Maybe this is absurd, but I’ve never honestly checked the accuracy of my projections. It’s partly because I have placed a lot of trust in a computer that runs regressions with reliable data I have supplied, but it’s mostly because I originally started doing this for my own sake. I used to rely on ESPN’s projections, but the former journalist in me started to realize: it has a customer to please, and the customer may not be pleased, for example, if he sees Corey Kluber ranked in the Top 60 starting pitchers for 2014. (At this point, I am giving ESPN an out, given that everyone at FanGraphs and elsewhere knew the kind of upside he possessed.) Kluber is not the issue, however; the issue is that although ESPN (probably) wants to do its best, it also does not want to alienate its readers who, given its enormous audience, are more likely to be less statistically-inclined than FanGraphs’ faction of die-hards.
In sum: I started doing this because I no longer trusted projections put forth by popular media outlets.
So I didn’t really care how every single projection turned out. I wanted to find the players I thought were undervalued. For three years, it has largely worked in my rotisserie league. (Honestly, I am a complete mess when I enter a snake draft.)
Anyway. All of that is no longer. I quickly sampled 2014’s qualified pitchers — 88 in all — to investigate who panned out and who didn’t. I will ignore wins because they are pretty difficult to project with accuracy; I’m more concerned about ERA, WHIP and K’s.
Here is a nifty table that quickly summarizes what would have been tedious to transcribe. You will see a lot of repeat offenders, which should come as no surprise. At least there is some semblance of a pattern for the misses: I underestimated unknown quantities (and aces, who all decided to set the world ablaze in 2014) and overestimated guys in their decline. There isn’t much of a pattern to the guys I got right. Just thank mathematics and intuition for that.
Here would be a shortlist of my most accurate projections from last year, measured by me using the eye test:
Name: 2014 projected stats (actual stats)
Nathan Eovaldi: 5 W, 3.82 ERA, 1.32 WHIP, 6.4 K/9 (6 W, 4.37 ERA, 1.33 WHIP, 6/4 K/9)
R.A. Dickey: 11 W, 3.84 ERA, 1.24 WHIP, 7.2 K/9 (14 W, 3.71 ERA, 1.23 WHIP, 7.2 K/9)
Alex Cobb: 12 W, 3.49 ERA, 1.17 WHIP, 8.1 K/9 (10 W, 2.87 ERA, 1.14 WHIP, 8.1 K/9)
Hiroki Kuroda: 11 W, 3.60 ERA, 1.18 WHIP, 6.7 K/9 (11 W, 3.71 ERA, 1.14 WHIP, 6.6 K/9)
John Lackey: 10 W, 3.67 ERA, 1.25 WHIP, 7.5 K/9 (14 W, 3.82 ERA, 1.28 WHIP, 7.5 K/9)
Kyle Lohse: 9 W, 3.60 ERA, 1.17 WHIP, 6.1 K/9 (13 W, 3.54 ERA, 1.15 WHIP, 6.4 K/9)
If it brings consolation to the reader, I have since tightened the part of the projection system that predicts win totals. I’m not gonna lie, it was pretty primitive last year because I thought it’s already a crapshoot to begin with. Obviously, it shows, even in the small sample above. It’s still difficult given the volatility inherent in the category, but the formulas are now precise.