FanGraphs Baseball

Earlier, I asked you to participate in an exercise projecting both next year’s Mookie Betts and next year’s Javier Baez. The idea is that Betts seems representative of a particularly safe prospect, while Baez represents something of a more volatile asset. I promised that I would analyze the results given a sufficient sample size of votes, and, such a sample size has already been achieved. Interestingly, as of right now, there have been three more votes in the Baez poll than in the identical Betts poll. The best possible conclusion is that three FanGraphs readers had their browsers lock up at a most unfortunate time. The worst possible conclusion is chilling indeed.

So I think it’s safe to move forward with a little analysis. Before getting there, I hope you understand that *I* understand that I didn’t conduct this exercise perfectly. Nevermind the wisdom of the exercise in the first place; all my words might’ve biased the voters to some degree. I could’ve written nothing, or I could’ve at least put the polls before the words. But, what’s done is done. Also understand that, while you’re going to see a measure of uncertainty, this is perceived uncertainty, and not actual uncertainty. We can’t know actual uncertainty. We’re just going to go ahead and pretend like what we think is a decent proxy for what actually is. Let’s see how the community feels about Mookie Betts and Javier Baez, for 2015.

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Infielder John McDonald announced his retirement on Wednesday, having recorded at least some manner of major-league playing time in each of the past 16 seasons.

McDonald’s defensive reputation was excellent almost till the end — and the numbers support that narrative. In more than 4000 innings at shortstop, McDonald saved about 19 runs, or about six runs per every 150 games. At second base, he was (unsurprisingly) better, saving roughly 16 runs per every 150 games at that position.

Offensively, his track record was decidedly less impressive. In 2651 plate appearances, McDonald slashed just .233/.273/.323, producing a 56 wRC+. Not poor enough, that, to prevent him from having a legitimate major-league career. That said, McDonald was sort of a human case study designed to test how little offense a team would tolerate from an elite defender.

What’s notable about McDonald’s offensive performance is that, of the two metrics which most directly inform batting average — that is, strikeout rate and BABIP — McDonald constantly posted above-average figures by one of them. Between 1999 and 2014, the league-average strikeout rate was 17.2%. Over that same interval, McDonald produced a 14.3% strikeout rate. Maybe not quite a standard deviation better than the mean, that, but still solidly above average. The trouble for McDonald was his batted-ball outcomes. Over those same 2651 plate appearances — more than an adequate sample for such a thing to become reliable — he produced just a .264 BABIP, rendering him something like a 30 on the 20-80 scale by this particular measure. The frequency with which he made contact, in other words, was never adequate enough to compensate for his lack of power on contact.

McDonald’s curious batting profile has some value for its capacity to provide a better understanding not only of current players — but, perhaps more intriguingly, for prospects. For, inspecting his batted-ball profile, one finds that McDonald consistently produced infield-fly-ball rates (IFFB%) above — which is to say, worse than — league average. Even more relevant to this line of inquiry, McDonald’s IFFB rates were also high relative to all his batted balls. Indeed, both inuition and Steve Saude (writing for Community blog in 2012) tell us that, if IFFB% offers some clues as to a hitter’s BABIP, then IFFB as a percentage of all batted balls offers even more in that way.

Regard, for example, this graph, which plots BABIP vs. IFFB% for all players who recorded more than 5000 plate appearances between 2002 and -14:

And now regard this second graph, which plots BABIP vs. IFFB per Batted Ball for that same population:

The correlation is considerably stronger in the latter instance — and continues to grow stronger in larger samples, too, as the table below illustrates (again, using the sample of hitters from 2002 to -14).

While there are certainly calculators that exist and which offer more sophisticated means by which to estimate a player’s “true talent BABIP,” as it were, it’s striking the degree to which a player’s infield-fly profile reveals his likely BABIPs. This is advantageous, as well, as fly-ball and pop-up rates become reliable in about just 20% of the plate appearances as does BABIP.

As for McDonald himself, one finds that he produced infield flys on 5.8% of his batted balls — a figure which would equate roughly to a .270 BABIP. His career mark, again, was a .264 BABIP.

Finally, for the benefit of the reader, here’s a reference table featuring rough equivalencies between IFFB per batted ball and BABIP:

There is one pitcher out there that throws his changeup over 30% of the time and calls it a ‘heavy sinker.’ Alex Cobb aside, though, we traditionally lump the changeup in with the slider, the curve, the splitter — it’s not a fastball.

And yet, in some really important ways that go beyond movement and leak into usage, the change works like a sinker. In a league where strikeouts rule, the change actually has a strikeout problem.

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Just before the start of the new year, Chris Mitchell published at The Hardball Times some expanded results from his work on KATOH, the name he’s given to a methodology for estimating not only the probability of a particular minor-league prospect graduating to the majors, but also — in this expanded version — for estimating actual WAR thresholds prospects are likely to cross given their minor-league resumes.

Mitchell’s work advances our understanding of which metrics at the minor-league level correlate most highly with major-league success. Below are five questions I asked regarding these most recent findings, and Mitchell’s answers concerning same.

Because you’ll do a better job of it than I would, could you provide a brief explanation of KATOH — in particular, of the variables that most directly inform it?

KATOH aims to answer a series of questions about a minor-league baseball player: “How likely is this player to play at least one game in the majors through age 28?” and “How likely is it that he’ll reach certain performance benchmarks — 4, 6, 8, 12, and 16 WAR — through age 28?” I arrived at these probabilities by running probit regression analyses, which tell us how a variety of inputs influence an outcome that has two possible outcomes. In this case, the variables in question include a player’s age and some of his offensive stats relative to league average: strikeout percentage, walk percentage, isolated slugging, batting average on balls in play, and frequency of stolen base attempts.

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A number of people who are actually good at math have asked before why we don’t present measures of uncertainty, like error bars, when talking about WAR or projections. I’m not in charge of those things, myself, so I can’t give you the official answer, but, they’re difficult numbers to calculate, if they’re possible at all, and many people wouldn’t know how to understand them, and it’s unclear how much those measures would add to the picture anyway. Sometimes you’ll see projections presented in percentiles, like PECOTA, but generally speaking the percentiles can include almost any and all outcomes, so that doesn’t help much. There’s interest in seeing uncertainty, quantitated. It’s challenging, to do it in a meaningful way.

But I want to try something, again with your help. Last week, I ran a bunch of polls, and a few days ago I analyzed the information generated. Here I’d like to take a similar approach. Now, the post was inspired by the transcript of Dave’s Wednesday chat:

12:40
Comment From Curtis
Of all the prospects yet to debut in MLB, who has the highest bust probability in your eyes? Best chance to succeed?
12:41
Dave Cameron: Baez seems to have a very high chance of being nothing. Mookie Betts will be a solid player unless he dies.

Let’s think about Javier Baez and Mookie Betts. Let’s see what we can do to effectively crowdsource their uncertainties.

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Well, the 2015 Hall of Fame voting results rolled in Tuesday, and just about every member of the baseball media has already checked in with his or her opinion. With any luck, I’ll have the last word — at least chronologically. What are we to make of the election of Randy Johnson, Pedro Martinez, John Smoltz and Craig Biggio, and its resulting future impact upon the holdovers? Let’s take a look at some trends, and hone in on a couple of players most significantly impacted by this week’s proceedings. Read the rest of this entry »

Ben and Zachary answer emails about Hall of Fame voting, stats in other sports, baseball with no projections, and more (plus a Play Index about plunkings).

In an appearance on FanGraphs Audio in early 2012, shortly after the latter had been traded to the Yankees in a deal that sent Jesus Montero to Seattle, managing editor Dave Cameron spoke to the possible reasons for then-rookie Michael Pineda’s success in 2011 despite the almost total absence of a changeup. The changeup, or at least some manner of pitch defined more by its vertical than its horizontal movement, is regarded generally as a prerequisite for success as a starting pitcher. By way of example, consider: of 2011’s 94 qualified pitchers, only seven (or, 7.4%) threw their curveball, changeup, and/or splitter a combined 10% or less. One of those seven was R.A. Dickey, a knuckleballer. Another was Alexi Ogando, a reliever throwing in a starter’s role. Other pitchers with other unique circumstances occupied the remainder of the list.

Michael Pineda was a member of that group, as well. Despite throwing either a fastball or slider about 94% of the time in 2011, Pineda produced an excellent rookie season, recording the 11th-best strikeout- and walk-rate differential among that same group of qualifiers, a park-adjusted xFIP 13% better than league average, and the second-best WAR figure among all rookies (including hitters).

Other pitchers had exhibited the ability to survive without some manner of downward-moving pitch, but all of them were attended by an explanation. What, I asked, was Pineda’s? Cameron’s answer: If you throw 95 mph and also feature better-than-average control, you have a larger margin for error than pitchers who don’t have or do those things.

Indeed, Pineda’s combination of velocity and control wasn’t common. In 2011, the league-average walk rate was 8.1%. Only five starting pitchers posted a walk rate lower than that league-average mark while also recording an average fastball velocity of 94.5 mph or better.

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The Yankees re-signed Stephen Drew, for one year and a few million dollars. It’s a questionable deal — nothing was available much beyond what Drew signed for, for a reason — but it at least seems like a better deal than the Royals giving Kendrys Morales two years. And the idea is simple: Drew’s a known capable defender, and he’s hit before, and if he totally busts, he can be dumped with little problem. The Yankees paid for a little insurance, and as recently as 2013 Drew was an above-average player.

But, there was the matter of 2014. The most recent year is the most significant year, and for Stephen Drew, the most recent year was an absolute nightmare. Drew was described in last year’s FanGraphs player profile as a “line-drive hitter”, so this is appropriately discouraging:

Everything cratered. In his most recent season, Stephen Drew sucked, and thanks to Joel Sherman, we were provided the following quote from some anonymous baseball executive:

There was never a time in which he looked like a major league hitter.

Pretty harsh, pretty bleak. Pretty true, or pretty hyperbolic? That much, we can try to investigate. Did 2014 Stephen Drew ever actually look like a major-league hitter?

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