SubscribeYou may have noticed a recurring theme here is dealing with valuing pitchers relative to hitters. Average draft position starts drafting pitchers in round 2 and doesn’t really get going on them until the fourth round. The Price Guide (as well as several other sources) tends to rank the top-tier pitching higher and advocates grabbing Santana in the first round.
(Since it’s a topic I seem to be stuck on, I have appreciated the comments that have been left on the various pitcher/hitter ADP posts. Lots of stuff has been written that has really made me think about this.)
Anyways, being ever vigilant for evidence that supports one approach or the other, I took a look at how Rotoworld valued pitchers. Here are a few SP dollar values from their magazine, with overall rankings included:
5. Johan Santana – $37 (tied with Pujols)
14. Tim Lincecum – $31
19. Roy Halladay – $30
22. Cole Hamels – $29
26. Brandon Webb – $28
29. CC Sabathia – $28
Not quite as aggressive as the Price Guide, but considerably higher than ADP. Actually, those are probably a good indication of what you would end up paying if you were following the Price Guide and the rest of your league was using ADP.
Related posts:
Unless I know for sure a specific set of dollar values are the result of actual calculations and not just some guy picking numbers, then I ignore them. Do you have any idea if Rotoworld actually calculates the values mathematically or if someone like Matthew Pouliot just takes guesses and slaps a number next to each player?
@Mike: I don’t know for sure how they arrive at their numbers. When Matthew was updating his Rotoworld blog last year, I got the impression that the values were calculated mathematically. He’s seems like a sharp guy, so I’m assuming he’s got a good way to do things.
It seems to me that what happens with your Price Guide is that it naturally allows the players to fall where their value puts them, rather than “forcing” a 60/40 or 70/30 split between hitting and pitching. I’ve seen dollar value calculators online that allow the user to choose the split. I don’t know if your way is better or not. I’m in a non-traditional league (R, RBI, NSB, OBP, SLG for hitters and W, ERA, WHIP, SVS, K/BB for pitchers, with 10 hitting spots and 8 pitching spots), and your Price Guide, using 2008 stats, has 2 pitchers at the very top of the list, ahead of all hitters. The split is 57/43, which is close to the split if you just divide the 10 hitters by 18 total players (55.5%). Also, three of the top four players drafted would be pitchers. This would never happen in my league, so I would adjust for this myself. I realize 10/8 is an unusual league, but even with our setup, owners don’t take pitchers in the first round. It might be entirely possible that they should, though.
Most projection systems will project the top pitchers to be worth more than they really are in relation to the other pitchers.
This results from using the standard deviations of the projections when doing the standard score calculations rather than using the standard deviations of actual season data.
@Molson: But wouldn’t it be incorrect to apply the z scores from season data to the projections, since the projections are on a different scale?
As I understand it, most advanced projections use conservative playing time estimates, so that overall the league AB & IP approach what would be a normal total. Since the projection systems aren’t projecting injuries, this even things out on a league-wide scale.
Of course, I’m still new at understanding all this stuff. Maybe I’m missing something.
@Molson: Just read your comment on one of the other threads, addressing standard deviations. Not sure I understand completely, but I think I understand your point. So then, would we get better results if we instead projected most players to have full seasons of playing time, and then used standard deviations based off of prior seasons’ final statistics?
I still see some problems with this, but would it be better in assessing individual values of players? I guess in fantasy maybe we don’t care that the playing time for the entire player population totals correctly, since we are only concerned with the top 300 or so players?
The idea behind valuation systems such as this price guide is to determine what each stat is worth relative to one another.
Since projections tend to predict more clustering around the mean than actually occurs (read: smaller standard deviations), it will tend to inflate the value of a marginal stat.
If in the last three years, a marginal SB is worth about 1.5 HR, but your projections have a marginal SB worth 1.2 HR, then you’re going to underestimate SBs in your valuation.
@Molson actually in a standard deviation assessment SB are overvalued. Here’s a quote of a pretty smart guy (Rookies and Cream/Cafe)
Essentially, you are calculating z-scores for each category and summing everything up to get a total ranking. The problem is that the z-score method assumes that all categories are normally distributed. If categories are normally distributed, you could then actually convert z-scores to percentiles. For example, a z-score of +2 is at the 98th percentile. However, some categories (e.g., stolen bases and saves) are clearly not normally distributed. Thus, players with very high totals in these categories (i.e., outliers) are going to have total scores that are inflated.