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I've done some work in sports analytics for an NBA team, and while I think there is some value there, I also think that most of the low hanging fruit has alread

I've done some work in sports analytics for an NBA team, and while I think there is some value there, I also think that most of the low hanging fruit has already been plucked at this point. People have been pointing to the same examples of "Moneyball" in baseball, or 3-pointers in the NBA, for at least a decade at this point. The reality is that for a lot of sports, they are complex enough systems that even if insights are generated, they are complicated enough that its hard to action on them. Look at the animation of the predicted player movement on the field. Its pretty, but what can a manager or player reasonably do with that information? Not a lot. Right now, it seems like most analytics in sports are solutions looking for a problem, which is understandable so many of us in ML and statistics are sports fans. But ultimately, I don't see much impact being made, certainly not on a Moneyball-esque scale. There are still areas where analytics will continue to be important in sports and player assessment/team composition will always heavily rely on it. However the next steps for sports analytics in my mind are things that go on behind the scenes of the sport: personal training routines for players, managing wear and tear and predicting injuries, etc. Also, the leagues themselves have lots of room to improve their products with analytics. Many complaints have been raised (which I agree with) about how boring modern basketball/baseball has become due to analytics causing teams to converge on strategies that maximize efficiency but are also quite boring. It isn't on the players/coaches to try something more exciting at the expense of winning. It's the league's job to make the product interesting. Analytics can be part of the solution, like determining that by pushing the 3-point line out several feet, the value of the shot is brought in line with other options that simply aren't viable right now.

stanford_labrat 5y ago
If you don't mind my asking, what kinds of models/statistics/analytics did you find most useful? I'm curious because I've done some analytical work for professional League of Legends teams in the past, but have recently been unenthusiastic about pursuing this anymore because my creativity has dried up about what is "useful". I also share your opinion that most of the low hanging fruit has already been done. Obviously the games are different, but perhaps there are some common themes or generalized structures that can help frame what questions to ask?
- thom 5y ago
  There are lots of commonalities between invasion sports - famously one of Pep Guardiola’s coaches at Manchester City is a former water polo player, for example. I assume much of what’s been learned there (minus the ball) applies to games like League of Legends. So, from what little I know of LoL, you’re simultaneously trying to attack the enemy base and protect your own. Presumably there are interesting second order things to study here - how can you get close to the base, how can you destroy it quicker than the opponent destroys yours etc. I assume if there are any epiphanies along the lines of “shoot threes” in basketball or “shoot closer to goal” in football, they’re probably well trodden. Much of the game is presumably controlling the space between, trying to create overloads against opposition players, and not be overloaded yourself. The best teams in football are experts at using space, and at manipulating their opponent’s shape to create gaps or opportunities for ambushes. So an interesting thing to study would be teams tendencies to overextend - what sort of structures appear to offer them opportunities, but actually keep your players close enough together to pounce on them. The ghosting work mentioned in the article would be interesting here, as would work on pitch control: https://www.researchgate.net/profile/William-Spearman/publication/327139841_Beyond_Expected_Goals/links/5b7c3023a6fdcc5f8b5932f7/Beyond-Expected-Goals.pdf?origin=publication_detail https://www.researchgate.net/profile/William-Spearman/public...
  - colinmhayes 5y ago
    Lol and soccer are extremely different. It's much more analogous to basketball or fighting sports. You want to get more jabs in than your opponent so that when you go in for the kill you have more strength/stamina left than they do. The question is more along the lines of how do we cause the most damage? Preventing yourself from being damaged isn't as important because it happens naturally if you control the tempo.
- amch 5y ago
  Would love to hear more about your analytics work in League of Legends. Is it fair to say that the "useful" numbers/stats tend to be low-hanging fruit and not particularly interesting / requiring of creativity? I imagine with a fairly dynamic meta-game, you're limited by the data available to you, which can quickly become obsolete. I'd also imagine any data you can crunch from solo queue games is of limited use in competitive play.
dillondoyle 5y ago
Have you looked at improving individual performance/tweaking training plans? Versus more game theory of group games? Or have any research suggestions to read for say a competitive college athlete performance level? I'd be super interested in that and seems like the more data we can collect from watches etc the more potential variables to model or train ML suggestions on? lots of companies spending big money on this market too. Apple, pton, smart watches. The documentary Breaking2 from nike about the marathon is really good, shows some advanced individual measurements that wouldn't be available to almost everyone (lactic acid blood draws while running..). in my sport climbing finding new ways to push lactic acid tolerance or better improving physiology through BFR or whatever training would be really valuable and maybe push the sport even further.
lprubin 5y ago
I completely agree that modern analytics has made baseball more boring. More strikeouts, more pitching changes, less stolen bases, more walks, and more pitches per at bat all make the game more boring for sure. But basketball seems the opposite to me. Analytics has shown that except in a few superstar cases, back to the basket isolation mid range jumpers while the rest of the team stands around and does nothing (IMO the most boring play in basektball), is a terrible play call. Analytics has shown that the pick and roll is a fantastic play in terms of points expectancy and I find it to be a really enjoyable play to watch because it often results in dunks and nifty passes or acrobatic layup attempts. Analytics has shown that faster pace, shooting earlier in the shot clock, and getting out in transition more often to be hugely valuable strategies, all strategies that I think are more enjoyable to watch. The perceived value of 3-pointer shooting has caused offenses to spread the floor creating more driving lines and making it harder for defenses to pack the paint which has created more offense which I think most people enjoy seeing. I'd love to hear what you think analytics has done to make modern basketball more boring.
- edmundsauto 5y ago
  It's interesting in baseball (I agree the game is less exciting!) because the new way of playing is optimized for winning games, not for fan enjoyment. To me, this says that baseball's structural balance between pitchers and hitters is fundamentally unstable, with both sides optimizing to increase variance. Analytics only uncovered these structural issues for teams to exploit. Personally, I think if the strikeout rate were tamed back to 1990s levels, the game would balance.
  - lprubin 5y ago
    I believe MLB management agrees with you because they're considering testing moving the mound back a foot which would give hitters more time to react and should help balance the strikeout rate.
    - arrosenberg 5y ago
      They also keep messing with the damn ball, which this year has the league hitting about .230 because more fly balls are falling inside the park.
hogFeast 5y ago
Football isn't like American sports. Arsenal bought an analytics company (more than one iirc), and did absolutely nothing with it. Man City are only just getting into the area. Liverpool have been doing it for a while (with fairly mixed success). There is almost no interest from management in applying analytics. Additionally, sports are not equal. Baseball suits analytics. NBA is harder. And football is harder than the NBA (by a significant degree). All of this is measurable btw. One very simple point relating to this: there is no equilibrium strategy in football, it varies based on the opposition, teams that are bottom of the league can (under certain conditions) prevent a huge obstacle to the best team in the league. Either way, it doesn't matter because almost no football teams are applying this knowledge (there are a few exceptions, some of the top teams are run by professional gamblers and they have been printing money from analytics for decades...so, in those cases, it is being applied).
- hervature 5y ago
  The EPL has no salary cap, the obvious winning strategy is to buy the best players. Analytics in sports usually start with finding undervalued players which is the game that weaker teams are playing, not the teams you mentioned. Also, analytics in soccer has been around for a long time: https://fivethirtyeight.com/features/how-one-mans-bad-math-helped-ruin-decades-of-english-soccer/ https://fivethirtyeight.com/features/how-one-mans-bad-math-h...
  - hogFeast 5y ago
    It isn't. Again, football is not like American Football or the NBA where, for different reasons, you can buy one player and win it all. It is very possible to produce a winning team (although not one that will likely win the league) composed only of specialist players: Fulham (got to the Europa League final), Bolton, Stoke (first-time with Pulis), Leicester...these are just teams in the recent past in one league. The vast majority of teams that try to "buy the best players" don't succeed (usually there is only one or two players currently in the game who can win games by themselves, there is a substantially larger group of players who cost essentially the same and don't perform at all) because they pay too much/buy players randomly who don't fit their system/overestimate skill. I would look more closely at what I said. I did not say that analytics hasn't been around for a long time (for some reason, the article you link fails to mention that Reep actually worked for clubs, and found some degree of success...but there is no Nash strategy, Bolton got to the Europa League playing long ball football...it is a fine strategy suggesting that it doesn't work on average means nothing because everything is conditional in football). Btw, your model of salary cap=pay most for players is obviously flawed if you consider that the non-existence of a salary cap does not occur in a vacuum. Player salaries/transfer fees are a dynamic competition so the lack of salary cap means that most players are overpriced (because clubs are inherently overoptimistic...there are clubs who make money just by developing players and selling them to bigger clubs). The actual implication of salary caps (in combination with FFP) is that success is correlated to your ability to generate revenue. It isn't that the top clubs can pay more for players, it is that they can "lose" more overpaying than anyone else. As an example, Manchester United have rock-solid sponsorship revenue (iirc, they even have a "tractor partner"...they sponsors for literally everything) so they can overpay for almost every player, lose tons of money doing so, and end up doing okay. If a club lower down the table made deals as bad, they would get relegated. No salary caps just mean the rich clubs get richer.
hervature 5y ago
What I want to see happen is for a league to experiment with data driven rules that encode how the game should be played. For example, a dynamically altered 3-point value that ensures the expected value for different plays are equal. For example, some days, the 3-point will be worth 2.7 and other days 3.1 depending on league data. It would be awesome to see multiple strategies be optimal and reward teams that are good at multiple strategies as they can adjust their play style for whatever the data derived rules are.

stanford_labrat 5y ago

If you don't mind my asking, what kinds of models/statistics/analytics did you find most useful? I'm curious because I've done some analytical work for professional League of Legends teams in the past, but have recently been unenthusiastic about pursuing this anymore because my creativity has dried up about what is "useful". I also share your opinion that most of the low hanging fruit has already been done. Obviously the games are different, but perhaps there are some common themes or generalized structures that can help frame what questions to ask?

thom 5y ago
There are lots of commonalities between invasion sports - famously one of Pep Guardiola’s coaches at Manchester City is a former water polo player, for example. I assume much of what’s been learned there (minus the ball) applies to games like League of Legends. So, from what little I know of LoL, you’re simultaneously trying to attack the enemy base and protect your own. Presumably there are interesting second order things to study here - how can you get close to the base, how can you destroy it quicker than the opponent destroys yours etc. I assume if there are any epiphanies along the lines of “shoot threes” in basketball or “shoot closer to goal” in football, they’re probably well trodden. Much of the game is presumably controlling the space between, trying to create overloads against opposition players, and not be overloaded yourself. The best teams in football are experts at using space, and at manipulating their opponent’s shape to create gaps or opportunities for ambushes. So an interesting thing to study would be teams tendencies to overextend - what sort of structures appear to offer them opportunities, but actually keep your players close enough together to pounce on them. The ghosting work mentioned in the article would be interesting here, as would work on pitch control: https://www.researchgate.net/profile/William-Spearman/publication/327139841_Beyond_Expected_Goals/links/5b7c3023a6fdcc5f8b5932f7/Beyond-Expected-Goals.pdf?origin=publication_detail https://www.researchgate.net/profile/William-Spearman/public...
- colinmhayes 5y ago
  Lol and soccer are extremely different. It's much more analogous to basketball or fighting sports. You want to get more jabs in than your opponent so that when you go in for the kill you have more strength/stamina left than they do. The question is more along the lines of how do we cause the most damage? Preventing yourself from being damaged isn't as important because it happens naturally if you control the tempo.
amch 5y ago
Would love to hear more about your analytics work in League of Legends. Is it fair to say that the "useful" numbers/stats tend to be low-hanging fruit and not particularly interesting / requiring of creativity? I imagine with a fairly dynamic meta-game, you're limited by the data available to you, which can quickly become obsolete. I'd also imagine any data you can crunch from solo queue games is of limited use in competitive play.

dillondoyle 5y ago

Have you looked at improving individual performance/tweaking training plans? Versus more game theory of group games? Or have any research suggestions to read for say a competitive college athlete performance level? I'd be super interested in that and seems like the more data we can collect from watches etc the more potential variables to model or train ML suggestions on? lots of companies spending big money on this market too. Apple, pton, smart watches. The documentary Breaking2 from nike about the marathon is really good, shows some advanced individual measurements that wouldn't be available to almost everyone (lactic acid blood draws while running..). in my sport climbing finding new ways to push lactic acid tolerance or better improving physiology through BFR or whatever training would be really valuable and maybe push the sport even further.

lprubin 5y ago

I completely agree that modern analytics has made baseball more boring. More strikeouts, more pitching changes, less stolen bases, more walks, and more pitches per at bat all make the game more boring for sure. But basketball seems the opposite to me. Analytics has shown that except in a few superstar cases, back to the basket isolation mid range jumpers while the rest of the team stands around and does nothing (IMO the most boring play in basektball), is a terrible play call. Analytics has shown that the pick and roll is a fantastic play in terms of points expectancy and I find it to be a really enjoyable play to watch because it often results in dunks and nifty passes or acrobatic layup attempts. Analytics has shown that faster pace, shooting earlier in the shot clock, and getting out in transition more often to be hugely valuable strategies, all strategies that I think are more enjoyable to watch. The perceived value of 3-pointer shooting has caused offenses to spread the floor creating more driving lines and making it harder for defenses to pack the paint which has created more offense which I think most people enjoy seeing. I'd love to hear what you think analytics has done to make modern basketball more boring.

edmundsauto 5y ago
It's interesting in baseball (I agree the game is less exciting!) because the new way of playing is optimized for winning games, not for fan enjoyment. To me, this says that baseball's structural balance between pitchers and hitters is fundamentally unstable, with both sides optimizing to increase variance. Analytics only uncovered these structural issues for teams to exploit. Personally, I think if the strikeout rate were tamed back to 1990s levels, the game would balance.
- lprubin 5y ago
  I believe MLB management agrees with you because they're considering testing moving the mound back a foot which would give hitters more time to react and should help balance the strikeout rate.
  - arrosenberg 5y ago
    They also keep messing with the damn ball, which this year has the league hitting about .230 because more fly balls are falling inside the park.

hogFeast 5y ago

Football isn't like American sports. Arsenal bought an analytics company (more than one iirc), and did absolutely nothing with it. Man City are only just getting into the area. Liverpool have been doing it for a while (with fairly mixed success). There is almost no interest from management in applying analytics. Additionally, sports are not equal. Baseball suits analytics. NBA is harder. And football is harder than the NBA (by a significant degree). All of this is measurable btw. One very simple point relating to this: there is no equilibrium strategy in football, it varies based on the opposition, teams that are bottom of the league can (under certain conditions) prevent a huge obstacle to the best team in the league. Either way, it doesn't matter because almost no football teams are applying this knowledge (there are a few exceptions, some of the top teams are run by professional gamblers and they have been printing money from analytics for decades...so, in those cases, it is being applied).

hervature 5y ago
The EPL has no salary cap, the obvious winning strategy is to buy the best players. Analytics in sports usually start with finding undervalued players which is the game that weaker teams are playing, not the teams you mentioned. Also, analytics in soccer has been around for a long time: https://fivethirtyeight.com/features/how-one-mans-bad-math-helped-ruin-decades-of-english-soccer/ https://fivethirtyeight.com/features/how-one-mans-bad-math-h...
- hogFeast 5y ago
  It isn't. Again, football is not like American Football or the NBA where, for different reasons, you can buy one player and win it all. It is very possible to produce a winning team (although not one that will likely win the league) composed only of specialist players: Fulham (got to the Europa League final), Bolton, Stoke (first-time with Pulis), Leicester...these are just teams in the recent past in one league. The vast majority of teams that try to "buy the best players" don't succeed (usually there is only one or two players currently in the game who can win games by themselves, there is a substantially larger group of players who cost essentially the same and don't perform at all) because they pay too much/buy players randomly who don't fit their system/overestimate skill. I would look more closely at what I said. I did not say that analytics hasn't been around for a long time (for some reason, the article you link fails to mention that Reep actually worked for clubs, and found some degree of success...but there is no Nash strategy, Bolton got to the Europa League playing long ball football...it is a fine strategy suggesting that it doesn't work on average means nothing because everything is conditional in football). Btw, your model of salary cap=pay most for players is obviously flawed if you consider that the non-existence of a salary cap does not occur in a vacuum. Player salaries/transfer fees are a dynamic competition so the lack of salary cap means that most players are overpriced (because clubs are inherently overoptimistic...there are clubs who make money just by developing players and selling them to bigger clubs). The actual implication of salary caps (in combination with FFP) is that success is correlated to your ability to generate revenue. It isn't that the top clubs can pay more for players, it is that they can "lose" more overpaying than anyone else. As an example, Manchester United have rock-solid sponsorship revenue (iirc, they even have a "tractor partner"...they sponsors for literally everything) so they can overpay for almost every player, lose tons of money doing so, and end up doing okay. If a club lower down the table made deals as bad, they would get relegated. No salary caps just mean the rich clubs get richer.

hervature 5y ago

What I want to see happen is for a league to experiment with data driven rules that encode how the game should be played. For example, a dynamically altered 3-point value that ensures the expected value for different plays are equal. For example, some days, the 3-point will be worth 2.7 and other days 3.1 depending on league data. It would be awesome to see multiple strategies be optimal and reward teams that are good at multiple strategies as they can adjust their play style for whatever the data derived rules are.