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Advancing Sports Analytics Through AI Research

78 points by atg_abhishek 5y ago

slg 5y ago
This article is presenting some of this stuff as new when it has been used for years in sports. I haven't read the linked paper yet, so maybe there is some deeper insight there that I am missing in this article, but computer vision, statistical learning, and game theory are all well established in multiple sports.
- thom 5y ago
  That’s very broadly true but football (of the soccer variety) still lags a bit behind basketball. The number of clubs using computer vision, or deep learning models on tracking data is probably still in the single digits. Even clubs that do have people capable of doing this (or pay a provider), aren’t necessarily integrating that with their actual coaching or recruitment.
dovrce 5y ago
Technology makes sports less interesting, refinement culture https://paulskallas.substack.com/p/refinement-culture https://paulskallas.substack.com/p/refinement-culture
- bart_spoon 5y ago
  I don't completely disagree with every point made there, but overall I think the author has trouble defining refinement culture in part because its not so much a coherent idea, and more a rambling rant against things changing. I don't really find most of their post to make any substantial points at all, beyond a few of the points about basketball and baseball. I agree that those sports have suffered in terms of watch-ability at the hands of maximizing competitive efficiency. But I don't really know why anyone would expect anything different. The point of the game from a team/player perspective is to win. Using analytics to determine that there are advantages to three-point shots or walks isn't any different than any other strategic layer of the game. The solution shouldn't be to rail against analytics (which is no solution at all), it should be for the leagues to use analytics of their own to control for some of these strategies, like a government is supposed to hedge against externalities in an market. For basketball, simply push the 3-point line back a few feet so its no longer so much more advantageous than a midrange jump shot. I'm not much of a baseball fan, but surely the league can do something, like increases the number of balls to walk to 5, that might bring some of the boring-but-efficient strategies into line with more exciting ones. Though I suspect the author and many others would be against any substantial rule alterations given their abhorrence towards change, and would rather just pine about the good old days.
  - JALTU 5y ago
    +1 for "just" changing the rules or ball or whatever to improve (or at least test) game dynamics for greater balance and (presumably) interest. My personal interest is for soccer to allow more substitutions to let coaches tweak the team more as the game is played. Small change; maybe big results in scoring.
whoisjuan 5y ago
As an AI enthusiast, this is exciting. As a Football fan, this is a concerning trend. Usage of technology has ruined several aspects of traditional association football. The VAR for example, is ruining the serendipitous nature of football. Fans in general dislike the VAR because it removes this extra element of drama that the sport has. I’m supportive of technology in sports though. I just think there should be a limit, otherwise they become a very boring and cold activity. I’m not necessarily against the suggested approach here. Having analytics of how a game is going or went seems useful and a net positive. It’s just hard to tell if that could keep eroding those entertainment elements that make Football fun to watch.
- jgalt212 5y ago
  Baseball has been grievously harmed by the numbers. Too many strike outs and home runs, and the shift has just become infuriating. Reading a 6-3 in the scorebook, one has no idea what actually happened.
- daveevad 5y ago
  As a basketball fan, my only gripe with VAR is how much time it takes to complete. Advanced AI would presumably extend into those systems and make the review near instantaneous.
- rjtavares 5y ago
  I wouldn't really be concerned. This new element basically changes coaching and recruitment, not so much the actual play. Unlike basketball, football is open enough to not have an easy to prove maximizing strategy (unlike basketball, with the emphasis on 3 pts shooting).
  - clairity 5y ago
    the 3-pointer by itself isn’t point-maximizing, and surely doesn’t assure victory. the point-maximization is only valid in the context of the current game aesthetics. if a team tried to shoot only 3-pointers, they’d almost certainly lose because the counter-defense becomes simple. only by mixing shot selection does the 3-pointer’s slight advantage matter, and it’s not enough of an advantage to trivialize the game. it does what it was meant to do, which is inject unpredictability/variety into both offense and defense, especially at the end of close games, for more interesting and exciting gameplay.
rjtavares 5y ago
> Arrigo Sacchi, a successful Italian football coach and manager who never played professional football in his career, responded to criticism over his lack of experience with his famous quote when becoming a coach at Milan in 1987: “I never realised that to be a jockey you had to be a horse first.” Love that quote.
- jtsiskin 5y ago
  I’m not sure - surely a jockey who did have the opportunity to be a horse (via a one year brain transplant) would have a significant advantage
PaulRobinson 5y ago
There's nothing jumping out at me as original thinking in this article or the paper abstract that points to the paper being worth more study. All of what has been mentioned has been conceived of, thought about and discussed before, and some years ago. I've seen more advanced thinking in some models used to make bets on football matches. As a Man City fan, I'll try and make this sound less snarky than it will come across, but it's going to be snarky: it's good to see Liverpool are finally - with Deep Mind's help - catching up to where many other teams in the league were more than half a decade ago. Most players and coaches are not particularly interested in these models and the most useful applications seem to be related to scouting and the transfer market - "I need a replacement for player X, but 10 years younger and with a better left foot", is the sort of problem that data science is already helping with - and the clubs themselves prefer to put their money onto the field of play rather than expensive and experimental ML training, so the market for this sort of work is limited. The challenges in this field are not really about data, algorithms or computational capability of the hardware. The main issues are of applicability, cultural fit and economics. Finding methods for addressing _those_ issues might be more useful to mainstream AI adoption than the data, algorithm and hardware problems many seem to look at first.
- wdb 5y ago
  Sports analytics is an interesting field, worked in it, you can use it for keep tracking of the health of your players. To know when you need to keep them from playing the next game or stop them from continuing their current game.
  - PaulRobinson 5y ago
    On health tracking stuff, rugby was some years ahead of any other sport - is that still the case?
- thom 5y ago
  Liverpool have been doing this for years without Deepmind’s help (it’s been more than five years since Michael Edwards was decried as a laptop guru who betrayed Brendan Rogers, for example). The analytics team at City Football Group has only really been built over the last year (but are very good). I don’t know that any of their dabbling before that was at this level. Your point about applicability is totally right though, see Arsenal with StatDNA and the arguably weak impact of Barcelona’s Innovation Hub. I’d argue that Leicester’s use of (at the time) very rudimentary stats, nevertheless well integrated into their process, was a big part of their sudden success.
wdb 5y ago
Interesting years ago worked on using AI and body sensors to determine the health and the chance of concussions in football players. Awesome project.
bart_spoon 5y ago
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?
jvanderbot 5y ago
I really want to see boxing scoring be fully automated. I would love to see crowdsourced classifiers of "good" boxing matches, rounds, and exchanges, compared with the traditional queensbury rules and hit scoring.

78 points by atg_abhishek 5y ago

slg 5y ago

This article is presenting some of this stuff as new when it has been used for years in sports. I haven't read the linked paper yet, so maybe there is some deeper insight there that I am missing in this article, but computer vision, statistical learning, and game theory are all well established in multiple sports.

thom 5y ago
That’s very broadly true but football (of the soccer variety) still lags a bit behind basketball. The number of clubs using computer vision, or deep learning models on tracking data is probably still in the single digits. Even clubs that do have people capable of doing this (or pay a provider), aren’t necessarily integrating that with their actual coaching or recruitment.

dovrce 5y ago

Technology makes sports less interesting, refinement culture https://paulskallas.substack.com/p/refinement-culture https://paulskallas.substack.com/p/refinement-culture

bart_spoon 5y ago
I don't completely disagree with every point made there, but overall I think the author has trouble defining refinement culture in part because its not so much a coherent idea, and more a rambling rant against things changing. I don't really find most of their post to make any substantial points at all, beyond a few of the points about basketball and baseball. I agree that those sports have suffered in terms of watch-ability at the hands of maximizing competitive efficiency. But I don't really know why anyone would expect anything different. The point of the game from a team/player perspective is to win. Using analytics to determine that there are advantages to three-point shots or walks isn't any different than any other strategic layer of the game. The solution shouldn't be to rail against analytics (which is no solution at all), it should be for the leagues to use analytics of their own to control for some of these strategies, like a government is supposed to hedge against externalities in an market. For basketball, simply push the 3-point line back a few feet so its no longer so much more advantageous than a midrange jump shot. I'm not much of a baseball fan, but surely the league can do something, like increases the number of balls to walk to 5, that might bring some of the boring-but-efficient strategies into line with more exciting ones. Though I suspect the author and many others would be against any substantial rule alterations given their abhorrence towards change, and would rather just pine about the good old days.
- JALTU 5y ago
  +1 for "just" changing the rules or ball or whatever to improve (or at least test) game dynamics for greater balance and (presumably) interest. My personal interest is for soccer to allow more substitutions to let coaches tweak the team more as the game is played. Small change; maybe big results in scoring.

whoisjuan 5y ago

As an AI enthusiast, this is exciting. As a Football fan, this is a concerning trend. Usage of technology has ruined several aspects of traditional association football. The VAR for example, is ruining the serendipitous nature of football. Fans in general dislike the VAR because it removes this extra element of drama that the sport has. I’m supportive of technology in sports though. I just think there should be a limit, otherwise they become a very boring and cold activity. I’m not necessarily against the suggested approach here. Having analytics of how a game is going or went seems useful and a net positive. It’s just hard to tell if that could keep eroding those entertainment elements that make Football fun to watch.

jgalt212 5y ago
Baseball has been grievously harmed by the numbers. Too many strike outs and home runs, and the shift has just become infuriating. Reading a 6-3 in the scorebook, one has no idea what actually happened.
daveevad 5y ago
As a basketball fan, my only gripe with VAR is how much time it takes to complete. Advanced AI would presumably extend into those systems and make the review near instantaneous.
rjtavares 5y ago
I wouldn't really be concerned. This new element basically changes coaching and recruitment, not so much the actual play. Unlike basketball, football is open enough to not have an easy to prove maximizing strategy (unlike basketball, with the emphasis on 3 pts shooting).
- clairity 5y ago
  the 3-pointer by itself isn’t point-maximizing, and surely doesn’t assure victory. the point-maximization is only valid in the context of the current game aesthetics. if a team tried to shoot only 3-pointers, they’d almost certainly lose because the counter-defense becomes simple. only by mixing shot selection does the 3-pointer’s slight advantage matter, and it’s not enough of an advantage to trivialize the game. it does what it was meant to do, which is inject unpredictability/variety into both offense and defense, especially at the end of close games, for more interesting and exciting gameplay.

rjtavares 5y ago

> Arrigo Sacchi, a successful Italian football coach and manager who never played professional football in his career, responded to criticism over his lack of experience with his famous quote when becoming a coach at Milan in 1987: “I never realised that to be a jockey you had to be a horse first.” Love that quote.

jtsiskin 5y ago
I’m not sure - surely a jockey who did have the opportunity to be a horse (via a one year brain transplant) would have a significant advantage

PaulRobinson 5y ago

There's nothing jumping out at me as original thinking in this article or the paper abstract that points to the paper being worth more study. All of what has been mentioned has been conceived of, thought about and discussed before, and some years ago. I've seen more advanced thinking in some models used to make bets on football matches. As a Man City fan, I'll try and make this sound less snarky than it will come across, but it's going to be snarky: it's good to see Liverpool are finally - with Deep Mind's help - catching up to where many other teams in the league were more than half a decade ago. Most players and coaches are not particularly interested in these models and the most useful applications seem to be related to scouting and the transfer market - "I need a replacement for player X, but 10 years younger and with a better left foot", is the sort of problem that data science is already helping with - and the clubs themselves prefer to put their money onto the field of play rather than expensive and experimental ML training, so the market for this sort of work is limited. The challenges in this field are not really about data, algorithms or computational capability of the hardware. The main issues are of applicability, cultural fit and economics. Finding methods for addressing _those_ issues might be more useful to mainstream AI adoption than the data, algorithm and hardware problems many seem to look at first.

wdb 5y ago
Sports analytics is an interesting field, worked in it, you can use it for keep tracking of the health of your players. To know when you need to keep them from playing the next game or stop them from continuing their current game.
- PaulRobinson 5y ago
  On health tracking stuff, rugby was some years ahead of any other sport - is that still the case?
thom 5y ago
Liverpool have been doing this for years without Deepmind’s help (it’s been more than five years since Michael Edwards was decried as a laptop guru who betrayed Brendan Rogers, for example). The analytics team at City Football Group has only really been built over the last year (but are very good). I don’t know that any of their dabbling before that was at this level. Your point about applicability is totally right though, see Arsenal with StatDNA and the arguably weak impact of Barcelona’s Innovation Hub. I’d argue that Leicester’s use of (at the time) very rudimentary stats, nevertheless well integrated into their process, was a big part of their sudden success.

wdb 5y ago

Interesting years ago worked on using AI and body sensors to determine the health and the chance of concussions in football players. Awesome project.

bart_spoon 5y ago

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?

jvanderbot 5y ago

I really want to see boxing scoring be fully automated. I would love to see crowdsourced classifiers of "good" boxing matches, rounds, and exchanges, compared with the traditional queensbury rules and hit scoring.