Quote:
Originally Posted by footitt
I understand the concept behind this, but I do not know how to execute this in excel. Given that my draft is in 4 hours, I don't think I'm going to have enough time to do this.
I did go ahead though and sort players into groups of similar skill level/draft position. The groups range in size from 48 players. I also have a draft strategy, where each round I am picking from a corresponding group. I have 6 groups for forwards, 2 for defensemen and 2 for goalies.
This strategy gives me a good grasp of which players to look out for, and when. The only really big issue is that they aren't organized by position, so I have to be monitoring that on the fly so that I don't draft 4 centremen and only 1 winger (our league has 2 positions of each C, LW, RW, 4 D, 2 Goalies and 1 Utility spot).
All in all I feel comfortable and confident going into this draft.

I think the main thing is to have a range for each player based on past performance. Using regression for this is probably more trouble than it's worth. However, for future reference, this is how you would construct a time series.
For each player included in the study, do the following:
Let's use Ovechkin's 7 seasons from '06'12 as an example. Calculate the Y variable in the manner you believe is most reliable for your study. For goals, I might suggest using "adjusted GPG." Once you have calculated this, it will be your dependent (Y) variable and should be in one column. So Ovechkin's adjusted GPG for seasons '06'12 might be in cells C1C7. Next, label your independent (X) variables, which will be time lagged from your Y variable. You might label them T1, T2, etc. You would then copy and paste cells C1C6 into cells D2D7 for variable T1. You don't copy cell C7, because that would be his adjusted GPG for 2012, and couldn't be used until at least 2013. You don't copy anything into cell D1, because he has no data before 2006. For variable T2, you would copy cells C1C5 (or D2D6) into cells E3E7. Again, for each season you lag, you would lose one observation (i.e., if only using T1, then lose Y for 2006... if using T2 also, then also lose Y for 2007). You don't want any gaps in your X variables (e.g., having a T1, but no T2), as this will affect your results. If you only used T1 & T2 as X variables, and found T2 to be an insignificant variable, then you would recaulculate the regression only using T1.
Hopes this makes some sense and may be useful to someone.