A few weeks ago, I ventured into the topic of whether the 2016 Cleveland Indians’ starting rotation had a chance at breaking the league-adjusted team strikeout rate record held by the 1990 Mets. Those Mets (comprising a front four of Dwight Gooden, David Cone, Frank Viola, and Sid Fernandez) struck out 47% more batters than a 1990 league-average rotation. That was ridiculously good in 1990, and today, it’d be even more incredible were a team able to do it, given the increase in strikeouts league-wide and the expectation that there probably is a ceiling to the strikeout trend. (Because there has to be, right?)

The reason we focused on Cleveland was simple: they almost reached the level of those Mets for a few months in the beginning of the 2015 season. In April and May, they were striking out around 27% of the batters they faced, a mark which nearly approximated the sort of video-game numbers required to match the league-adjusted total of the 1990 Mets. Though they finished first in baseball by striking out 24.2% of batters (which was also the highest strikeout rate for a starting rotation in baseball history), they finished only 41st-best in terms of yearly league-adjusted K rate. Ho-hum. The conclusion of that previous article was, unsurprisingly, that Cleveland would have to outperform their expectations by a sizeable amount to have a chance at the 1990 Mets.

But one of you astute, noble readers was not entirely satisfied with that rational answer. Instead, phoenix2042 challenged us by putting forth a question: what would a starting rotation that could beat that record look like in the modern game? Which 2016 personnel would a team require in order to best a strikeout rate that’s 47% better than the league average? Well, phoenix2042 — and the rest of you wondering readers — this piece aims to answer that question. We will build rotations worthy of a video game, and they will best the 1990 Mets.

First, as before, let’s look at what rotation-wide strikeout rates would be required to break the record in this coming season. I’ve taken the average yearly increase in rotation strikeout rate for each league: over the past 30 years, strikeout rates for starting rotations have increased by about 0.2% per year, on average, and at a slightly higher rate in the past 10 years. Averaging this trend, I calculated the so-called “holy grail” strikeout rate of just over the 1990 Mets (i.e. >47% above league average):

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Dan Straily needed to see a doctor. He wasn’t running a fever or suffering from strep throat; he had a bum shoulder. The symptoms of his malady were decreased velocity and general ineffectiveness. He initiated some independent research, and upon the recommendation of Houston Astros pitching coach Brent Strom and bullpen coach Craig Bjornson, Straily, 27, picked his practitioner.

After sitting in the waiting room that is Triple-A for much of the 2015 season, Straily paid a visit to Driveline Baseball in Seattle, where he met with Kyle Boddy. Boddy — the subject of a recent post here by Eno Sarris — isn’t an M.D., but you can think of him like a pitching doctor. Straily showed up, rattled off his ailments, and named his desired health benchmarks.

Straily told Boddy he needed to get his fastball back to sitting at 92 mph, with the ability to touch 94. That’s where he was when he first came up as an exciting, 23-year-old pitching prospect with Oakland back in 2012. Lately, his fastball had been sitting 89, and he struggled to touch 92 at all, and his effectiveness plummeted. The reason was the shoulder; he needed to get that healthy. And his breaking ball, he told Boddy, needed sharpening up.

Straily’s average fastball velocity by year

Boddy listened to his patient, and ran the preliminary examinations. That meant a trip to the biomechanics lab to analyze Straily’s delivery, and some tests to measure the movement and spin rate on his pitches. The doc came back with good news.

“I brought everything back and I said, ‘You know, your breaking ball is actually fine. I think that problem will go away if you throw 94 and sit 92,’” Boddy said. “And [Straily] said, ‘Alright, perfect.’ So we were on the same page from the get-go.”

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With the start of the regular season just four days away, we find ourselves in the thick of preview season. No matter where you look, it all boils down to the question: what’s 2016 going to look like? At FanGraphs, we’ve just wrapped up our yearly Positional Power Rankings that assess the season through the lens of each position.

As you might have noticed, each team is made up of the sum of these positional projections and they will all start playing together as 30 units in nine-inning contests next week. If you’re into that sort of thing, we offer Playoff Odds that estimates each club’s shot at postseason baseball (explained here).

It’s important to remember, for all the reasons cited in the previous link, that these projected standings are incapable of total precision. In reality, even with a perfect model for individual player projections, you still wouldn’t hit on every team. And we don’t have anything close to a perfect model for individual players. Yet these projections do offer an objective reading of where the teams stand relative to one another based on what we know. They might wind up being wrong, but they’ll be wrong because they’re flawed not because they’re trying to write an interesting narrative.

Despite clear signs of parity, especially in the American League, our projections think only one division is going to be particularly close: the National League East.

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There’s not a lot of footage out there of Garrett Richards throwing a changeup. This is because he would pretty much never throw a changeup, and this is because doing so never made him feel all that comfortable. So, in the past, Richards wouldn’t throw many changeups, but, below, you can see Richards throw a changeup from just a few weeks ago. As a bonus, I’ll also include an additional changeup, thrown on Tuesday.

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Episode 642
Dave Cameron is both (a) the managing editor of FanGraphs and (b) the guest on this particular edition of FanGraphs Audio, during which edition he examines Rusney Castillo‘s role (or lack thereof) with Boston, Hyun-soo Kim‘s role (or lack thereof) with Baltimore, and whether Jesus Montero’s career trajectory suggests that there’s also no such a thing as a hitting prospect (it doesn’t suggest that).

This episode of the program is sponsored by SeatGeek, which site removes both the work and also the hassle from the process of shopping for tickets.

Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 54 min play time.)

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Ben and Sam banter about a listener-suggested expression, then answer listener emails about Albert Pujols’ impact on the Cardinals, rooting against incentive clauses, what a lack of analytics looks like, and more.

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I don’t normally like to re-visit post topics, especially within the same month, but I’m doing this for two reasons. For one, now we have some data. And for two, the Dodgers have been plagued by bad news for much of spring training, so it’s worth spreading a little optimism. Players have been slowed by injuries left and right, but Alex Wood looks like he could be poised for a major rebound season.

This is what I wrote on March 3. The talk back then was about how Wood spent the offseason trying to correct his mechanics, which started slipping from normalcy somewhere around the end of 2014. It didn’t help when Wood later hurt his foot, which caused further mechanical inconsistency as he worked through the ache, but mainly, Wood wanted to get his arm slot back to where it had been. He was never one to pitch over the top, but as his performance declined, Wood’s left arm dropped lower and lower.

About that! In early March, we had Wood’s words. Now that it’s later March, we have Wood on the mound.

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The NCAA Final Four is set, and we’re inside a week until baseball games actually start to mean something. Today, we’ll reach the halfway point of our ball-in-play-based analysis of 2015 starting pitcher performance. Yesterday, it was the NL Central. Now, the NL West.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Starting Pitcher BIP Profiles – NL West

Name	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	ADJ C	K %	BB %	ERA –	FIP –	TRU –
Kershaw	84.91	89.47	83.07	2.7%	25.5%	21.8%	50.0%	88	33.8%	4.7%	55	51	56
Greinke	87.78	91.04	86.02	3.1%	29.8%	19.1%	48.0%	76	23.7%	4.7%	43	71	64
Bumgarner	87.46	90.80	85.46	4.3%	31.3%	22.7%	41.7%	92	26.9%	4.5%	75	74	70
T.Ross	87.79	90.13	86.55	2.0%	17.9%	18.6%	61.5%	79	25.8%	10.2%	84	76	73
Ch.Anderson	88.52	91.59	87.00	3.6%	30.8%	23.6%	42.0%	94	17.3%	6.3%	110	106	94
Bettis	88.06	92.09	85.67	1.4%	27.1%	22.2%	49.3%	95	19.5%	8.4%	108	99	95
R.Ray	90.50	91.77	90.24	2.2%	32.4%	22.2%	43.3%	105	21.8%	9.0%	90	91	99
Heston	89.25	92.84	86.64	2.5%	23.5%	21.0%	53.0%	99	18.9%	8.6%	101	103	100
Shields	89.69	93.14	86.52	3.5%	30.8%	20.8%	44.9%	117	25.1%	9.4%	100	114	101
Cashner	88.79	92.08	87.09	2.7%	27.2%	22.7%	47.4%	106	20.5%	8.2%	111	99	101
Kennedy	89.73	92.44	87.13	3.0%	35.7%	22.8%	38.5%	121	24.4%	7.3%	110	116	101
B.Anderson	88.98	93.65	86.70	0.4%	18.1%	15.2%	66.3%	98	15.5%	6.1%	95	101	102
Bolsinger	88.41	91.70	86.79	1.3%	27.8%	17.8%	53.1%	105	21.0%	9.7%	93	100	102
De La Rosa, J.	86.07	90.84	83.67	1.2%	26.1%	20.7%	52.0%	104	21.1%	10.2%	107	107	103
Despaigne	87.41	90.40	85.69	1.7%	25.4%	22.4%	50.5%	96	12.6%	5.9%	149	122	105
De La Rosa, R.	89.13	90.55	88.30	2.4%	30.4%	18.1%	49.1%	107	18.5%	7.8%	120	123	106
Hellickson	90.14	93.64	87.19	1.5%	35.0%	21.1%	42.4%	112	19.0%	6.8%	118	114	107
Vogelsong	88.34	92.78	85.48	2.1%	34.0%	19.2%	44.7%	104	18.1%	9.7%	120	116	109
Rusin	88.60	92.64	85.62	2.7%	24.5%	20.8%	52.1%	109	14.5%	6.9%	137	121	116
Collmenter	86.12	91.84	79.40	5.2%	34.8%	25.6%	34.5%	121	12.6%	4.8%	97	119	128
Kendrick	89.70	93.37	86.46	2.7%	36.5%	22.0%	38.8%	127	12.7%	7.2%	162	157	140
AVERAGE	88.35	91.85	86.03	2.5%	28.8%	21.0%	47.8%	103	20.2%	7.4%	104	104	99

Most of the column headers are self explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitchers’ Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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