FanGraphs Baseball

Before this post gets published, the White Sox and Orioles will begin a baseball game in Baltimore played before no one. The few scouts in attendance will keep to themselves, and those watching from elsewhere will be unheard. There will probably be birds, and birds are always making noise, but we’re generally pretty good at tuning them out, because they never shut up. Two things, before going further:

(1) Of course, what’s going on in the rest of Baltimore is of far greater significance than what’s going on inside Camden Yards. For every one thought about the baseball game, there ought to be ten million thoughts about the civil unrest, and what it means and what’s to learn. My job, though, is to write about baseball, and so this is a post about baseball. I am qualified to do very few other things.

(2) The game will be played under extraordinary circumstances, but it’s also one game. A sample of one is, for all intents and purposes, no better than a sample of zero, so we’re not going to learn much today. We’d need a few thousand of these to really research and establish some conclusions. The post basically concerns the hypothetical, inspired by what’s taking place.

Home-field advantage exists in all sports. It’s a known thing, to varying degrees. The first thing that occurs to most people, as far as an explanation is concerned, is that the team at home has people yelling in support of it. The team on the road, meanwhile, has people yelling other things at it. The average person prefers support over mean and critical remarks. Now, consider the game in Baltimore. Strip the crowd effect away completely. What could that do? What might we expect of the home-field advantage of a team that plays with no fans?

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The Fringe Five is a weekly regular-season exercise, introduced a couple years ago by the present author, wherein that same author utilizes regressed stats, scouting reports, and also his own fallible intuition to identify and/or continue monitoring the most compelling fringe prospects in all of baseball.

Central to the exercise, of course, is a definition of the word fringe, a term which possesses different connotations for different sorts of readers. For the purposes of the column this year, a fringe prospect (and therefore one eligible for inclusion in the Five) is any rookie-eligible player at High-A or above both (a) absent from the most current iteration of Kiley McDaniel’s top-200 prospect list and (b) not currently playing in the majors. Players appearing on any of McDaniel’s updated prospect lists or, otherwise, selected in the first round of the current season’s amateur draft will also be excluded from eligibility.

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When the Giants watched Pablo Sandoval leave as a free agent this off-season, they didn’t really have any internal replacements ready to take his place at third base. With most of their money allocated towards re-signing pitchers Jake Peavy, Ryan Vogelsong, and Sergio Romo, the team ended up bargain-hunting for a new third baseman. They found their man in Miami, importing Casey McGehee from the Marlins, as they continue to be a franchise that emphasizes hitting for contact; McGehee’s primary calling card as a big leaguer.

Preferring this skill has worked out well for the Giants over the years, leading them to underrated players like Angel Pagan, Marco Scutaro, Joe Panik, and Nori Aoki. While other teams have chased power in an environment where it has become ever more scarce, the Giants have been content to single their way to three World Series titles. So while McGehee is an unconventional third baseman — he hit just four home runs last year — the Giants targeting an underpowered contact hitter shouldn’t have been a huge surprise.

Unfortunately for the Giants, the beginning of McGehee’s career in San Francisco has been a disaster. After an 0-3 performance on Tuesday night, he’s now hitting .160/.207/.255, and if you can believe it, he’s actually been even worse than that line would suggest, because BA/OBP/SLG don’t account for the extra harm that comes from hitting into double plays. And nobody in baseball hits into double plays like Casey McGehee.

McGehee has already hit into eight twin killings this year; no one else has done more than five times, so he leads the league in GIDPs even though he hasn’t actually played enough games to qualify for the batting title yet. That McGehee is leading the double play charge shouldn’t be a huge surprise, however, as he hit into a whopping 31 double plays last year, tied for the eighth highest single season total in Major League history.

McGehee is basically the perfect storm of a double play candidate. He specializes in making contact and hitting ground balls, only unlike most guys who pound the ball into the ground, he’s remarkably slow. McGehee has the batted profile of a leadoff hitter and the foot-speed of a designated hitter; if he comes up with a man on first base and less than two outs, there’s a pretty good chance that two outs are on their way.

So when you take into account the negative value of the extra outs McGehee is making by hitting into double plays — and at FanGraphs, we have a metric called RE24 that does just that — we find that he’s been the very worst offensive player in baseball to date, some 12 runs below a league average performer. That’s kind of remarkable, considering he’s only played in 16 games. While the Giants early-season struggles are not solely McGehee’s fault, no one has done more to single-handedly bring down their team’s ability to score runs than the Giants third baseman.

Unfortunately, the Giants still don’t really have an alternative at third base; that’s why they had to trade for McGehee in the first place. So, the team is probably just going to have to keep running him out there and hope he turns it around, but since he’s going to be in the line-up, they should think about doing something to reduce the likelihood of McGehee threatening Jim Rice’s single-season double play record (36), set back in 1984. Since there’s no real good alternative if they benched him, I’d instead like to suggest something even more radical; make him the leadoff hitter.

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I attended the Oakland A’s game last night, something I do a few dozen times a year, and Sonny Gray happened to be on the mound plying his trade against a talented Anaheim squad. Though he was shaky in the first inning (his career ERA in the first inning is 4.50, so there could be something to that), he settled down to go eight innings with two earned runs, six hits, one walk, and six strikeouts. He was efficient, looked like an ace, and the A’s won the game.

I moved to the Bay Area just before Sonny Gray was getting his first shot in the majors. In relation to how many games I’ve gone to in Oakland, I’ve witnessed an inordinately high number of his starts in person, so I’ve been able to witness how he’s grown and evolved as a pitcher over the past few years. As April comes to an end, I’m confident enough in those changes to finally write about them. To frame our discussion, let’s begin with a chart of Gray’s fastball usage (separated by type), from the time he was promoted in 2013 until now:

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You might not have realized it, but last night’s slate of games had seven teams reach double-digit run totals. Throughout the night I began to notice there were quite a few games with really high run totals. Then last night while watching the Astros pile runs on the Padres into the 9th inning, armed with a laptop and some SQL code, I set out to research how often this many teams score 10 or more runs on a given day.

MLB Game Days with Teams Scoring 10+ Runs

It turns out this hasn’t happened since 2012, and it has only occurred on nine days over the last decade of baseball. One of those days in 2007 had eight teams break double digits, and all of these days occurred on days with 15 games being played. Yesterday, there were only 14 games played due to the postponement of the White Sox – Orioles game. Most of the occurrences happened earlier in the 2000s when the run environment in Major League Baseball was higher. Today’s low run environment makes days like yesterday less and less likely.

This also gives us a chance to look at how a team’s runs per games are distributed to find out how likely it is that one team scores 10 or more runs in a game. They don’t follow a normal, bell-curve distribution, but instead it’s more skewed (and roughly follows a negative binomial distribution). I’ve illustrated the distribution below using actual results. This represents the percentage of games that a single team scores a certain number of runs.

The run scoring is skewed heavily to the right. I’ve called out the percentage (6.9%) of team-games that have had 10 or more runs scored, so they don’t happen very often. I have the box going all the way out to 30 runs, because that’s the record the Rangers set in Baltimore in 2007. Seven teams having 10 or more runs on one day is obviously less frequent than having just one team do it especially when there are only 28 teams playing instead of 30.

For those of you interested in reading more about run distributions, I have already laid out the math behind modeling run and hit distributions with the negative binomial distribution, and Tom Tango has produced a more accurate but slightly more complicated distribution as well.

By framing runs above average on StatCorner, Chris Iannetta was 54th of 78 catchers that caught at least 1000 pitches last year. This year, he’s first. All it took was a little studying. After reading up, a little twist of the butt and a new relaxation technique was enough to change the fortunes of a 32-year-old backstop.

Some credit should go to Hank Conger, really. Because of his exacting manager, and his own inquisitive mind, Conger has spent a lot of time reading up on the best catching techniques. Conger admitted that he’d read all about where Jason Castro said he put his butt in order to give the umpire a better look at lefties.

And Conger made sure Iannetta knew what he knew. “We talked about it a lot,” admitted Iannetta about framing and his former teammate. “We talked about it in the offseason. We texted. We talked about it all spring.”

One of the things they talked about was the positioning that Jeff Sullivan spotted. “I have wider shoulders, so I have to make sure they can see around me,” Iannetta said. “I try to angle my body, I’ve tried angling my body a little.”

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Joc Pederson does not fit the traditional “three true outcomes” profile visually. Listed at 6-foot-one and 185 pounds, Pederson plays center field and is a far cry from the lumbering slugger personified most in Adam Dunn over the past decade. However, Pederson has been a high walk, high strikeout player with decent power throughout his minor league career with some very good comps and that has carried over so far in Major League Baseball in the early part of the season.

Over the past three seasons, Pederson has moved quickly and steadily to the three true outcomes looking at the level where he received the most plate appearances in each season.
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Devised originally in response to a challenge issued by viscount of the internet Rob Neyer, and expanded at the request of nobody, NERD scores represent an attempt to summarize in one number (and on a scale of 0-10) the likely aesthetic appeal or watchability, for the learned fan, of a player or team or game. Read more about the components of and formulae for NERD scores here.

Most Highly Rated Game
Kansas City at Cleveland | 18:10 ET
Ventura (22.0 IP, 90 xFIP-) vs. Salazar (13.0 IP, 61 xFIP-)
This game receives a higher score than Pirates-Cubs by mere decimal points according to the author’s very proprietary and useless methodology. That Yordano Ventura has been removed from each of his last two starts, however, likely adds a layer of intrigue to this contest which widens that gap. Or if not a layer of intrigue, then a shroud of it. And if not a shroud, then perhaps a mantle. And if not a mantle, then a cloak. What one learns more than anything is that, to describe the appeal of this game, some manner of loose-fitting cloth garment is necessary. What else what one learns is that Ventura and Danny Salazar have produced the fourth-highest and absolute highest average fastball velocities, respectively, among all 145 pitchers to have thrown 10 innings as a starter.

Readers’ Preferred Broadcast: Cleveland Radio.

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BABIP is a really hard thing to predict for pitchers. There have been plenty of attempts, sure, but nothing all that conclusive — probably because pitchers have a negligible amount of control over it. So naturally, when I found something that I thought might be able to model and estimate pitcher BABIP to a high degree of accuracy, I was very excited.

My original idea was to figure out the BABIP — as well as other batted ball stats — of individual pitches from details about the pitch itself. Velocity, movement, sequencing, and a multitude of other factors that are within the pitcher’s control play into the likelihood that a pitch will fall for a hit (even if to a very small degree). But much more than all of those, pitch location seems to be the most important factor (as well as one of the easiest to measure).

I got impressively meaningful results by plotting BABIP, GB%, FB%, wOBA on batted balls, and other stats based on horizontal and vertical location of the pitch. So I came up with models to find the probability that any batted ball would fall for a hit with the only inputs being the horizontal and vertical location (the models worked very well). I even gave different pitch types different models, since there were differences between, for example, fastballs and breaking balls. I found the “expected” BABIP of each of each pitcher’s pitches, and then I found the average of all of those expected BABIPs — theoretically, this should be the BABIP that the pitcher should have allowed.

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