The Most Interesting NL Rebuilder: Colorado Rockies

Over the past two weeks, we’ve been taking a look at some of the most interesting teams in baseball – one contender and one rebuilder from each league. What makes a team “interesting”? Taking advantage of the extreme nature of its ballpark, for a couple of clubs. Bucking some of the game’s most prevalent current trends and having success, for another. Or almost completely breaking from every pattern displayed in a club’s fairly successful recent past. In this final installment, let’s look at our NL rebuilder, the Colorado Rockies, who finally may have developed a concrete plan of attacking their ever-present conundrum – how on earth does one build a winning ballclub in Coors Field?

THE COORS FIELD EFFECT

Before one can develop a battle plan for success at Coors Field, the impact of playing half of your games one mile above sea level needs to be quantified and understood. Utilizing granular batted ball data, I have compiled park factors by comparing actual batted-ball outcomes to projected ones, by assuming that each ballpark’s actual batted ball mix was converted into singles, doubles, triples and homers at MLB average rates. As you might expect, Coors Field ranked as the most hitter-friendly park in baseball in 2013, with an overall park factor of 127.8. This obviously was not a one-year phenomenon, as they also led in 2012 with a 130.8 park factor. If you break it down by batted-ball type, Coors was the most hitter-friendly park for fly balls (176.4), the third most for line drives (109.6) and even the third most for ground balls (114.1). It has the fifth highest park factor for singles (104), the second highest for doubles (120), the fourth highest for triples (145), and the second highest for homers (133). It is not only the sole ballpark to inflate all four types of base hits, it inflates them all by at least a full standard deviation. This isn’t just about homers – Coors mounts a full frontal assault on pitchers and their sometimes delicate sensibilities. Obviously, however, the biggest driver of a park’s run-scoring environment is its effect on fly balls, so that’s where we’ll take a closer look at the data.

FLYBALL PARK FACTORS
ADJ FOR BIP SPD/ANGLE
2013	ACT AVG	ACT SLG	PRJ AVG	PRJ SLG	PARK FCT
COL	0.335	0.829	0.256	0.617	176.4
BOS	0.342	0.858	0.273	0.707	151.1
SD	0.282	0.744	0.254	0.614	136.7
MIL	0.305	0.806	0.275	0.698	129.3
BAL	0.314	0.870	0.292	0.759	124.9
NYY	0.270	0.736	0.264	0.658	116.5
MIN	0.285	0.725	0.264	0.672	116.3
NYM	0.272	0.686	0.260	0.631	114.7
CWS	0.269	0.751	0.270	0.672	114.3
CIN	0.280	0.790	0.279	0.735	109.6
CUB	0.284	0.770	0.279	0.729	108.2
TEX	0.271	0.713	0.270	0.694	103.7
LAD	0.259	0.657	0.260	0.642	102.7
DET	0.286	0.731	0.281	0.726	102.3
HOU	0.310	0.877	0.313	0.873	99.8
TOR	0.294	0.844	0.304	0.829	99.8
TB	0.284	0.753	0.291	0.768	95.9
OAK	0.251	0.666	0.264	0.685	92.9
CLE	0.294	0.792	0.303	0.828	92.4
PHL	0.319	0.859	0.322	0.913	91.9
WAS	0.273	0.698	0.287	0.745	88.8
LAA	0.292	0.771	0.307	0.854	84.9
AZ	0.284	0.745	0.302	0.833	83.2
ATL	0.303	0.768	0.326	0.902	77.7
STL	0.249	0.620	0.278	0.719	76.5
MIA	0.243	0.569	0.269	0.669	76.1
PIT	0.261	0.641	0.286	0.757	76.1
SF	0.261	0.626	0.283	0.744	76.0
SEA	0.283	0.757	0.323	0.913	71.8
KC	0.254	0.615	0.291	0.755	70.0
MLB	0.284	0.743	0.284	0.743	100.0

The above table shows actual fly ball production in all 30 MLB parks compared to projected fly ball production using batted-ball data in the manner previously described. There’s some interesting info here – while Coors inflates fly ball offense the most, there was more actual fly ball production in four other MLB parks last year – Boston, Houston, Baltimore and Philadelphia. The ball was struck with much more authority in those parks last season – in fact, in every park with the exception of San Diego, the ball was struck with greater average authority than in Coors Field. That speaks well of the Rockies’ pitching staff – more on them later – but not as well of its position players.

Coors is generous on fly balls hit to all fields. Breaking the field into sectors reduces the sample sizes used in determining the park factors, increasing their year-to-year volatility, but it’s still very illuminating information. From left to right, the 2013 sector-specific Coors Field fly ball park factors break down like this: LF = 118.6 (sixth in baseball), LCF = 166.3 (third), CF = 149.9 (fifth), RCF = 287.9 (first), and 150.4 (second). Only two other parks inflate fly ball production to all five sectors (Milwaukee and Baltimore), and no other park ranks among the 10 most hitter-friendly in each sector, or is at least one-half of a standard deviation more hitter-friendly than the MLB average in each sector. The 2013 RCF park factor is no fluke, by the way – it was a very similar 276.9 in 2012. In the air, there is no escape for pitchers in Coors Field. Which leads us to…..

A PITCHING PHILOSOPHY THAT JUST MIGHT WORK

For most of their history, the Rockies have followed a similar script. Score a ton of runs, give up more. Fool yourself into thinking you have a better offense than you do – more on that later, as well – and watch the pitching wither as the season progresses and attrition sets in. The rules state that you can only have 25 active players on the roster, and 13 would seem to be about the absolute maximum number of pitchers you can carry. More hits and runs mean more batters faced, more pitches, more pitching changes, more attrition. If there is no escape in the air for pitchers in Coors Field, they must either miss a lot of bats, or keep it on the ground to prevent runs with any degree of success. Last season, the Rockies got to work on the latter.

The raw traditional numbers say that the Rockies’ pitching was poor in 2013 – their 4.44 staff ERA ranked last in the NL, and 28th overall, ahead of only the Twins and Astros. The batted-ball data, however, tells a far different story. The Rockies’ staff ranked sixth in the NL (and in MLB) in contact management. They did this despite allowing the second most line drives (960) in the major leagues. They ranked so high because they induced the second most ground balls (2026) in baseball – over two standard deviations higher than the MLB average – and allowed the third weakest fly ball contact in the majors.

Though they managed contact very well, their overall projected 2013 pitching rank drops to 18th overall and 10th in the NL once the K’s and BB’s are added back to the equation. Their staff total of 1064 strikeouts ranked 29th in baseball, and last by a mile in the league in which pitchers get to bat. They obviously need to improve their bat-missing for their pitching plan to fully evolve, but help is on the way, as we shall soon see.

Let’s take a closer look at the outcome frequency and batted-ball production data for some of the key members of the Rockies’ starting rotation to see how they fit into the new and improved pitching plan.

FREQ
Chacin	%	REL	PCT
K	15.9%	80	10
BB	7.7%	98	55
POP	6.1%	79	27
FLY	25.4%	90	29
LD	23.5%	111	83
GB	44.9%	105	62
—	—	—	—
Chatwood	%	REL	PCT
K	14.4%	73	7
BB	9.0%	117	74
POP	3.7%	49	8
FLY	23.1%	83	11
LD	17.7%	82	2
GB	55.4%	129	97
—	—	—	—
De La Rosa	%	REL	PCT
K	16.3%	82	15
BB	9.0%	114	83
POP	5.6%	72	20
FLY	23.3%	82	3
LD	24.9%	117	97
GB	46.2%	108	78

PROD
Chacin	AVG	OBP	SLG	REL PRD	ADJ PRD	ACT ERA	CALC ERA	TRU ERA
FLY	0.255		0.582	69	48
LD	0.669		0.937	110	104
GB	0.196		0.203	66	101
ALL BIP	0.303		0.447	83	84
ALL PA	0.252	0.308	0.372	91	92	3.47	3.54	3.57
—	—	—	—
Chatwood	AVG	OBP	SLG	REL PRD	ADJ PRD	ACT ERA	CALC ERA	TRU ERA
FLY	0.309		0.617	87	73
LD	0.694		0.839	104	104
GB	0.227		0.242	91	92
ALL BIP	0.324		0.429	86	82
ALL PA	0.274	0.338	0.363	100	96	3.15	3.88	3.71
—	—	—	—
De La Rosa	AVG	OBP	SLG	REL PRD	ADJ PRD	ACT ERA	CALC ERA	TRU ERA
FLY	0.325		0.700	104	91
LD	0.664		0.813	96	99
GB	0.189		0.218	67	103
ALL BIP	0.318		0.453	89	98
ALL PA	0.263	0.328	0.375	99	107	3.49	3.84	4.16

First, let’s look at Chacin, most likely the club’s “ace” at this point, despite a shoulder injury that will delay the start of his season. He has been a consistent ground ball generator throughout his relatively brief career, fitting the club’s preferred starting pitcher mold. He not only minimizes quantity of fly ball contact, he also minimizes damage done in the air, as he runs a high weak fly ball rate, almost a must in Coors, where average fly balls often reach the gaps, and sometimes the seats. Posting an actual relative production figure of 69 while pitching half of your games in Coors is amazing – adjusted for context, it plunges even further to 48. His line drive rates have swung wildly from season to season, from a percentile rank as low as 4 in 2011 to as high as 98 in 2010.

Chances are that his 2013 line drive percentile rank of 83 will regress downward this season. His K rate has also fluctuated wildly throughout his career – his stuff is simply too good for his K rate percentile rank to remain as low (at 10) as it was last season. Chacin’s adjusted relative production on all BIP – the best measure of contact management ability – of 84 is quite good, though it creeps up to 92 when the K’s and BB’s are added back. His 2013 “tru” ERA of 3.57 approximates his actual 3.47 mark. He’s not a true ace, but should be a solid #2-3 starter with upside if healthy.

Tyler Chatwood takes things to extremes. His K rate is beyond miniscule, with a 2013 percentile rank of 7 that is in line with career norms. On the plus side, he generates tons of grounders (97 percentile rank), a true skill. The quantity of ground ball contact is impressive, but so is the quality – he has a high weak grounder rate, leading to a solid adjusted relative production figure on grounders of 92. Like Chacin, he kept fly ball damage under control (87 actual relative production, 73 adjusted for context). On the flip side, Chatwood’s line drive rate was off-the-charts low in 2013 (2 percentile rank), and is likely to regress closer to his 2011 percentile rank of 82 in this category.

With an adjusted relative production figure on all BIP of 82 – even better than Chacin – Chatwood ran in some pretty fast company last season, matching Andrew Cashner‘s mark and besting Madison Bumgarner’s 83 figure. Add the K’s and BB’s back, however, and it shoots up to 96, with a “tru” ERA of 3.71, over a half-run higher than his actual mark. His 2013 actual performance is about as good as it gets for an extreme-low K guy in an extreme-high run-scoring environment. There’s a nice foundation here, but Chatwood has zero margin for error unless he begins to miss more bats.

Jorge De La Rosa missed the bulk of both 2011 and 2012 following Tommy John surgery. He returned in 2013 with all but one component of his typically ground-ball centric repertoire. What was missing – the strikeouts – was a very important missing piece. He allows extremely few fly balls (3 percentile rank), and many of the ones he does allow aren’t hit very well. His ground ball percentile rank of 78 is in line with career norms. Like Chacin and Chatwood, his line drive rate has pinballed up and down throughout his career, and he should expect regression downward from his 2013 percentile rank of 97. Overall, De La Rosa’s contact management skills were about average last season (98 adjusted relative production on all BIP), but once the K’s and BB’s are added back, it jumps to 107, and a “tru” ERA of 4.16, 0.67 above his actual 2013 mark.

This is where we stumble upon another piece of the Rockies’ overall run prevention strategy. Step 1 – Induce a bunch of ground balls. Step 2 – Assemble an exceptional infield defense to convert them into outs. For each of these pitchers, look at the difference between the ground ball “REL PRD” and “ADJ PRD” columns. The “REL PRD” column measures actual production on ground balls to the MLB average scaled to 100, while the “ADJ PRD” adjusts that figure for team defense, ballpark, luck, etc., to isolate the pitcher’s true ability. For all three pitchers, the “REL PRD” column, measuring actual performance, is significantly lower. We’ve already noted that Coors Field inflates even ground ball offense, but the Rockies’ 2013 infield defense was so good that it outweighed that inflationary effect and then some, helping these pitchers to perform better when allowing ground balls than they “should have” based on their respective batted ball mixes.

In fact, utilizing the granular batted ball data, the Rockies out-defended their opponents on ground balls by the sixth largest margin in MLB last year. Their left side of Nolan Arenado and Troy Tulowitzki in particular should keep them at or near the top of infield defense rankings for the foreseeable future, continuing to enable these ground ball pitchers to continue to “play up” above their true talent levels.

To improve their overall run prevention ability to a level befitting a contender, a lot more missed bats are going to be required. To that end, another ground ball guy, Brett Anderson, will be added to the mix this season, and he has shown better bat-missing ability than the holdovers from 2013. Jordan Lyles, acquired from the Astros this offseason, is in the short-term rotation mix, and also has a strong track record of keeping the ball on the ground, though he isn’t a bat-misser. Not too far down the road is the arrival of youngsters Jonathan Gray and Eddie Butler, who both have combined high strikeout rates with ground ball inducement skill throughout their respective college and minor league careers. These two have ace potential, and could push the current starters down a notch or two in the pecking order, making the unit as a whole significantly more dangerous. Before too long, the words “Colorado Rockies” and “run prevention” may no longer be mutually exclusive. Now, let’s look at the other side of the coin.

WHAT ABOUT THE OFFENSE?

As mentioned earlier, due to the Coors Field effect, the Rockies are typically perceived as good offensive club with bad pitching. In 2013, at least, the exact opposite was true. Based on granular batted ball data, their offensive contact quality ranked 26th in baseball, and 13th in the NL. This, despite hitting the third most line drives (973) in baseball. Only the White Sox hit the ball with less authority on fly balls, and only the Brewers hit the ball more weakly on the ground.

It is very easy to be fooled by the relatively high raw offensive numbers the Rockies typically post as a team, and think that they are a more talented offensive club than they truly are. In 2013, the Rockies ranked first in the NL in batting average (.270), fourth in OBP (.323), first in SLG (.418), and second in runs scored (706). Despite all of this they were, based on the granular batted ball data, unequivocally a bad offensive club. As we did with the pitchers, let’s take a look at some of the Rockies’ key position players’ outcome frequencies and batted-ball production data to get a better feel for their true talent.

FREQ
Arenado	%	REL	PCT
K	14.0%	70	19
BB	4.5%	57	8
POP	8.3%	106	56
FLY	27.1%	96	38
LD	23.4%	110	72
GB	41.2%	97	46
—	—	—	—
Cuddyer	%	REL	PCT
K	18.5%	93	55
BB	8.5%	108	62
POP	3.2%	41	8
FLY	29.0%	102	55
LD	19.3%	91	16
GB	48.5%	114	82
—	—	—	—
Gonzalez	%	REL	PCT
K	27.1%	136	93
BB	9.4%	119	71
POP	10.6%	136	88
FLY	25.9%	91	31
LD	28.5%	134	99
GB	35.0%	82	7
—	—	—	—
Rosario	%	REL	PCT
K	23.4%	118	80
BB	3.2%	41	1
POP	6.6%	85	37
FLY	31.2%	110	72
LD	23.7%	111	78
GB	38.4%	90	27
—	—	—	—
Tulowitzki	%	REL	PCT
K	16.6%	83	41
BB	11.1%	141	84
POP	9.0%	115	69
FLY	30.8%	109	69
LD	20.4%	96	35
GB	39.8%	93	35

PROD
Arenado	AVG	OBP	SLG	REL PRD	ADJ PRD
FLY	0.252		0.640	76	67
LD	0.698		0.927	114	94
GB	0.201		0.213	71	85
ALL BIP	0.310		0.471	90	83
ALL PA	0.264	0.297	0.402	94	87
—	—	—	—	—	—
Cuddyer	AVG	OBP	SLG	REL PRD	ADJ PRD
FLY	0.370		1.019	181	139
LD	0.806		1.014	145	106
GB	0.330		0.368	198	102
ALL BIP	0.414		0.656	166	113
ALL PA	0.329	0.387	0.521	158	113
—	—	—	—	—	—
Gonzalez	AVG	OBP	SLG	REL PRD	ADJ PRD
FLY	0.471		1.485	348	212
LD	0.747		1.267	164	112
GB	0.280		0.323	147	89
ALL BIP	0.424		0.833	217	142
ALL PA	0.297	0.363	0.584	164	114
—	—	—	—	—	—
Rosario	AVG	OBP	SLG	REL PRD	ADJ PRD
FLY	0.385		1.029	189	118
LD	0.671		0.924	109	116
GB	0.281		0.281	132	121
ALL BIP	0.384		0.639	150	128
ALL PA	0.291	0.314	0.484	118	102
—	—	—	—	—	—
Tulowitzki	AVG	OBP	SLG	REL PRD	ADJ PRD
FLY	0.400		1.182	231	185
LD	0.630		0.795	89	103
GB	0.324		0.352	187	138
ALL BIP	0.377		0.656	151	133
ALL PA	0.306	0.384	0.532	160	143

We’ll start with the easy one – Troy Tulowitzki, He’s just really…..good. He’s had the same frequency profile for years – strong K/BB ratio, fairly high popup rate (69 percentile rank in 2013, over 60 in his last five qualifying seasons, fairly low line drive rate (35 percentile rank in 2013, below MLB average last four qualifying seasons). His hard fly and grounder rates are solid, but just as importantly, his soft fly and grounder rates are very low. And, hey, he’s a shortstop, where his powerful skill set is virtually nonexistent. Coors takes this very good offensive player (143 adjusted relative production) and turns him into a great one (160 actual relative production).

Next let’s look at the other three veterans of the group, Michael Cuddyer, Carlos Gonzalez and Wilin Rosario. The first won the NL batting title in 2013, the second is considered one of the game’s premier hitters, and the third has hit 49 homers and slugged over .500 – as a catcher – in the last two years combined. Offensive stalwarts, right? Not exactly. They all have their true strengths – Cuddyer rarely pops up (8 percentile rank in 2013), a rarity for a hitter with power. Gonzalez led MLB regulars in line drive rate last season (99 percentile rank), a real skill of his, as his line drive percentile rank has been 87 or higher in three of the last four seasons. Rosario hit a lot of line drives in 2013 (79 percentile rank), which is likely to regress, but hit them really hard, which isn’t.

They all do some things poorly, also. Cuddyer’s line drive rate (16 percentile rank in 2013) has been below average in four of the last five seasons. Gonzalez has a massive K rate (93 percentile rank in 2013), and his popup rate spiked upward to 88 last season. Rosario never, ever walks (1 percentile rank in 2013). Truth be told, these three are in the average range as players, but are made to look much better than that by their home park.

If you took every batted ball hit by Michael Cuddyer over the last six seasons and placed them in a neutral context, with singles, doubles, triples and homers generated at MLB-average rates for his batted ball mix, you would have:

2008 = .284-.361-.434
2009 = .268-.335-.454
2010 = .278-.343-.437
2011 = .276-.339-.430
2012 = .264-.321-.445
2013 = .268-.331-.445

Ladies and gentlemen, Michael Cuddyer, slightly above average MLB corner outfielder. His production on fly balls (from 139 adjusted relative production to 181 actual relative production) and liners (from 106 to 145) were pumped up, largely by the Coors effect, and he also got incredibly lucky on grounders (198 actual relative production, only 102 after adjustment for context). His adjusted relative production on all BIP was a relatively modest 113, compared to his actual relative production of 166 on all BIP. Gonzalez? Remember that 287.9 fly ball park factor to RCF? That just happens to be where Gonzalez most often hits the ball. Look at what his home park does to his relative production on fly balls (from 212 adjusted relative production to 348 actual relative production) and liners (112 to 164). He even posted a 147 actual relative production figure on grounders despite an extremely high soft grounder rate, due in large part to his tendency to roll over soft grounders to the pull side.

Gonzalez’ overall batted ball authority supports a 142 adjusted relative production figure – context inflates it to 217. Rosario? He’s Miguel Olivo with a much lower popup rate. His fly ball authority supports a 118 adjusted relative production figure, but Coors helps bump it all the way up to 189. On all BIP, his 128 adjusted relative production figure is inflated to 150 by context, though that obviously takes a major hit once his K’s and BB’s are added in.

How about Arenado? He’s a work in progress, for sure. Both his K and BB rates are low, and he did have a high line drive rate in his 2013 debut (72 percentile rank). He should add strength and hit the ball with the greater authority in the near future – and he’ll need to, based on his batted-ball production table. He showed below average batted-ball authority on all BIP types last season, and didn’t get much of a boost from Coors, only moving from 83 adjusted relative production to 90 actual relative production on all BIP with context added into the equation. Why is that? There is a tipping point, authority-wise, at which the Coors fly ball effect kicks in, and Arenado doesn’t often enough reach that threshold just yet. In 2013, his can-of-corn fly balls turned into slightly better hit cans of corn at Coors.

As described earlier, Rockies’ starting pitchers were effective at keeping much of the fly ball contact they allowed below that tipping point, helping key their success. Every ballpark has a tipping point exit velocity-wise where fly balls suddenly tend to cease being can-of-corn outs, and begin being extra-base hits. Coors’ tipping point is lower than all of the rest. The other four position players discussed above are physically mature men whose average fly ball has met and exceeded that tipping point. Arenado will get there, maybe this season, and he too will likely become an average player that Coors makes look great.

And that’s pretty much what the Rockies need to do offensively – accumulate more average-ish players. They have one true star in Tulo, and the aforementioned average players that appear to be stars. Justin Morneau might not even be an average offensive player at this point, D.J. LeMahieu sure isn’t, and center field is a mess. Plug truly average players into those three spots, and they’re off to the races offensively.

The Colorado Rockies aren’t nearly where they want to be just yet, but for the first time in quite awhile, they appear to have a clear roadmap to show them the way. With any luck, within two years they have the potential to be a well above average run prevention club. Their offense too can be quite good with the elimination of the remaining black holes in their lineup. To finish the job, they must recognize those black holes as such, and not be thrown off course by the blinding magnitude of the Coors’ effect.