:format(webp)/cdn.vox-cdn.com/photo_images/7112530/145297271.jpg)
ed. It's been awhile since we've dusted off the ol' Counting Rocks column, but after seeing some of the stuff that user ChacinIsTheFuture has been turning out lately on xBABIP and such, it seemed like a great opportunity to see what other numerical ponderings have been drifting through our friend's head. Enjoy!
Last night, we saw another wonderful performance by Christian Friedrich. This was the type of performance that we all hoped that the former #1 Rockies prospect would produce on a consistent basis. Of course most of those hopes were born back in 2009 when he struck out 159 batters in 119 ⅔ innings. Since then he spent two years in AA ball dashing our hopes and plummeting to his latest ranking of #17 on the Purple Row purp list.
Friedrich then spent this last off-season getting back into shape at the behest of Dan O'Dowd, as well as getting some pitching instruction by Cliff Lee. Since then, he's been doing everything possible to help make us believe again. Christian started to show a glimmer of his old self in spring training with 10 strikeouts in 8 innings pitched with only 1 walk before being assigned to AAA. In AAA. Friedrich only continued to impress posting a 27:4 strikeout to walk ratio while averaging 6 IP in his 5 starts before being called up to the majors.
Since being called up, Friedrich has had four road starts ranging from average to very good, and two home starts where it seem he’s done his part to help spread the rumor that humidor got unplugged. However, when putting all of it together, it’s still looking like a pretty good start to his hopefully long and successful MLB career.
To delve deeper into Christian’s start, and how good it has really been, I want to first look into a very useful and helpful pitching stat called FIP (Fielding Independent Pitching). FIP tries to determine a pitches actual performance by looking at the four outcomes that a pitcher directly affects; HR ,K, BB, and HBP. By assigning a value to each of these outcomes and then adding in a constant to normalize it to look more like an ERA, which it is trying to predict, you end up with a formula that looks like this: FIP = ((13*HR)+(3*(BB+HBP-IBB))-(2*K))/IP + C, where C is the constant that adjusts FIP so that league average FIP is the same as league average ERA.
So here's the Rockies FIP for all pitchers with at least 10 innings along with their actual ERA.
| Name | FIP | ERA |
| Adam Ottavino | 1.40 | 0.69 |
| Rafael Betancourt | 3.07 | 2.25 |
| Josh Roenicke | 4.03 | 2.27 |
| Matt Belisle | 2.24 | 2.43 |
| Matt Reynolds | 4.76 | 4.43 |
| Christian Friedrich | 2.57 | 4.50 |
| Drew Pomeranz | 4.49 | 4.70 |
| Rex Brothers | 2.61 | 5.19 |
| Juan Nicasio | 3.91 | 5.28 |
| Jeremy Guthrie | 6.14 | 5.48 |
| Alex White | 4.91 | 5.60 |
| Esmil Rogers | 3.90 | 7.20 |
| Jhoulys Chacin | 6.99 | 7.30 |
Once you have obtained a pitcher's FIP, Fangraphs recommends using the following scale to then evaluate the pitcher.
| Rating | FIP |
| Excellent | 2.90 |
| Great | 3.25 |
| Above Average | 3.75 |
| Average | 4.00 |
| Below Average | 4.20 |
| Poor | 4.50 |
| Awful | 5.00 |
Looking at those numbers, and comparing them to the chart above, a couple of things stand out. The Rockies have a pretty good bullpen, with 3 of them in the excellent range, and 1 of them in the great range. Also, you see that Friedrich’s FIP is a stellar 2.57 which puts him firmly in the Excellent area, Coors Field starts included.
Now to break it down further, I want to look at why each players FIP was where it was by seeing what their individual stats looked like, The key to having a good FIP is high K rate and low BB, HBP, and HR rates. One quick note, my rates will probably not be identical to the rates that you'll see on Fangraphs or Baseball-Reference due to the fact that I remove IBB not just from BB totals, which is commonly done but also from the Batters Faced total. It makes a small difference but for me, it is more statistically true of a player's performance.
| Name | K% | BB% | HBP% | HR% |
| Adam Ottavino | 30.6% | 4.1% | 2.0% | 0.0% |
| Rafael Betancourt | 26.7% | 4.0% | 0.0% | 2.7% |
| Josh Roenicke | 16.5% | 15.0% | 0.8% | 0.8% |
| Matt Belisle | 19.0% | 2.4% | 0.8% | 0.8% |
| Matt Reynolds | 26.0% | 5.2% | 0.0% | 5.2% |
| Christian Friedrich | 22.9% | 6.4% | 0.0% | 1.3% |
| Drew Pomeranz | 18.7% | 13.1% | 0.9% | 1.9% |
| Rex Brothers | 27.1% | 12.9% | 0.0% | 0.0% |
| Juan Nicasio | 21.1% | 8.2% | 0.4% | 2.7% |
| Jeremy Guthris | 8.8% | 7.9% | 0.9% | 4.6% |
| Alex White | 12.8% | 7.7% | 1.3% | 3.2% |
| Esmil Rogers | 23.8% | 12.3% | 0.8% | 1.6% |
| Jhoulys Chacin | 18.5% | 12.6% | 1.7% | 5.9% |
Now these numbers really don't mean a whole lot without context so the next thing I did was compare them to the league average numbers of 19.7% K %, 7.8% BB%, 0.8% HBP% and 2.6% HR%. To do this I simply subtracted each players percentage from the league average mark.
| Name | NetK% | NetBB% | NetHBP% | NetHR% |
| Adam Ottavino | 10.9% | -3.7% | 1.2% | -2.6% |
| Rafael Betancourt | 7.0% | -3.8% | -0.8% | 0.1% |
| Josh Roenicke | -3.2% | 7.2% | 0.0% | -1.8% |
| Matt Belisle | -0.7% | -5.4% | 0.0% | -1.8% |
| Matt Reynolds | 6.3% | -2.6% | -0.8% | 2.6% |
| Christian Friedrich | 3.2% | -1.4% | -0.8% | -1.3% |
| Drew Pomeranz | -1.0% | 5.3% | 0.1% | -0.7% |
| Rex Brothers | 7.4% | 5.1% | -0.8% | -2.6% |
| Juan Nicasio | 1.4% | 0.4% | -0.4% | 0.1% |
| Jeremy Guthris | -10.9% | 0.1% | 0.1% | 2.0% |
| Alex White | -6.9% | -0.1% | 0.5% | 0.6% |
| Esmil Rogers | 4.1% | 4.5% | 0.0% | -1.0% |
| Jhoulys Chacin | -1.2% | 4.8% | 0.9% | 3.3% |
Now Ideally you want a positive number in the first column and a negative in the next three. This would tell us that a pitcher has an above average K rate, while also allowing fewer BB HBP and HR then the average pitcher. While there are quite a few players close to having all those covered, the only person on that list that has all four is Christian Friedrich.
So what is causing Friedrich's ERA to be so much higher then his FIP? Well a .367 BABIP against seems to be the biggest culprit. So I did a little digging into Friedrich's xBABIP, and it is surprisingly close to his actual BABIP at .381. This is mainly due to his 34.3% LD rate. So going forward, if Friedrich can limit turn more of those line drives into ground balls we could be seeing the beginning of something special.