Yesterday I began a series in which I look at the best pitches of the 2014 season, and then how that seems to translate to success. The first edition featured fastballs, and that revealed that almost all of the best fastballs in the league belong to some of the better pitchers in the league.

You can head over to that one for a more in depth description of the process and what I am looking for, but it is essentially exactly what it sounds like. I take the top 10 of each pitch type by Fangraphs pitch value ratings, i.e. wFB, and see how the players who feature those offerings fare by both ERA- and FIP- (the “minus” means that it is park adjusted, and placed on a scale in which 100 is league average, and lower is better). This edition will feature curveballs, and with that said, here are the results:

Though we are only looking at the top 10 anyway, it is important to note that there are far fewer pitchers who throw cutters than who throw normal fastballs, and the total sample drops from 88 in the first post to 34 here. And with that, we have fewer elite cutters in the league than fastballs, with just one cutter topping 15 runs compared to five fastballs, and the 10th spot on the list being worth 2.3 runs versus 13.7 on the fastball list (though they both belong to Ian Kennedy).

Again though, somewhat similarly to the fastball list, we see plenty of top-flight starters who feature some of the better cutters in the league. Adam Wainwright heads the list with 22.4 runs, and he is a bonafide ace. His 66 ERA- ranks tied for 6th in the league, and his FIP- of 79 ranks 12th in the league.

There is quite the drop to the 2nd spot where we see AL Cy Young winner Corey Kluber at 13.6 runs, about 9 runs below Waino. But we see similar — actually better — results, with an equal 66 ERA- and even better 64 FIP- which placed him 2nd in baseball and 1st on this list. I mentioned in the fastball article that Kluber had a bad fastball, but that he uses his cutter in that capacity anyway. We see that here, and in that he throws it almost 30% of the time.

The leader here in ERA- at 61 is Johnny Cueto, who featured the best fastball and comes in here at 7th with a 4.7 run cutter. Lester is a close 2nd at 63 ERA- while featuring a 6.6 run cutter.

Ian Kennedy appears here in addition to the fastball list, but he isn’t the outlier he was in the fastball study. His 105 ERA- is the worst on the list, but Scott Feldman and Jake Peavy are close in that regard, and those two along with Bud Norris are pretty far worse by FIP-. In fact, it seems that elite cutters belong to guys with low ERAs more than they do to elite FIPs, though there are certainly some of the latter as well. I think that makes some sense anyway, as cutters tend to induce weak contact (run prevention) more than they lead to strikeouts (an aspect of FIP), though that could be some confirmation bias as well.

All in all, the list has an average ERA- of 82, 18% better than league average and within 1% of guys like James Shields (on the list) and Tyson Ross. The average FIP- is 89, not quite as great but still 11% better than the league and in line with guys like Madison Bumgarner and Alex Wood.

For comparison, the fastball group averaged 72 (ERA) and 76 (FIP), meaning those with top notch fastballs tended to be better than those with top flight cutters. It is an interesting comparison, though, as guys use cutters in different ways. As I said, Kluber likely uses his cutter at times when most would go to the regular fastball, while others (like Cueto) use it as a secondary/out pitch in addition to a great fastball.

But it is much more specialized as well, in that only 34 qualified starters out of 88 are credited with throwing a cutter, as I said before, whereas everyone throws a fastball. Normally we think of cutters as a subset of the fastball family, but I’m wondering if it doesn’t end up falling in line with secondary stuff like breaking balls in terms of it’s relation to ERA- and FIP-.

I also ran a regression analysis between wCT and ERA- and FIP-, as I did with the fastball group, to determine the r (correlation) and r^2 (the fit of the model, how well the variance in one variable is explained by the other) values of the set. For ERA-, the model carries a 59% correlation, and a 0.35 r^2, both slightly better than what was returned in the fastball study. FIP- featured a 49% correlation and 0.24 r^2, both slightly lower than that of the fastball/FIP test.

That could lend some credence to my theory that cutters help with run prevention more than they do with FIP-type numbers. Plus we again have to remember the sample is much smaller, and that pitchers don’t generally throw cutters if they don’t feel helpful, which could add some upward bias to the results that we could see with all of the secondary pitches. Why continue to throw a pitch if it isn’t working?

I will keep all of this in mind, and continue to make some individual comparisons in future pieces, as well as a final analysis once every pitch has been analyzed. Thanks for reading, and check back again tomorrow and through next week for the continuation of this series, as I will do my best to pump one of these out a day until the end, provided school picking back up doesn’t throw me off too much.