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01-11-2013, 05:30 PM
  #11
Czech Your Math
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Quote:
Originally Posted by footitt View Post
Yes, that's what I have it set as. The problem is when I have 150 players stats over the majority of their career, it doesn't tell me too much when I look at it all at once.

It's goals, assists, powerplay points, and hits. And it's an 8 man league.

What I've figured I'll do is sort it by each 20 players or so of similar ranking, giving me a better look at which players to expect to look for draft round per draft round.

Ya I realize all of that. I just felt that there would be a way to better back up my intuition, and I feel that having some numbers behind it really adds to it.
Perhaps the most useful type of regression would be a time series. Basically, your dependent and independent variables are the same, except the independent variables are time lagged. For instance, for goals:

Y = B0 + M1X1 + M2X2 + ... where X1 is T-1, X2 is T-2

I.e., Y could be goals in 2012, X1 is goals in 2011, X2 is goals in 2010, etc., all for the same player. Another Y would be goals in 2011, with X1 then being goals in 2010, X2 goals in 2009, etc.

You can try different combos, but I would guess doing separate studies for each category would work best. Rather than just use raw goals, using adjusted GPG is probably going to yield more useful coefficients (otherwise variability in games may affect them as much or more than skill level).

I did a quick, simple study as an example, using (when possible) Y seasons of 2008-2012 for each of several players (Crosby, Malkin, Ovechkin, Stamkos, St. Louis, H.Sedin, Kovalchuk, Thornton, and Iginla):

Adjusted Total GPG: Y = .128 + .406*X1 + .33*X2
Adjusted Total APG: Y = .292 + .531*X1 + .078*X2

(Y = per-game metric in Year T, X1 = same metric in Year T-1, X2 = same metric in Year T-2).

The further back you lag the series, the more observations you lose, and the more likely it is that those further lagged variables will be insignificant. Also the Y-intercept (e.g. .128 for GPG in the above example) is going to vary with skill level, so you may have to either group players by general skill level in each category, or not use a Y-intercept.

For GPG, lagged independent variables such as shots/game or Sh% might also be useful.


Last edited by Czech Your Math: 01-11-2013 at 05:36 PM.
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