general online play and say well let's pull some data out of that and use that to build our models but then you have to decide whether the style of play is so different that maybe that doesn't work either you know pickup basketball should never be used as a data set

to evaluate pro basketball. And that's kind of the question we have to face there. So yeah, it makes it a lot harder to evaluate a lot of statistical things because if you had a character become much stronger overnight, but then you use the data from three weeks ago to say how strong that character is, you could get yourself in a lot of trouble. So it's just one of those factors that we're kind of used to and that we kind

of build whatever systems we can to handle it. In certain cases, we just have to say, we understand that this is adding uncertainty to our model, but we have to do it anyways. And you just kind of move forward with that. You bring in more, you kind of triangulate your analysis more rather than allowing the numbers to speak for themselves as much as you might like. So it's just it's just a factor of the nature of what esports is and as those updates roll out i mean the nice thing at least is that you know what changed you

get patch notes and you can see how attributes shifted whereas in baseball the the ball changes and no one seems to know when or why or how, but at least you get that certainty, although it's still hard to anticipate the effects and the ramifications sometimes. And some of those is that certain strategies become dominant and ascendant, and sometimes that's good for the game and spectator-friendly, and other times it's not. So from an aesthetic perspective, have there been any times where, I guess, analytics played a role in that, in determining the dominant play styles at the time in a way that either made the game more entertaining or less entertaining?

Yeah, I think it's hard to draw a direct line between the influence of analytics on that and the actual way the game was played. But I will say that probably one of the most important or most common uses of analytics in esports is trying to identify the best style to play at any given time and getting to that ideal style faster than your opponents do. When you have a game that changes in a small way every two weeks and changes in a major way every six months, the ability to react to those changes and understand them is incredibly important. You have to be flexible. You have to be versatile.

You know, if you were a basketball team that shoots a ton of three-pointers and you build your roster around that and three weeks later, they move the three-point line out by a foot, you're in big trouble. But if you were a team that is, you that is pretty good at shooting three-pointers but also really good in the key, then you're in a much better position to, oh, if the game changes, we'll be able to shift ourselves and maybe give a little more play time to a different part of our roster,

whatever it's going to be. So that's part of what goes into building esports rosters is players who can play multiple styles well, coaches and analysts who can understand you know hey we used to play this character a lot but that character's weaker now what's another character that's maybe that does a similar thing and will do better or you know or a character that is something else entirely that's gonna be much stronger now and so yeah that ability to kind of read and react understand the the patch notes and understand where is the games's kind of, we call it the metagame or just the meta, you know, what is strong in this current version of the game.

And if you can get to that one week faster than everybody else, you can win the games that you wouldn't have won otherwise. And how much of a role do stats play in scouting and preparing for specific opponents and identifying opponent tendencies? So scouting opponents, I think they play, stats play a decent role there. I think just given the volume of games being played, there's still a lot more importance on watching video than there is on the stats. But typically the stats will come in to either to guide your video review. You say, oh, they're really strong in this area and they look weak in this area. Why is that? And then you go and you watch the video from that lens.

That's often where the stats will come in. Or going into international events, it's a really big deal too because there are only a couple of big international events per year, depending on the esport you're in you know if you're playing teams with that you don't normally play against in your own domestic league then there's a lot more importance on how do we quickly kind of download an insight a set of insights on this team because we haven't been watching them all year on it uh in a game like counter-strike or dota 2 where it's built around an open tournament circuit where your teams are you know every couple months traveling somewhere else in the world for one of the many international events then it's different that you're constantly scouting different sets of opponents and the analysts tend to turn that direction more often so it depends on kind of the competitive structure you know of what your esport tends to do and is

there a role for for analytics also in pairing players and constructing rosters, putting teams together and trying to project that this person's skills will mesh well with another's? Yeah, I think there's a huge opportunity for analytics to play a much bigger role in talent scouting and in roster creation. I think there are some teams that I know of that apply a certain layer of analytics to their roster creation. I think there are some teams that I know of that apply a certain layer of analytics to their roster creation. And there are some teams that are recognizing more and more that they want analytics to play a bigger role. I'd say especially on the side of scouting upcoming talent, when it comes to kind of constructing your starting roster, it's still important. But what's more important is kind of identifying the next really good player a little bit earlier than anybody else does. And I think the opportunity for analytics to play a big role there is pretty well understood,

but the method of applying it hasn't really been hit on. If anybody is further ahead of it than I think they are, they're doing a good job of covering it up, because I tend to talk to a lot of people and kind of know what's being worked on and what isn't. But yeah, I think there's a lot that can be done to kind of know what's being worked on and what isn't. But yeah, I think there's a lot that can be done to kind of, because there's so much public data on what the general player base is doing. You know, there are data sets out there that if you know how to get the value out of them, you can find these players and at least, you know, put them on a list for a tryout

because, you know, things like that, you need to know who this person is and not just how they perform in the game, but who they are out of the game, how well can they communicate, how well do they take coaching, things like that. But to get them into your tryout before they get, A, you don't necessarily have a history of outmoded thinking that has to be overcome. Everything is sort of new and in flux. And, B, you have a young tech-savvy audience that, in theory at least, it seems like should be more receptive to some of these principles and ideas. And then C, of course, everything is digitized. And so when you talk about, well, can we track players and chart players' motions in other sports, they have to wear things or you have to have cameras. And in esports, at least in theory, you could document all of that just in the game. So it seems like there's a lot of potential there. So it's a pretty broad question, but how do you expect all of this to evolve? What are the next frontiers and what kind of implications could it have for esports as a whole as this analysis gets more sophisticated? Yeah, I think there's a huge opportunity for analytics to play a bigger and bigger role

in esports in the way teams scout their upcoming talent, in the way they prepare for matches, in the way they train and coach their current rosters. I think there's a pretty high level of awareness that analytics are and should be important in esports. that analytics are and should be important in esports what is still you know kind of coming or on its way is real proof and real solid evidence of wins that analytics have have achieved for their teams you know having that player you know i think if you know basically a money ball moment where somebody can come out with some hard evidence that says look here is a list of players that we picked up that nobody else is picking up because we applied analytics and here they are and we went out and

you know we drastically overperformed expectations so there it's it's maybe a little unfair to look for kind of that that huge moment or that that key turning point because it's analytics are kind of getting baked in from the start to some extent so you're going to have a more gradual kind of ascendancy of of analytics being a little bit useful and then kind of you know a little more useful and eventually they're maybe they're very useful but there was never this big inflection point so it's it's maybe not fair to kind of look for that inflection point if the nature of the circumstances doesn't really allow for it but but i think there's there's definitely a lot of attempts and efforts being made to kind of be the ones making those breakthroughs i think what's what's really lacking right now is not something like data access what it's we're not lacking kind of the

will of the of the teams to adopt it there are some kind of barriers among coaches you know more than you might expect given like you said kind of the demographics of who the coaches are. And they should be more tech savvy or more willing to adopt analytics. And there's still a little bit more resistance there than you might think. But what's really lacking, I think, is an incentivized and like a group of kind of highly qualified individuals who can make those breakthroughs. kind of highly qualified individuals who can make those breakthroughs. There are definitely people who have come through in the space and demonstrated that they have good ideas, that they have the technical skills to execute those ideas. Look, we've done something really cool in the analytics space.

And then they end up getting hired by a team or something like that. There have been a few of those. But more often, you see people coming up with one piece of that puzzle one piece of that skill set look i built something really cool technically but it doesn't really have the analytical value that you'd hope for look i've done i have this really cool idea that would have a ton of analytical value but i have no idea how to build it and so it doesn't really go anywhere like you haven't really had the kind of the superstar analysts can come about and that's partly because of you know just speculation but i think that's partly because of, you know, just speculation,

but I think that's probably because the industry still being so young, there hasn't been a lot of opportunity for people to be financially incentivized to come through and get paid, you know, a fair salary to do that work. People who are doing it as a hobby, you know, are more likely to be very much on the younger end of kind of the spectrum. Just e-sports as a whole is much younger than traditional sports, including kind of the fan base or the hobbyist base. And so you have, you know, a lot of 17, 16 year old kids doing this when it might be a 26, 27 year old kind of baseball fan who has a professional background and technical experience and does that in baseball.

experience and does that in baseball. And it's much easier for that person to come out with, you know, mature, highly qualified work than it is for the, you know, for the teenager who's still figuring it out. And maybe the level of work is even the same between the two, but one of them is going to carry more weight than the other because they have a resume behind it or something like that. So there's, I think there's still a lot of time that needs to happen. There's a lot of ability to pay people with those skill sets at a fair rate. And it's just going to catch up as the industry kind of matures. Yeah. Yeah. And it'll be interesting to see how that changes compensation structures and salaries as you can kind of quantify individual players' performances,

for instance. And obviously a lot of this is making its way onto broadcast too. And so it's a sort of fan facing, I guess. My last question is, I don't know whether there's been much research into this or whether there's enough history to tell, but is there any sense of what an esports aging curve looks like? Because of course, it's been historically very much a young person's sport even more than most, and people drift away from the game for various reasons at the professional

level, but part of it is perhaps just reaction time and quick Twitch reflexes. But then you do have some examples of more veteran players who can compensate with experience and savvy. So is there any sense of what a typical esports career trajectory might look like in the future, at least when it's a viable career for more people across the board? Yeah, I think what we're seeing in terms of the players kind of aging out is we definitely aren't mature enough in esports to have the same kind of data set that a traditional sport would have where, hey, can a baseball player keep playing into their their 40s can a hockey player play in their late 30s or into their 40s can a basketball player go whatever like we have a lot more opportunity you know to to evaluate that and we also have a lot more kind of data points to ground to in terms of the physical influence of

aging in those things in esports if you really step back and think about it the physical influence of aging in those things. In esports, if you really step back and think about it, the physical factors should play much less of a role. So the natural kind of aging process, reaction speed should not be slowing down into your late 20s. Not really. My understanding of it at least, maybe it is, but we haven't had the opportunity to kind of measure it in an esports context yet, and we probably won't for a while. It's really more kind of social aspects that have been aging players out.

You know, the 23-year-old who's been playing for three years and is now losing his spot because there's an 18-year-old who's better. You know, he didn't age out of the league. It's just that the overall player base is getting bigger, and suddenly there are people who are better than he is. It might not be that he got any worse. It's just now there are more rookies to choose from. It might be that in some cases, the person who has dedicated every waking hour of their life

from the time they were 16 until they went pro at 17, which is in most games, that's the way you're allowed to go pro. And now they're 21 and they're just tired and they want to go out and live their life and they lose motivation because of that. And so they haven't aged out of it physically. They've aged out of it socially maybe. And because the organizations themselves are relatively immature from the perspective of, you know, knowing how to guide these players to live their lives and have work-life balance and still be effective, you know, all of these things have prevented organizations over the past several years from doing as well as they could to keep these players at peak condition and keep them fully engaged and able to sustain their careers. And we're seeing over the past couple of years a lot more of that as the infrastructure comes around, that the teams know how to keep these players playing professionally and being valuable into their mid and late 20s. And I think there's no reason that we wouldn't see that continue, you know, as we kind of

separate living and working environments more, which is something that from the traditional sports world, you might be pretty surprised by. But a lot of teams up to a year or two ago would all live and play in the same house 24 hours a day. And so things like that, you know, add to that kind of social burden of being a professional gamer. And as those kind of barriers start to get removed, I think that adds a lot to the lifespan of a pro player. So it's not really a physical thing. It's much more structural.

So I think we're going to need at least another probably 10 years to see that stabilize more. And then start to see, you know, let's find a player with a 10-year career or a 15-year career and see what happened to them over the trajectory of it and get a certain volume of those. Right. Okay. Well, this is all fascinating to me and I will be interested to see how it all develops and you can keep track of the industry by following Tim on Twitter at Tim7Husen. You can also check out Esports One, which will be launching next month, I believe. And Tim, I really appreciate your time. Thank you. Yeah, absolutely. This is a lot of fun.

And I'm always happy to talk about eSports and kind of share what's happening in our emerging industry. Yes. All right. We'll take a quick break and we'll be back in just a moment to talk volleyball with Giuseppe Vinci. All right, so it is time to talk volleyball, and we are joined now by Giuseppe Vinci. He has been working on volleyball analytics for over a decade now, since he worked as the technical coordinator for the Italian men's national team,

which was training for the Olympics. He later worked for the U.S. women's National Team for a period of years and other teams too. And then he founded the website Volleymetrics, which was sort of the leading volleyball analysis site. Now he continues that work for Huddle. Hey, Giuseppe. Hello. How are you? Doing well. Thanks for coming on. So give us a sense of where volleyball ranks on the spectrum of ease of analysis if say a one is a sport that is completely impenetrable and impossible to analyze and 10 is say baseball which lends itself very readily to sabermetric style analysis where would you put volleyball on that scale i will say

a good eight just because of the structure of it the The ball just goes from side to side. You've got same players on the court for a rally. You've got the rally structure, the game structure. And it's pretty fun to analyze it and not too hard that you have to go crazy to do some good analysis. to go crazy to do some good analysis. So can you give us a history of volleyball analysis? I don't know if you started it or if it existed in some form before you, but how did it take off? How has it been embraced? And what have some of the major breakthroughs been? Yeah, absolutely.

So I sometimes make this statement that sounds a little odd and it's hard to confirm it nor deny it, but I strongly believe that Vol is probably one of the sports that started with analytics the earliest, just because it lends itself

to analysis so easily, right? But for what I know and what I've seen and I've been in the sport for such a long time, especially focusing on analytics and video and so forth. As early as in the 70s, there were teams having statistics on the bench or on the sideline for international competitions. Then going into the 80s, numbers got bigger and bigger.

into the 80s, numbers got bigger and bigger. And one of the most famous U.S. volleyball coaches of that era, Doug Beale, who ended up being also the CEO of USA Volleyball for a number of years, was the first coach that really broke through with the numbers, right? And he had a few people working with him that really helped him in that. And he has become a legendary coach also because of that. And off of that kind of style of coaching with Doug Beal and Yusef Al was starting to win a lot internationally and winning two Olympic gold medals, 84 and 88, back to back and all of that. A lot of coaches worldwide started to pick it up. Italy, a young former player, coach, and kind of big into computers back then, started a company in the mid-80s that has grown a lot since then, but that all teams started to use. And so in the 90s, starting in the 90s, most of the top teams had stats and video on the bench. I can recall as early as 2006 having even live video on the bench for the coaches that they could review it. So you see how the history goes pretty way back for Vollable and analytics and all that comes with it. So that's why I often say,

hey, Vollable was definitely one of the first ones to really go into it. And what were some of the early insights or what sort of advantages did the early adopters derive? Yeah. So the early approach was simply grading skills on a 1 to 5 or 1 to 4 scale from bad to good and just weight them based on how often they happen, like frequency and all of that. But over the years, just because there has been so much time, it got much more complex. The early concepts were about quality of the reception. So where does the ball go after a serve?

So that basically how is the setter going to play? What plays is he going to run? What decision is he going to make based on the quality of that first touch by a team? So those were definitely early on metrics as well as offense metrics. So if I take my kill, so the points that I gain attacking and the errors I make, okay, am I netting positive? Am I not? Am I making more damage than anything in the way I'm attacking? Am I being too aggressive or not?

And so those were definitely the very early metrics that were available in Volvo. And they evolved since then quite a bit. that were available in Balboa, and they evolved since then quite a bit. And have there been some misconceptions that have been overturned by this analysis, things that people used to think or teach or coach that turned out not really to be backed up by the numbers? Yeah, I will say, well, first, over the years, the rules changed quite a bit over the last 20 years. So those early misconceptions or findings, they're not necessarily true to this day because of the rules changes.

But definitely, the early misconceptions were when you called score points just when serving, that serve, yes, was important, but because serving tough, what we say in volleyball, it was as important as today, just for the change of rules. So back then, the side-out percentages, yes, were important, but point scoring was way more important in their point of view. Today is the exact, like I would say, almost the opposite,

where side- out is just uh if you can sit out you can win period back then in the days was a slightly different because of the rules right but i will say that there have been findings that completely uh change the game or revolutionize the coaching is more there have been lots of findings that have allowed coaches to start focusing on the right things. One of the big changes in the last, I would say, 10 years has been going away to what I mentioned early on with this one to like zero to four grading scale, but weighting them a bit better.

Because if you just look at the game, yes, perfect and positive, there's some in between a skill executed perfectly or in a good way, there's maybe a 3-4% side out. So people are starting to stop weighting the skill execution just as one, two, three, four, but they realize that threes and fours are way more value than a one and a two. And so they are starting weighting them appropriately by moving to percentages and more appropriate metrics like efficiencies and things along those lines. Can you explain the side out in a little more detail for people who may not follow volleyball closely? Yeah. So just like in tennis, there's a side out percentage where whenever you are not serving,

so you're receiving the ball, that's what we say in volleyball, whether you win the point or not, and not necessarily on the first touch. So side out is if you win the point at all when you are receiving the ball from the server. And that's the side out percentage, which is the highest, the metric that is the highest, has the highest correlation to whether you're going to win or lose a match, especially with today's rules. I see. A metric that has gained a lot of traction in the last 15 years is what in English we call the first ball side out. And that's how often can you side out on the very first reception,

the very first possession you have after a serve. And that has gained more and more importance just because of how physical the game has become in recent years and how much lower is your chance to have another chance at attacking the ball if you allow the opponents to actually get a chance at attacking and scoring. I see. So has this changed the sport dramatically from a spectator perspective, aesthetically speaking? Because in some sports, you start to see these principles become pervasive, and it makes the game more entertaining to watch. And sometimes the optimal way to play is less visually interesting. So has one of those been the case in volleyball?

That's a great question. And that's probably one of the biggest topics being discussed in the volleyball world, especially during men's volleyball season. So with this change of rules, the problem with the old rules where you could score only when you were serving is that these matches will start being three hours, three and a half hours sometimes. So very long, not televisable. So around the year 2000, the rules were changed to where every ball is a point for either of the teams, right? You can score any time.

And with that, the time, like the matches got shorter, there were more points scored, so it got more exciting. The problem is that soon after, after coaches through looking at the stats and studying the game this new game realized that okay i score a point every time i need to serve really tough and it's okay to miss a lot of serves and that's something that spectators have an hard time understanding like you will watch a men's volleyball game, especially that is more physical. You'll see a lot of miss serves, like maybe for every point scored directly. So with an ace, you see two miss serves.

And it's not rare to see two, three miss serves in a row. So the fans don't really like that. And if we were to be able to explain to them the numbers, they'll understand why that is necessary if you want to win. So I will say that the rules kind of made the game better because now there are more rallies, more points scored. But at the same time, there's this challenge with the serves where coaches now got smarter, know the numbers and missing serves is okay from a technical perspective, from a strategy perspective, but the fans not very often understand that.

Uh-huh. That sounds somewhat similar to baseball in some ways. So when you started working with a team, or maybe when even earlier analysts did, was there any kind of clash or resistance among the players to these ideas, or was it fairly seamless i will say early on players would often not be given statistics or numbers because there was this uh this uh this belief that okay they'll they'll not understand it they'll over uh overestimate

like how important this is they'll not react well to it. At this point, you will see a lot of athletes actually consuming a lot of statistics and wanting to know more and more and more, especially with location data coming along. So that's actually, I think, a pretty nice thing about Volvo. Again, going back to our initial topic of kind of the history of the analytics in the sport, this generation of athletes has been given stats since they were like teenagers, like growing players. So there's not much resistance to it. There's actually more acceptance.

And also on the coaching side, there's a lot of it. Funny enough, if you ever ended up at a coaching volleyball clinic or course, yes, there's a lot of tactics and technique courses and lessons. But I will say that more often than not, a good 20% of those conventions, coaching conventions or clinics, talk about data and video just because Volvo is so tied to it given'd imagine that the level of engagement with these ideas varies and maybe some teams can afford to have certain tools or coaches or analysts and others can't. And I know that you work on some tools that are pretty widely available, but how much does this vary based on where the game is being played? Well, naturally, it varies a lot, even just because of budget and people that you have available.

So it's definitely at the elite level. And for elite level, let's say Division I collegiate and any professional league around the world, I think you can hardly find a team that doesn't have a performance analyst. In some cases, that person might be part-time, but it's present on all the matches, provides live stats to the bench during the match

and so forth. If you go down to Division II, Division III collegiate, or even club volleyball or junior volleyball, you more rarely see somebody doing the work of gathering stats but since it's so since it's so widespread you will actually often see either the coach the head coach with an iPad so with mobile devices it became so easy keeping stats but more often than stats you'll see teams at least filming the video to go back and get some numbers off of it. So you will be surprised by how much it's used also at the lower level. And one of the reasons is that we're not talking about soccer.

Let's use the American word for it. We're not talking about soccer where it's very hard to film a game but in volleyball you just set up a camera behind the service line you film the game and and you can go back at it and you don't need an operator so that that's also it kind of uh takes down a barrier of entry that is pretty high if you take sports that are played outdoors and on big fields rather than a court and a steady camera. And how much has this availability of statistics and data and video affected scouting and preparing for specific opponents and figuring out opponent tendencies and that sort of thing? Yeah, definitely a lot. There is a trend. So this is where the USA probably differs a lot

from the rest of the world to a certain extent, where outside of the US, the approach was very much on scouting first and then on performance. While in the US, there's always been more on performance first of my team and performance metrics and then scouting the opponents.

But the availability definitely impacted the scouting area quite a lot. That's something that happens more at the highest level where the margins become so thin that, and you have athletes that can actually execute a scouting report. and you have athletes that can actually execute a scouting report. And at one point, those two points can really make the difference, all the difference in the world, much less at the lower levels. But the evolution of scouting has been actually pretty interesting and pretty cool to see where you started on years ago just with maybe looking at strategy based on the quality of the reception or of the dig.

And today you start seeing a lot of teams that start using positional data. And they start looking more and more and more at situations rather than just what happens in this fixed framework that we have always looked at. So there's much more creativity, much more different approaches, thanks to the advent of just online sources where you can get a lot of video, and also the advent of much easier-to-use technology for dashboards and all things along those lines. And there's plenty of teams that are taking advantage of that and plenty of sports. And that's not different and valuable. And you've worked with high-level teams, men's teams, women's teams, indoor teams,

beach teams. Do all of the same principles apply kind of across the board? Or are there certain tactics or techniques that are more applicable to one or the other oh yeah so men's and women's indoor volleyball are definitely very different nowadays because of the rally points system which we were talking about before now serving on the men's side has gained so much importance. And given that the net, the height of the net hasn't really changed in many years, but athletes now are much, much more trained, well-trained.

Coaches get smart and they understand having a 6'10 player is better than having a 6'8 player because I can just be more physical at the net or from the service line. So the game on the men's side has become very much a serving game where if you cannot serve effectively and serve very tough, like serving at 70 plus miles per hour, you're not going to be competitive at the high level. On the women's side, with the net being smaller, but the players still being better athletes, maybe than in the past, just because they can get selected earlier and so forth, you have less of the jump serving and these very tough, hard serves,

but more of kind of float serves where the ball kind of moves and it's more difficult to predict where it's going to go because it drops early and things like that. But you are starting to see a little switch on the women's side where there are big physical athletes that can go and serve tough and be very aggressive from the service line. athletes that can go and serve tough and be very aggressive from the service line. And that's causing some trouble to some teams internationally that if you cannot handle that,

you're in a very tough spot to win very important competitions. And the current top teams in the world, two of them are Serbia and Italy. On the women's side, they have some of those athletes that can go back to the service line and be very effective. And there are very few athletes like that today in the world on the women's side. While on the men's side, there's many. The beach side is definitely a different game,

totally different approach. The scouting is way, way different because teams play much differently based on who they have on the other side of the net. So you cannot just scout what do they do against others. You have to be really focused on what do they do against me when we already played before or against teams that are very much like me. That's how the scouting works.

On the volleyball indoor side, it's more about how do they play or against teams that are very much like me. That's how the scouting works. On the volleyball indoor side, it's more about how do they play because the teams have a system, given that there are six players on the court and not just two on the beach volleyball side. Plus, on the beach volleyball side, you have your outdoors. So wind, rain, sun, there are so many different conditions that impact how a team might play that it's much more about reading the game, even more so than in volleyball. So, reading the game and understanding, okay, how are they going to play against me? So, those will be the big differences in between men and women indoor volleyball and then beach volleyball. I see. And how much do players go back and forth between indoor and beach? And if they do, is it sort of similar to, say, tennis with clay courts and hard courts where you have specialists at one or the other? And what would make a player excel at one or the other?

one or the other and what would make a player excel at one or the other? There have been very few athletes that have been very successful in doing both at the same time. Currently, there's none that I can think of that is doing both high-level beach volleyball and high-level indoor volleyball. Unless you are a top beach volleyball athlete, you are likely to make a better living by being on the indoor side because there are more jobs, because there's 12 to 14 players on each team's roster. So a lot of great players

that could be great on the sand, they struggle to make the switch because it's not as much of a safe job because on the beach side, on top of having less teams and needing to be very good to make a good living, you also depend on how much you win. It's like kind of tennis style where based on your finish, you get a check from the organizers or from the International Federation.

And therefore, you don't see that often happening and they're completely different games. So even great athletes indoor to make a switch to the sand, so to beach volleyball, it will take them some time to adjust, and there are cases of it. Karch Kirai, the current head coach for the U.S. women's national team, he won two gold medals indoor, and then won a gold medal on beach volleyball as well so he's probably the biggest

example but he made a career switch like he was playing the beach volleyball but then he decided okay i'm gonna focus on it this past summer canada women's won the world championship and some of their athletes were former indoor players but now they focus 100% on beach volleyball. So it's more of a switch in the career rather than just, oh, I can do both. And I think you mentioned earlier that player tracking technology is starting to enter the sport. So how pervasive is that? And how might that change things? Yeah. So the biggest challenge with tracking technology and so forth is that if you take sports like basketball and soccer, right? Historically, there have been some sports prone to statistics, but never a lot into

it. In recent years, that has changed because there's so much more data. Now all these franchises, these organizations see value in whether in picking the players that they want to trade and all of that or in simply deciding a style play and once they realized that they invested a lot of money in it and giving the giving just the size of that market they were able to invest much more money than a Volable team could. So they made a lot of progress. So on the Volable side, there has been progress, but tracking technology and positional data is something that not many teams have access to,

if not through services like what I've been working on and so forth. And that's kind of has been maybe the last three to five years. So that's the hurdle that we have to get over in Voluways. How do we become able to have that tracking data available to teams? And how do we start using it for analysis? And I know that the international Voluwe Federation is working on some ideas and some leagues are as well. We're not very far at all.

It is possible. It's just what teams are going to make what leagues more than teams. What leagues are going to make the investment to do that and to do that it needs to be connected to some fun engagement as well, not just the performance of

the teams and the athletes. That's a whole different topic, how to use that data to engage more fans, especially on a worldwide sport, global sport like volleyball. And might it change things in terms of technique or strategy, or are the ways that positioning is charted now and video and all the other ways that things are recorded doing a decent job of

answering those questions? Or are there things that just can't be answered without that kind of comprehensive, precise sort of information? Yeah, I'm a big believer that precise informational location will definitely change the game, probably quite a bit, even just by where do I position my players in specific situations or where do i want to attack the court being able to see the correlation between the position of the ball and the position of the players or where points are scored and where the players are is gonna tell us a lot like a comparison to baseball i can feel think of outfielders, like, you know how much of the field they can cover, right?

And in volleyball, that is a concept that is not yet really known. We don't have statistics or numbers or coordinates that tell us, okay, this defender is effective within this area of the court. effective within this area of the court so let's position it let's position this player in this spot so that he can cover or she can cover the right area of the court for this type of attacker and that is definitely going to change the game quite a bit and make us able to make decisions even more specific to athletes and i i very much look forward to it and again it's not that far it's possible i've been doing it and working on it but again the scalability of it is always the challenge which leagues are gonna really gonna think about this is gonna benefit

not just the teams but our fans because that's what drives the sports industry, right? The engagement of the fans and the communities and all that. And so, as you said, volleyball was early to analytics and using data, but are there any stats or concepts that were borrowed from or inspired by other sports or other things in volleyball that people who follow other sports would recognize, whether it's some sort of win expectancy metric or maybe trying to assign a win value to individual players? Yeah, I think in recent years, there's been a lot of approach to kind of a plus, plus

and minus approach and very much about, okay, skills importance, finding metrics that summarize in one number. Okay, this is the impact of this player, real points created. Those have been all concepts definitely borrowed by other sports and just adapted to the volleyball world. And I think they're definitely useful. Again, it's always a matter of mindset and being open. adapted to the volleyball world. I think they're definitely useful. Again, it's always a matter of mindset and being open to use different approaches, especially when being early to the game is great, right?

But also it creates this fixed mindset around, oh, we know how this is done. Why do we need to change, right? And with the advent of so much more data and technology and these metrics borrowed by other sports, I think that mindset is changing. And the more coaches, many coaches changing it is pushing the rest of the world of volleyball and beach volleyball

to have a more, a better approach at analyzing that is not within a fixed framework, but that is more specific to situations and teams and players rather than the same for everybody. And then lastly, has there been a lot of work on the sports science side when it comes to training and physical movements and injury prevention and maybe wearable technology? Yeah. So it's not widely adopted from teams worldwide, but there's a lot of wearables just even to understand workload of just number of jumps, number of max jumps, because you can imagine, involved during a practice, you are jumping a lot. And the most important thing, so we have that information. What I know some teams are working on is the actual landing. So usually with insoles in your shoes, how do you land?

Usually with insoles in your shoes, how do you land? How many Gs of force? You are putting on one leg, on the other leg, on what side of your foot in order to go and know exactly, okay, this player has this type of issue to his right knee, and we are exerting a little too much force on this knee today. And now the strength coaches are able to call it out and say, hey, this player is done for today if you don't want him to get hurt. So there's definitely some inclusion of that type of technology,

but not widespread. What is widespread is just counting how many jumps with devices, wearables, and that's becoming more and more. But initial studies from the last just few years show that there isn't almost any correlation at all between how high you jump and how tired you are, kind of, and how well you are attacking, which is a very important skill. But it's more about, hey, if you can jump

at least your average, then the rest is technique and you can create value. There hasn't been anything showing that it's highly correlated, like how well you attack and you score points with how high you jump and match consistently, which is counterintuitive, but that's what research says so far.

All right. Well, you can find Giuseppe Vinci on Twitter at Mr. Juice, Mr. G-I-U-S-E, and you can check out what he is working on on the Volumetrics page at huddle, huddle.com. Thank you very much for your time, Giuseppe. Thank you very much. All right. That willuddle.com. Thank you very much for your time, Giuseppe. Thank you very much. All right, that will do it for today. Thank you for listening. We have two more of these episodes to go,

and the next one will be on NASCAR and cycling, but we're going to take a quick detour and bring you a non-multisport sabermetrics exchange episode next. There'll be a little year-in-review episode with me and Megan Sam, so look for that before New Year's, and then we will resume and finish this series by the end of the week. You can support Effectively Wild on Patreon by going to patreon.com slash effectively wild. The following five listeners have already signed up and pledged some small monthly amount to help keep the podcast going and get themselves access to some perks.

Chad Jobin, Head Zookeeper, Rob Haberkamp,, Stuart Verhulst, and Zachary Levine. Thanks to all of you. You can join our Facebook group at facebook.com slash groups slash Effectively Wild. You can rate, review, and subscribe to Effectively Wild on iTunes and other podcast platforms. Keep your questions and comments coming for me and Meg and Sam via email at podcast at fangraphs.com or via the Patreon messaging system if you are a supporter. Thanks to Dylan Higgins for his editing assistance. We will be back to talk to you very soon. Good fortune and luck

You won't catch the rally boys Good fortune if you can Hey, hey, the rally boys