Sloan Sports Analytics Conference

[sousuffer]

I never said anything that it doesn't help, just that it would be foolish to use on it's own. And really, even with any kind of advanced model, it would be. Again, it tells you what happened, but with little context. I've never said to dismiss the idea, just that the idea of relying solely on advanced statistics in hockey is completely foolish, just as relying solely on the eye test would be. Do I think, forced to choose, that one would be better going strictly with the eye test? Yeah, I do. Do I think stats are useless and don't help? Not one bit.

As far as your system, it does sound a bit impressive, but even with that model, you'd be foolish to ignore those players and just go with it. Also, I'll admit I know nothing about those players, but it seems like you have a lot of narrative there. The first guy sounds impressive, the other three sound like you're using your opinion and conjecture to make your point. Isn't this exactly the kind of thing the advanced stats community is against?



Ok, seriously, we're comparing it with cancer now? That's where it gets ridiculous. My main point was that statistics out there right now for hockey really don't tell you a lot, and there's no model out there that will give you context about what's going on. More yet, for the fifth time, I never said the eye test is the only way to go or we should never try to find these models, just that on their own, they wouldn't do much. Is that really a stretch?

It's not ridiculous - you are changing your story. You stated that in baseball these models can be built because the game is simple but hockey is too complex and has too many moving parts to generate models that tell us anything useful. Now you're telling me there's no model yet but that it can be done. I simply argued your original point that if models can be built for more complex systems like diseases, then it can be done in hockey with a proper effort. I never once stated that it was already done.

 

[The Mighty Duck Man]

It's not ridiculous - you are changing your story. You stated that in baseball these models can be built because the game is simple but hockey is too complex and has too many moving parts to generate models that tell us anything useful. Now you're telling me there's no model yet but that it can be done. I simply argued your original point that if models can be built for more complex systems like diseases, then it can be done in hockey with a proper effort. I never once stated that it was already done.

Obviously there's a chance it could be done, you never say never, but there's definitely nothing out there that fits the bill. There's also a chance that better statistics don't really come to light

Even then, though, it would still be fairly flawed. A model for a complex disease is one thing, but a game is quite a bit different, especially when it comes to projecting how they'll become.

To be honest, I don't know nearly enough about cancer and it's components to make the comparison. However, I have a hard time believing there will be a model, solely using statistics and without having seen a game, that will tell you about a player's strengths and weaknesses, especially in those junior ranks. It might tell you what he was able to produce, or perhaps how efficient said player is, but not much about their game. I know I'm a skeptic, but I do believe any model used would have to include some level of scouting, even if just for context alone.

 

[Mathletic]

As far as your system, it does sound a bit impressive, but even with that model, you'd be foolish to ignore those players and just go with it. Also, I'll admit I know nothing about those players, but it seems like you have a lot of narrative there. The first guy sounds impressive, the other three sound like you're using your opinion and conjecture to make your point. Isn't this exactly the kind of thing the advanced stats community is against?

It's a given that you should always take early results coming in small samples with precaution. That said, I'd invite you to ask about those players on the Q forum. I'm sure you'll get some answers about each of these guys. Also, early results are consistent with what I'm getting for previous years and for the NHL.

Gignac and St-Amant were both 12th round picks and both had very good seasons. I didn't try to prove my point by quoting stats, that said, they're both already performing at the league average level for forwards (as rookies) offensively, and above average level defensively for St-Amant. There's a few links talking about unexpected seasons from these 2 guys. Both are in french but both saying these 2 players have come out of nowhere to play important roles on their team as rookies.

Ouellet-St-Amant said to have the tools to play in the pros by his coach

http://www.abitibiexpress.ca/Sports...-les-outils-pour-jouer-professionnel»/1

Here on Gignac, (Gignac an unexpected surprise)

http://www.journaldequebec.com/2013/12/22/william-gignac-une-surprise-inattendue-chez-les-sags

One on Luc Deschênes. Of course you may take this with a grain of salt as he was traded shortly after. You may take this as his coach pumping his tires to raise his value but still, very accurate from what I saw.

His former coach saying Deschesnes was able to handle top 4 minutes at times. Still has some stuff to work on as any rookies but should become a very good player in the league. Has to improve his speed but already has very good vision, shot, offensive abilities and already very strong.

http://www.acadienouvelle.com/sports/2013/12/09/luc-deschenes-impressionne-la-direction-des-tigres/

Also, as a point of comparison, if compared to the actual Q picks. Only Jason Bell had an impact on his team. The player selected in the first round. Waked and Leblond also fielded some ice but haven't stood out yet.

I have a hard time believing there will be a model, solely using statistics and without having seen a game, that will tell you about a player's strengths and weaknesses, especially in those junior ranks. It might tell you what he was able to produce, or perhaps how efficient said player is, but not much about their game. I know I'm a skeptic, but I do believe any model used would have to include some level of scouting, even if just for context alone.

Depends how you take it. One could use biomechanics to study a player's strengths and weaknesses on ice. You could also use various psychosocial and psychological tests to assess a part of one's mental game. That said, I don't think the ultimate goal of a good model is to tell precisely one's strengths and weaknesses. In the end the model should forecast the expected production from each player (production as in offensive, defensive or goaltending). The goal isn't to replace the people in place. It might lead to changes on what each person has to do but it shouldn't be seen as a dichotomy between stats and scouts as in moneyball.

 

[Bear of Bad News]

There's also a chance that better statistics don't really come to light

I suppose there's a chance, but how much of a chance?

At every point in the past, there's been something fundamental that people haven't understood about the sport of hockey. What gives you confidence that here and now is the time where we've gone as far as we can, and that there's no additional statistical knowledge that can be gained?

I know where my money is.

 

[The Mighty Duck Man]

Depends how you take it. One could use biomechanics to study a player's strengths and weaknesses on ice. You could also use various psychosocial and psychological tests to assess a part of one's mental game. That said, I don't think the ultimate goal of a good model is to tell precisely one's strengths and weaknesses. In the end the model should forecast the expected production from each player (production as in offensive, defensive or goaltending). The goal isn't to replace the people in place. It might lead to changes on what each person has to do but it shouldn't be seen as a dichotomy between stats and scouts as in moneyball.

That's something I could see being big, although I'm not sure if there are too many tests that can accurately convey things like vision or even Hockey IQ. It's a definite possibility that it will happen, but won't be easy, and I imagine accuracy can vary, especially since mental aspects like vision and hockey IQ don't even go hand in hand. It's definitely possible, but it'll be a long road IMO.

Also, again, I'm not saying stats are going to replace anyone, or that they're useless or broken or anything. Simply, I just can't see hockey getting to a point like baseball where the entire game can accurately be tracked and have data recorded for. Is there anything truly wrong with that statement? It's not a slam on statistics, just that I don't think we'll get to a point where they convey such a high level of accuracy, like with baseball. I think they'll miss some things, but that's fine, because the eye tests misses a lot of things, and both together should make a much clearer picture.

I suppose there's a chance, but how much of a chance?

At every point in the past, there's been something fundamental that people haven't understood about the sport of hockey. What gives you confidence that here and now is the time where we've gone as far as we can, and that there's no additional statistical knowledge that can be gained?

I know where my money is.

I'm not saying we'll start believing we totally understand the game, just that there aren't many raw statistics in hockey, which is crucial to developing advanced ones, and right now we're close to taking it as far as it'll go. I'm guessing we'll see things eventually released like passing percentages and actual possession times per zone(which will put corsi and fenwick right to bed), but even that has a chance of never really being available to the general public. It doesn't help that all 30 teams likely have all of this data made available to them, and likely have no interest in the general public seeing it. Individuals could track these things on their own, but considering they could've done that instead of coming up with two entirely different statistics, I'm not holding my breath on it happening soon.

 

[Mathletic]

That's something I could see being big, although I'm not sure if there are too many tests that can accurately convey things like vision or even Hockey IQ. It's a definite possibility that it will happen, but won't be easy, and I imagine accuracy can vary, especially since mental aspects like vision and hockey IQ don't even go hand in hand. It's definitely possible, but it'll be a long road IMO.

In the book "The Sprts Gene" they discuss various visual acuity tests. They even mention the guy working for the Carolina Hurricanes as being some sort of pioneer in the visual testing area in sports. One example of test they mention is the speed and accuracy at which players can determine whether or not a baseball, for example, was present in an image shown rapidly. They say it's one of few physical tests that pro athletes generally test well above average population unlike reflexes and whatnot. It's seen as a good proxy for something like vision or batting eye.

 

[The Mighty Duck Man]

In the book "The Sprts Gene" they discuss various visual acuity tests. They even mention the guy working for the Carolina Hurricanes as being some sort of pioneer in the visual testing area in sports. One example of test they mention is the speed and accuracy at which players can determine whether or not a baseball, for example, was present in an image shown rapidly. They say it's one of few physical tests that pro athletes generally test well above average population unlike reflexes and whatnot. It's seen as a good proxy for something like vision or batting eye.

Sorry, my apologies, I wasn't referring to vision in the sense of eyesight, but rather on-ice vision, as in the ability to see a play develop before it actually happens, or in most cases what a guy does or doesn't do with the puck. Ridiculously hard to scout as it is, but considering so much of it is a judgement call, I could see a few issues trying to come up with a statistical analysis about it.

 

[Mathletic]

Sorry, my apologies, I wasn't referring to vision in the sense of eyesight, but rather on-ice vision, as in the ability to see a play develop before it actually happens, or in most cases what a guy does or doesn't do with the puck. Ridiculously hard to scout as it is, but considering so much of it is a judgement call, I could see a few issues trying to come up with a statistical analysis about it.

I actually meant there seems to be a pretty close connection between the two

I'm no neuroscientist but basically what we call "vision" in hockey is pattern recognition. In the same way chess players can draw from millions of patterns they've seen before in order to figure out the best move to make. Athletes, like baseball players, have to make a quick decision based on the pitcher's motion, the early trajectory of the ball and so on. The better one's vision is, the better that person will notice subtle information that will be useful for recognizing similar patterns in the future.

Athletes, like chess players, cannot anticipate what is going to happen next. They simply draw from experience. There was an episode about memory on the show Doc Zone on CBC. IIRC they had a chess grand master recalling a board that was shown very quickly on a passing truck. The grand master could identify complex patterns very easily at first. However, once they started showing random patterns, the same person could no longer recall any of them. The grand master didn't recall the pattern per say. The person simply filled in the blanks by logic. The same thing that happens when we read. We don't read every letter, we fill in most of it by logic.

In the book the sports gene. They talk about how important pattern recognition is for a better as it is physically impossible to keep track of the ball. They give the example of Jenny Finch. Guys like Bonds, A-Rod and so on could hit any pitcher in the MLB throwing over 100mph. However, once they faced Finch, they couldn't even touch the ball. It wasn't that the ball went or seemed to go faster. It's just that they have no information to "anticipate" what is going to happen based on the arm angle and whatnot.

The same holds true for hockey players. They don't anticipate per say what is going to happen. They simply fill in the blanks based on the patterns they've seen before. The more information one can gather from a quick looks will become a huge advantage over time.

 

[Patman]

Google "systems biology" and "bioinformatics". We can use statistics to model hundreds of thousands of molecules and other moving parts in cancer whose functions/roles are much more seemingly random, but modeling 10 players in hockey has too many moving parts? Just because most people don't have the desire (and in some cases, intellectual power) to learn the tools necessary to figure out how to do so doesn't mean that we should just assume that the more "human" approach is the correct one. Human nature is that one tends to dismiss approaches that one does not understand.

The "eye test" used to be the way to find molecules involved in disease. Over the course of 20+ years, very few were discovered (leading to even fewer treatments). Since statistical approaches started becoming more commonplace (about 15 years ago), hundreds of new molecules (and data types) were discovered. Granted, there are many false positives that don't tell us anything, but the true positives are the important discoveries. Just because stats will give us some wrong answers doesn't mean the right ones it does give us won't tell us exactly what we need (and in a much better way than the eye test).

Comparing bioinformatics to hockey is woefully naive. In the modern era the field is loaded with data. In some ways, too much data. They have fundamentals which have been tried and tested models in how to decode DNA amongst several other elements of biological mathematics. There has been a lot to hack through. Deciding when protein chains begin and end, using a limited set of sequences to identify possible problem sequences in DNA. Say what you want, but there's a lot of rigorous, yet difficult, mathematics which relates DNA and the rest.

As for hockey. The issue with hockey is knowing how to break down the game. Baseball comes pre-broken down. Basketball and football have devised means. If you analyze simple things like goals and the rest you are very latently getting into the processes which define the game. When you're used to observing these processes then somebody tells you that this magical formula of ice time, scoring, and +/- says it all... well...

The question is how can you weaponize what coaches look for. The great thing about statistics and models when applied allows you to take into consideration things you have too little time to consider. You see a player for a particular window of time and there's a tendency to over-state performance in that small window. Define a construct then have somebody else create related data and then you have some useful stuff. Its knowing what to collect and then what it means.

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The general issue of sports analytics is learning how to use tools to turn action into data and further analyzing useful patterns of play.

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Personal comment. The sloan world and the stat world don't really inter-connect. They talk in similar terms and the stat world is more academic... just a disappointment more than anything else. The business background vs math background cultures are not merging.

 

[Patman]

As to Nate Silver...

He's invested into making analytics and so when all you have is a hammer.... that's as far as I go.


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I sympathize with Mr. Burke but he's also the one with the most throwing around money... sounds like an opportunity turned up more than anything else. Seems to me that a few people R&Ding the concepts of the game wouldn't be a terrible waste.

Information and context. Information is useless without a usable context. I'd be careful as casting certain people as dinosaurs. Just because they reject you and your ideas doesn't mean that their counter-argument isn't well-formed. Of course, it doesn't mean that it is either. I don't think hockey is really at the point where they want to sit down and start breaking apart the game aside from the film revolution of the 2000s decade.

I would take it, that so far, nobody has given him something he feels he can use. Until then, its all just noise.

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As far as I can tell, with a limited basis of viewing, even in the big sports the "analytics" "team" isn't more than 1-2 people. I would think baseball there might be small analytics teams at this point for some clubs but that's because of the volume of minor league data available and the maturity of methods.

My view, things aren't as far along as you think they are. The biggest revolution is in knowing what to collect.

 

[Mathletic]

As for hockey. The issue with hockey is knowing how to break down the game. Baseball comes pre-broken down. Basketball and football have devised means. If you analyze simple things like goals and the rest you are very latently getting into the processes which define the game. When you're used to observing these processes then somebody tells you that this magical formula of ice time, scoring, and +/- says it all... well...

The question is how can you weaponize what coaches look for. The great thing about statistics and models when applied allows you to take into consideration things you have too little time to consider. You see a player for a particular window of time and there's a tendency to over-state performance in that small window. Define a construct then have somebody else create related data and then you have some useful stuff. Its knowing what to collect and then what it means.

----

The general issue of sports analytics is learning how to use tools to turn action into data and further analyzing useful patterns of play.

----

Personal comment. The sloan world and the stat world don't really inter-connect. They talk in similar terms and the stat world is more academic... just a disappointment more than anything else. The business background vs math background cultures are not merging.

---

As far as I can tell, with a limited basis of viewing, even in the big sports the "analytics" "team" isn't more than 1-2 people. I would think baseball there might be small analytics teams at this point for some clubs but that's because of the volume of minor league data available and the maturity of methods.

My view, things aren't as far along as you think they are. The biggest revolution is in knowing what to collect.

I agree with a lot of what you're seeing. I'll extrapolate on some of your points.

Quite often I see people saying:"baseball and basketball are easy to analyze. They're discrete games in a way compared to hockey". I'd argue that even though, yes some parts of baseball are easy to analyze ... btw, we have such easy objective data in hockey like goals, assists, who's on the ice and whatnot ... Baseball analysts still had to go much deeper in the data.

As great as moneyball is as a book, the A's were still only an average offense. Although it is true that they were able to generate average offense with one of the smallest budget in the league and that provided some good value for the team. Their bread and butter was on defense. The moneyball narrative would be much less interesting if it went "How a budget team was able to become average."

Same for the Tampa Bay Rays. Once their GM, fresh of wall street, came in. What made the Rays and A's a contending teams was BABIP. Batting average on balls in play. Rays introduced all those quirky defenses we see in the league nowadays. Even when traditional baseball analysts thought you couldn't control where balls fell into play these guys went further than the crowd and developed their own metrics. They had to do this through basic scientific research and get their own data by either watching games or using softwares.

The same holds true in hockey. Although there's already quite a bit of an advantage to gain from various "advanced stats"... as seen with guys like MacArthur, Jagr, Grabovski, Weaver and so on ... The bread and butter of an analytics team will be to develop their own metrics by analyzing video data. Other sports aren't that much easier to analyze in the end.

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Sports leagues aren't open markets. So, it's quite hard to have any kind of evolution when league members decide on who their competition is.

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You're right, outside of the Tampa Bay Rays, very few teams have more than 2 or 3 people working in their analytics department. That said, there's quite a few businesses that have developed over the years and consult with teams on a regular basis.

 

[hatterson]

I agree with a lot of what you're seeing. I'll extrapolate on some of your points.

Quite often I see people saying:"baseball and basketball are easy to analyze. They're discrete games in a way compared to hockey". I'd argue that even though, yes some parts of baseball are easy to analyze ... btw, we have such easy objective data in hockey like goals, assists, who's on the ice and whatnot ... Baseball analysts still had to go much deeper in the data.

As great as moneyball is as a book, the A's were still only an average offense. Although it is true that they were able to generate average offense with one of the smallest budget in the league and that provided some good value for the team. Their bread and butter was on defense. The moneyball narrative would be much less interesting if it went "How a budget team was able to become average."

Same for the Tampa Bay Rays. Once their GM, fresh of wall street, came in. What made the Rays and A's a contending teams was BABIP. Batting average on balls in play. Rays introduced all those quirky defenses we see in the league nowadays. Even when traditional baseball analysts thought you couldn't control where balls fell into play these guys went further than the crowd and developed their own metrics. They had to do this through basic scientific research and get their own data by either watching games or using softwares.

The same holds true in hockey. Although there's already quite a bit of an advantage to gain from various "advanced stats"... as seen with guys like MacArthur, Jagr, Grabovski, Weaver and so on ... The bread and butter of an analytics team will be to develop their own metrics by analyzing video data. Other sports aren't that much easier to analyze in the end.

---

Sports leagues aren't open markets. So, it's quite hard to have any kind of evolution when league members decide on who their competition is.

---

You're right, outside of the Tampa Bay Rays, very few teams have more than 2 or 3 people working in their analytics department. That said, there's quite a few businesses that have developed over the years and consult with teams on a regular basis.

Something being easier doesn't mean it's easy. No one, at least that I've seen, doubts that a ton of work and intelligence has gone into and still goes into baseball analytics. It's clear that many very intelligent and determined people spent a great deal of time developing some of the advanced baseball statistics.

However that doesn't change the fact that hockey is a much more dynamic game than Baseball.

In Baseball there are roughly 200 discrete events per game (pitches) and even the majority of those don't have anything happening in them aside from a single event (a pitch). Since they are discrete events and don't really flow into each other it's much more feasible to pull similar situations from other games and get a wealth of data.

In Basketball you have a majority of the game happening in a set half court offense. The point guard brings the ball up the court at a modest pace and everyone sets up and runs some set plays. Yes, there's fast breaks and transition games but those still have very defined roles for the teams.

In both sports one team is very clearly on offense and one team is very clearly on defense. Baseball is obviously the most set like this. There is no connection between how your offense performs and how your defense performs aside from momentum or potential pressure (if your pitcher is getting lit up you might be more inclined to swing for the fences or take a risk to score a run). Basketball is a little more linked because of transition and fast breaks but there's still a lot of set team A on offense team B on defense type plays. Yes, being better defensively helps you offensively in that it gives you slightly more possession time, but you cannot dominate a game offensively by being amazing defensively and vice versa.

In hockey those relationship are a lot more grey. Take the Team Canada in the Olympics. They had one of the best defensive performances of all time in the Olympics. Yes, that's partly because of good defensive systems and skill but it was also because they were dominant offensively. One of the biggest reason they were so stifling defensively is because they just didn't let the other team have the puck. Theoretically a perfect defensive game is one in which the other team *never* touches the puck and thus you're never actually playing defense. Changing your offensive system naturally changes your defensive system and vice versa.

Plays are naturally a blend of offense and defense. A defenseman pinching in is an offensive play, but comes at a defensive cost. A forward coming back early to help out the defense is a defensive play, but comes at an offensive cost.

In Baseball you can have weird defenses like having 3 infielders on the left side of the infield, your center fielder in shallow right field, your left fielder in mid left-center and your right fielder back near the home run pole. Absolutely none of those changes has any effect on your team when it's your turn to bat.

In Basketball you can overload a defensive side, or double team someone, or play a tight zone or a loose zone and although there's some offensive impact in how you can transition, for the most part you can run both your defensive and offensive games separately. In hockey you really can't. Having Couturier shadow Crosby in the defensive zone naturally affects where he'll be when you're moving into the offensive zone.

That doesn't mean Baseball is easy or that hockey is impossible, it just means that any analysis is naturally less cut and dried.

 

[Mathletic]

Something being easier doesn't mean it's easy. No one, at least that I've seen, doubts that a ton of work and intelligence has gone into and still goes into baseball analytics. It's clear that many very intelligent and determined people spent a great deal of time developing some of the advanced baseball statistics.

However that doesn't change the fact that hockey is a much more dynamic game than Baseball.

In Baseball there are roughly 200 discrete events per game (pitches) and even the majority of those don't have anything happening in them aside from a single event (a pitch). Since they are discrete events and don't really flow into each other it's much more feasible to pull similar situations from other games and get a wealth of data.

In Basketball you have a majority of the game happening in a set half court offense. The point guard brings the ball up the court at a modest pace and everyone sets up and runs some set plays. Yes, there's fast breaks and transition games but those still have very defined roles for the teams.

In both sports one team is very clearly on offense and one team is very clearly on defense. Baseball is obviously the most set like this. There is no connection between how your offense performs and how your defense performs aside from momentum or potential pressure (if your pitcher is getting lit up you might be more inclined to swing for the fences or take a risk to score a run). Basketball is a little more linked because of transition and fast breaks but there's still a lot of set team A on offense team B on defense type plays. Yes, being better defensively helps you offensively in that it gives you slightly more possession time, but you cannot dominate a game offensively by being amazing defensively and vice versa.

In hockey those relationship are a lot more grey. Take the Team Canada in the Olympics. They had one of the best defensive performances of all time in the Olympics. Yes, that's partly because of good defensive systems and skill but it was also because they were dominant offensively. One of the biggest reason they were so stifling defensively is because they just didn't let the other team have the puck. Theoretically a perfect defensive game is one in which the other team *never* touches the puck and thus you're never actually playing defense. Changing your offensive system naturally changes your defensive system and vice versa.

Plays are naturally a blend of offense and defense. A defenseman pinching in is an offensive play, but comes at a defensive cost. A forward coming back early to help out the defense is a defensive play, but comes at an offensive cost.

In Baseball you can have weird defenses like having 3 infielders on the left side of the infield, your center fielder in shallow right field, your left fielder in mid left-center and your right fielder back near the home run pole. Absolutely none of those changes has any effect on your team when it's your turn to bat.

In Basketball you can overload a defensive side, or double team someone, or play a tight zone or a loose zone and although there's some offensive impact in how you can transition, for the most part you can run both your defensive and offensive games separately. In hockey you really can't. Having Couturier shadow Crosby in the defensive zone naturally affects where he'll be when you're moving into the offensive zone.

That doesn't mean Baseball is easy or that hockey is impossible, it just means that any analysis is naturally less cut and dried.

I agree with most of what you're saying although I think basketball and hockey are closer. Especially when analyzing PP. I think this situation in hockey resembles a lot of what we're seeing in basketball.

 

[hatterson]

I agree with most of what you're saying although I think basketball and hockey are closer. Especially when analyzing PP. I think this situation in hockey resembles a lot of what we're seeing in basketball.

Yes that's true. What I was saying about hockey was in regards to ES play.

Powerplays and goalie pulled situations are much more offense vs. defense but the issue you run into there is that they represent barely 10% of the average game. Not only does that raise sample size questions, it also raises motivation questions. Is it really worth investing a ton of time, especially when analytics is so new in hockey, to a phase of the game you won't be on for 80% of it?

 

[Mathletic]

Yes that's true. What I was saying about hockey was in regards to ES play.

Powerplays and goalie pulled situations are much more offense vs. defense but the issue you run into there is that they represent barely 10% of the average game. Not only does that raise sample size questions, it also raises motivation questions. Is it really worth investing a ton of time, especially when analytics is so new in hockey, to a phase of the game you won't be on for 80% of it?

Although it's 10% of playing time it still accounts for 20 to 25% of a team's GF. Not to mention that your findings may well lead to understanding of ES play, especially if we think about shot quality and that sort of thing. So yes, I think it's definately something worth investing time/money in. I don't understand why there would be sample size issues. It always depends on what you study but in general I don't see that being an issue.

 

[hatterson]

Although it's 10% of playing time it still accounts for 20 to 25% of a team's GF. Not to mention that your findings may well lead to understanding of ES play, especially if we think about shot quality and that sort of thing. So yes, I think it's definately something worth investing time/money in. I don't understand why there would be sample size issues. It always depends on what you study but in general I don't see that being an issue.

Sorry, I didn't mean that sample size would cause issues, just simply that you only have 10% of the hockey to actually analyze so it may be harder to explore all the options you want to.