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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 professi

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.
- stanford_labrat 5y ago
  Happy to share. I’ve worked as an “analyst” which basically has boiled down to creating 15 minute slide decks detailing player behaviors/patterns, which ideally would be used to scout upcoming opponents and look for weaknesses. The easily digestible stats like KDA or gold/min are relatively useless in this regard which is what I meant by them being low hanging fruit. So I’m currently in a situation where I’m curious on how to best quantify or develop a series of metrics that can prepare teams for their opponents. The current system of watching vods, taking notes, and making slides is rather dull if I’m being honest...and really not paradigm shifting. If you’re interested I can show you videos/presentations/figures.
bart_spoon 5y ago
I personally did work on modeling injury risk for an NBA team, which at the time was something they hadn't done much with and found very useful. Unfortunately it might be hard to apply that to LoL. > Obviously the games are different, but perhaps there are some common themes or generalized structures that can help frame what questions to ask? One of the most important things to understand is what the front office, coaches, and players want to know. Like I said, much of analytics these days (and not just in sports) is a solution searching for a problem. We build models based on what we can with the data, but the output of the models isn't actionable or something anyone is asking for. A better approach is to start from the questions that loom big in the eyes of those you are doing the analytics for. That sounds simple, but often doesn't happen, either because communication is poor, or neither party really understands what the other is trying to do. Also, often times a team doesn't even really know what questions they'd like answered, or at least not enough to verbalize it well. One thing I've found consistently useful is focusing on measuring uncertainty. These days people are extremely excited about machine learning and "AI", and I work extensively with deep learning in my current position. But most times in analytics, especially sports analytics, simply getting the best prediction possible isn't actually useful. What is far more useful is measures of uncertainty through more traditional statistical methods, especially Bayesian statistics and Monte Carlo simulation. It's one thing to predict a player in the draft will be better than another. But a team usually knows the answer to that already anyways. Its far more useful to quantify the range of possible outcomes for a player. Is it practically a sure thing? Is it not a sure thing but a reasonable bet? Or is it really a toss-up? These are insights that teams often don't have but can find much more useful in their decision-making. I generally don't focus on in-game strategy, since it is harder to give the team something they don't already know that they can reasonably act on. But meta-game insights could have a lot of potential. For traditional sports, this can be things like assessing player value, injury risk, draft capital, and contract valuation. Some of these may not translate well to LoL. I haven't played the game, but I've watched it a few times, and perhaps something could be done looking for insights regarding the hero pool. There are so many different heroes in the game that can act/oppose each other that quantifying those relationships somehow could be interesting.

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.

stanford_labrat 5y ago
Happy to share. I’ve worked as an “analyst” which basically has boiled down to creating 15 minute slide decks detailing player behaviors/patterns, which ideally would be used to scout upcoming opponents and look for weaknesses. The easily digestible stats like KDA or gold/min are relatively useless in this regard which is what I meant by them being low hanging fruit. So I’m currently in a situation where I’m curious on how to best quantify or develop a series of metrics that can prepare teams for their opponents. The current system of watching vods, taking notes, and making slides is rather dull if I’m being honest...and really not paradigm shifting. If you’re interested I can show you videos/presentations/figures.

bart_spoon 5y ago

I personally did work on modeling injury risk for an NBA team, which at the time was something they hadn't done much with and found very useful. Unfortunately it might be hard to apply that to LoL. > Obviously the games are different, but perhaps there are some common themes or generalized structures that can help frame what questions to ask? One of the most important things to understand is what the front office, coaches, and players want to know. Like I said, much of analytics these days (and not just in sports) is a solution searching for a problem. We build models based on what we can with the data, but the output of the models isn't actionable or something anyone is asking for. A better approach is to start from the questions that loom big in the eyes of those you are doing the analytics for. That sounds simple, but often doesn't happen, either because communication is poor, or neither party really understands what the other is trying to do. Also, often times a team doesn't even really know what questions they'd like answered, or at least not enough to verbalize it well. One thing I've found consistently useful is focusing on measuring uncertainty. These days people are extremely excited about machine learning and "AI", and I work extensively with deep learning in my current position. But most times in analytics, especially sports analytics, simply getting the best prediction possible isn't actually useful. What is far more useful is measures of uncertainty through more traditional statistical methods, especially Bayesian statistics and Monte Carlo simulation. It's one thing to predict a player in the draft will be better than another. But a team usually knows the answer to that already anyways. Its far more useful to quantify the range of possible outcomes for a player. Is it practically a sure thing? Is it not a sure thing but a reasonable bet? Or is it really a toss-up? These are insights that teams often don't have but can find much more useful in their decision-making. I generally don't focus on in-game strategy, since it is harder to give the team something they don't already know that they can reasonably act on. But meta-game insights could have a lot of potential. For traditional sports, this can be things like assessing player value, injury risk, draft capital, and contract valuation. Some of these may not translate well to LoL. I haven't played the game, but I've watched it a few times, and perhaps something could be done looking for insights regarding the hero pool. There are so many different heroes in the game that can act/oppose each other that quantifying those relationships somehow could be interesting.