Ideas for Future Studies

[Bear of Bad News]

If you have an idea for something that could be analyzed here, but you either don't have the time, the patience, or the ability, please post it here.

Conversely, if you're reading this and looking for some way that you can contribute, please grab an open idea and hammer away at it (either alone or with others).

Click to read more...

 

[Bear of Bad News]

Something I'd like to look at someday (but honestly, it's not goaltender-related and therefore it always falls to the bottom of my list) is a comparison of the actual value of NHL draft picks (measured by some metric) versus the perceived value of NHL draft picks (measured by the value that NHL GMs impute when they trade those picks on or before draft day).

I'm throwing it up for grabs, and would love to be involved if things get moving.

 

[Bear of Bad News]

Can we quantify the standards currently being used by Hall of Fame voters? Have those standards changed over time?

For a player playing today, is there an accurate way to estimate their change of being inducted? (Note that this is a different question than whether or not they deserve it).

 

[Bear of Bad News]

It's generally accepted that the level of play in the NHL has increased over the years; where people differ is in the answer of "how much"?

Some claim that Guy Lafleur would look like a beer leaguer against today's players, while some claim that the effect is minimal.

How much is the effect? What impacts do expansion, the increase of the population pool, the breaking of the color barrier, and other external factors have on the effect?

 

[Bear of Bad News]

How much do NHL teams make transactions with their level of competition in mind?

For example, if I'm a team in a division where I expect the division leader to have 115 points, do I make transactions differently than if I expect the division leader to have 100 points?

 

[Mc5RingsAndABeer]

What team statistics have the best predictive capability for overall season performance? Can such statistics indicate under/overvalued playoff teams?

 

[Buttonwood]

Something I'd like to look at someday (but honestly, it's not goaltender-related and therefore it always falls to the bottom of my list) is a comparison of the actual value of NHL draft picks (measured by some metric) versus the perceived value of NHL draft picks (measured by the value that NHL GMs impute when they trade those picks on or before draft day).

I'm throwing it up for grabs, and would love to be involved if things get moving.

I've actually toyed around with this. I 'predicted' career games of draft picks from 1996-2006 then divided that by Michael Schuckers draft value chart (derived for expected career games, http://myslu.stlawu.edu/~msch/sports/Schuckers_NHL_Draft.pdf) then multiplied it by a 'production factor' (ppg divided by average ppg). Summed them up by team year and volia!

Obviously, the weakest part of this assessment is the predicted games, but analyzing the draft and its impact pre-salary cap isn't as fun. Once I get around to it and tighten it up a bit, I'll probably pop it into a regression to explain team success (lagged 2-3 years of course).

 

[begbeee]

It's generally accepted that the level of play in the NHL has increased over the years; where people differ is in the answer of "how much"?

Some claim that Guy Lafleur would look like a beer leaguer against today's players, while some claim that the effect is minimal.

How much is the effect? What impacts do expansion, the increase of the population pool, the breaking of the color barrier, and other external factors have on the effect?

This is the Ultimate Question of fans' Life, Hockey Universe and Everything. At least mine.

I'm excited and looking forward to see someone find the solution and answer.

 

[bluesfan94]

I would love something like WAR (wins over average replacement) for hockey. There's just so many variables that make that difficult. Doing it with points only wouldn't be hard, but the defensive part would be hard to add in.

 

[Finlandia WOAT]

On HFBoards, there are two commonly accepted theories that are seemingly at odds with each other:

--Elite players produce on their own, regardless of talent, and;

--Players produce more with a (relatively) better supporting cast.

An interesting study would involve looking at "elite players" and just how much their fellow teammates factored into their production, and in turn, how much said players factored into their teammates production.

Perhaps an indicator of an elite player is not how much he and he alone produces, but how much he is able to increase the production of his teammates.

(If this is all a blinding flash of the obvious, then please forgive me)

 

[Bear of Bad News]

I've actually toyed around with this. I 'predicted' career games of draft picks from 1996-2006 then divided that by Michael Schuckers draft value chart (derived for expected career games, http://myslu.stlawu.edu/~msch/sports/Schuckers_NHL_Draft.pdf) then multiplied it by a 'production factor' (ppg divided by average ppg). Summed them up by team year and volia!

Obviously, the weakest part of this assessment is the predicted games, but analyzing the draft and its impact pre-salary cap isn't as fun. Once I get around to it and tighten it up a bit, I'll probably pop it into a regression to explain team success (lagged 2-3 years of course).

Very interesting! I'm going to separate this out into a new thread (I'll leave it here, too).

 

[TheDevilMadeMe]

Traditional adjusted points adjust a player's totals to leaguewide scoring levels. But we know that changes in leaguewide scoring don't affect all players the same. For example, scoring soared leaguewide as the 70s turned into the 80s, but the point totals of first line players increased by a much smaller amount than league offense as a whole. In the late 90s, scoring decreased all-round, but decreased a little less for 1st line players than others.

I would like to see a comprehensive look at the scoring levels of different "types" of players? Separating "first liners" from other forwards is one thing. I'm also interested in the percentage of offense that has come from defensemen and how it has changed over time.

Creating a new kind of "adjusted points" for just first line forwards and another for defensemen (and more for other categories that might be worth studying) would be the final goal.

I know CYM has done some preliminary work with this

 

[Czech Your Math]

Can we quantify the standards currently being used by Hall of Fame voters? Have those standards changed over time?

For a player playing today, is there an accurate way to estimate their change of being inducted? (Note that this is a different question than whether or not they deserve it).

This is what Pnep has done with HHOF Monitor Points. I'm guessing he is using arbitrary, intuitive values. I think it could be fine-tuned using linear regression.

It's generally accepted that the level of play in the NHL has increased over the years; where people differ is in the answer of "how much"?

Some claim that Guy Lafleur would look like a beer leaguer against today's players, while some claim that the effect is minimal.

How much is the effect? What impacts do expansion, the increase of the population pool, the breaking of the color barrier, and other external factors have on the effect?

For comparing the ability of players to score points in different seasons/eras, I recommend looking at my study "Improving Adjusted Scoring." Besides possible improvements to this study (as I've suggested), one could look at goalies using similar methods, but the sample would be much smaller and the influence of team factors may be larger.

Essentially, it's looking at major aspects of league quality using a similar methodology to league equivalency studies (e.g. AHL or KHL vs. NHL, etc.). My study focuses on top line forwards (and offensive d-men), due to more equal opportunity (ice time, PP time, etc.), large sample, and applicability when comparing these players to one another.

Short answer: Players from the period between initial expansion and the WHA merger ("the 70s") had a substantially easier time scoring adjusted points than after WHA merger. Lafleur would not be as dominant at his peak compared to his competition today, but he would still be very close to (or at) the top in many more recent seasons.

 

[TheDevilMadeMe]

Related to expansion, I would be very interested in a study of how much the numbers of a player were helped by playing for an O6 team after expansion and how much playing for an expansion team hurt

Edit: TCG did a little of this in his "Questioning Ed Giacomin" post

http://brodeurisafraud.blogspot.com/2010/10/questioning-ed-giacomin.html?m=1

 

[Czech Your Math]

How much do NHL teams make transactions with their level of competition in mind?

For example, if I'm a team in a division where I expect the division leader to have 115 points, do I make transactions differently than if I expect the division leader to have 100 points?

This seems to be the case. I believe Bill James looked at this effect (AL East?) and concluded such, but I may be remembering incorrectly.

What team statistics have the best predictive capability for overall season performance? Can such statistics indicate under/overvalued playoff teams?

This would be very interesting and I've thought it would be another candidate for multivariable regression.

 

[Czech Your Math]

Related to expansion, I would be very interested in a study of how much the numbers of a player were helped by playing for an O6 team after expansion and how much playing for an expansion team hurt

I think it would vary by player, team, season, etc.

It's definitely a large effect. Consider that in the O6, those 6 teams have a total GF/GA ratio of 1.00, but that after expansion:

O6 teams (total GF/total GA):
1968 1.27
1969 1.34
1970 1.28
1971 1.36
1972 1.41
1973 1.33
1974 1.40
1975 1.28
1976 1.06
1977 1.14
1978 1.20
1979 1.06
1980 1.09
1981 0.99

So while the advantage of the O6 peaked in the early-mid 70s, due to the repeated expansions, it took a long time for that advantage to be fully neutralized.

I would love something like WAR (wins over average replacement) for hockey. There's just so many variables that make that difficult. Doing it with points only wouldn't be hard, but the defensive part would be hard to add in.

This is a great idea. The difficulty is fairly determining replacement level. There is a thread I started just for that purpose. I think it would be easiest to determine for goalies, then forwards and much more difficult for defensemen. The metrics for d-men are so much less definitive, which complicates any study of their value.

 

[Polansky]

I think that faceoff winning percentage is the dumbest stat since a lot of time, a player wins the faceoff but for whatever reason the other team gets possession. I think that there should be a stat, clean wins, clean losses and draws. Also, I believe that location of win is important, where does the puck go. Each faceoff dot could be broken down into six or eight pies and we could keep track of which quadrant each faceoff goes to and from which faceoff dot it happened.

 

[Hynh]

What team statistics have the best predictive capability for overall season performance? Can such statistics indicate under/overvalued playoff teams?

Goal Differential and Clear Victory, while not particularly fancy, are good proxies. Both indicate that Florida was quite overvalued while LA was quite undervalued. They also indicate that Montreal got screwed.

 

[Buttonwood]

I think HFBoards actually has the power to turn people's opinions into a quantifiable data. Imagine a game day thread that acted like a stock market: you would buy and sell a player depending on performance. With enough opinions (crowdsourcing) you would move toward the 'right' value of performance for that player over a game and over a year.

This is very pie in the sky, I'll put down more details later. But it would be an interesting undertaking.

 

[bluesfan94]

The problem with that is that as fans, our opinions are very biased.

 

[Lonewolfe2015]

I've compiled a list of the selke votes by year since the lockout in excel (minus 04-05, only found the top 20 vote getters) and have been slowly adding stats per year to see if their might be any sort of statistical correlation between Selke success over the years.

Was wondering if anyone might have some ideas for how I can analyze the data better once it's done (probably will take awhile longer, getting burnt out with excel atm).

 

[Czech Your Math]

I've compiled a list of the selke votes by year since the lockout in excel (minus 04-05, only found the top 20 vote getters) and have been slowly adding stats per year to see if their might be any sort of statistical correlation between Selke success over the years.

Was wondering if anyone might have some ideas for how I can analyze the data better once it's done (probably will take awhile longer, getting burnt out with excel atm).

On the "Past Studies" sticky thread in this forum, Hockey Outsider has linked to studies he's done on Hart & Norris voting. I would recommend looking at those for ideas on how to analyze Selke voting.

 

[Lonewolfe2015]

On the "Past Studies" sticky thread in this forum, Hockey Outsider has linked to studies he's done on Hart & Norris voting. I would recommend looking at those for ideas on how to analyze Selke voting.

Those studies appear to no longer exist, the web links to the analysis' are deadends.

 

[Jumptheshark]

This is a great idea for a board

 

[Czech Your Math]

Those studies appear to no longer exist, the web links to the analysis' are deadends.

Sorry about that.

There are various ways I see to convert the voting into %s, in the case of your study "Selke shares."

First, you can use the maximum points one player can get as the standard. So if there are 60 voters and 5 points for 1st, the max would be 300. Then convert each player's voting points into a % share for that season.

Second, you can use total available points (or a constant % of such) as the standard. So, if it's 5-3-1 for 1st-2nd-3rd, and there are 60 voters, then the total points available would be 540. Then convert each player's voting points into a % share for that season.

Another way is to use the total points of the first place finisher as the standard. This has the disadvantage of varying from season to season, depending on the % of max points the first place finisher receives.

There are a couple of factors to consider for either/both method(s). If the voting point system changes (more places and/or different number of points), this will affect each system differently and affect various players (high finishers, low finishers) differently. You aren't bound to the actual point system used and could assign different values to the 1st, 2nd, etc. votes than those actually assigned by the award. Also, using total points available and/or possibly adjusting by a constant proportional to the total points avaiable on each ballot will affect various players differently. On a 5-3-1 ballot, the most a player can get is 5/9 or 55.6% of the total points. On a 3-2-1 ballot, the most a player can get is 3/6 or 50% of the total points.

Just realize that there is no simple solution, but that whatever method you use will affect the results. You might tinker with various ways and see which way(s) seem best.