Archive for Research

Can Diamondback Jake Lamb Survive?

Knock on wood, I certainly hope so. This piece isn’t about sending a tribute to the area, rather it is a discussion of the composition of the minor leagues and those who reach the major leagues.

While this article became a study of a the California League’s population, the concept began when I was thinking about Jake Lamb‘s prospect status. Lamb signed with the Diamondbacks last June and I stumbled upon him during his first Spring Training with the club — he ranked among the 10 best prospects I saw in Arizona. Intrigued, I followed his injury-riddled season closely and thought he would never garner the attention I believed he deserved because of his old age and collegiate pedigree (though, Hulet ranked him higher than anyone else this off season!).  Suddenly, I found myself buried in Excel attempting to discover what Jake Lamb’s chances were to become a major leaguer.

Statistical studies of prospects are difficult because the minor leagues are vast and rife with variables and failure. There are 189 teams across 16 full-season, short season and rookie leagues, each stocked with talent that may never make a major league 25-man roster. With over 5,000 minor leaguers vying for 750 MLB roster spots it can be easier to study the successes.

Studying only the players who reach the major leagues may be easier, but often such studies snag on “survivorship bias.” Survivorship bias may be present when a study’s population consists of a select group amongst a larger class. If one is going to study success, it’s wise to study failure too. For a demonstration of survivorship bias, read Dave Cameron’s post on The Value of Hunter Pence.

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2013 Disabled List Team Data

The 2013 season was a banner season for players going on the disabled list. The DL was utilized 2,538 times, which was 17 more than the previous 2008 high. In all, players spent 29,504 days on the DL which is 363 days more than in 2007. Today, I take a quick look at the 2013 DL data and how it compares to previous seasons.

To get the DL data, I used MLB’s Transaction data. After wasting too many hours going through the data by hand, I have the completed dataset available for public consumption.  Enjoy it, along with the DL data from previous seasons. Finally, please let me know of any discrepancies so I can make any corrections.

With the data, it is time to create some graphs. As stated previously, the 2013 season set all-time marks in days lost and stints. Graphically, here is how the data has trended since 2002:

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The Josh Johnson Dilemma

Earlier this year, Jack Moore reviewed Josh Johnson‘s inability to get hitters out while pitching from the stretch. Johnson and the Jays were very much aware of the situation, but even still, it did not improve as the season went on. In the end, Johnson limited batters to a .315 wOBA and a .307 BABIP when he worked out of a full wind-up, while opposing batters had a .440 wOBA and a .450 BABIP when Johnson worked out of the stretch. His BABIP while pitching from the stretch was 73 points higher than any other pitcher that made at least 15 starts in 2013.

The simple answer this dramatic split would be to simply point at Johnson’s BABIP and say he was unlucky. If one were to review the video from the first inning of his July 27th start against Houston, one could certainly believe that:
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Austin Brice and the Value of Release Point Repetition

Austin Brice is a legitimate prospect. The Marlins spent $205,000 to sign him out of high school in 2010, and he was ranked as the sixteenth-best farmhand in the Miami organization by Baseball America coming into the 2013 season, an area of prospect lists he will likely to continue to reside in this offseason. He’s just 21, has two pitches that flash plus, and has a prototypical pitcher’s body and smooth, easy, delivery.

He also has 190 career walks in 279 2/3 professional innings, including 82 in 113 frames in 2013. That’s a career 14.88% walk rate and a 15.16% mark in 2013, a number that was actually a step back from 2012 (14.08%) even though he was repeating the Low-A level (his ERA also shot up from 4.35 to 5.73, and his K-rate fell from 25.26% to 20.52%. Certainly, this past season did not bring the young righthander much good news.

Plenty of pitching prospects pair tantalizing stuff with frustrating inabilities to throw strikes, but Brice (whom I saw five different times in 2013, a virtue of living 45 minutes from NewBridge Bank Park) is an especially frustrating case because, as I said above, his delivery is one of his strengths. In this piece, I’m going to examine the root of his control problems and tie it to some more general and important lessons about the process behind throwing strikes.

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The Success Rates of Arizona Fall League All Stars

Players are sent to the Arizona Fall League for all sorts of reasons. The MLB-owned prospect-laden fall league serves as a domestic winter league, and so teams use it as they wish. But once you are selected as an all-star, an AFL Rising Star, you’ve got a unique stamp of approval, something akin to being an all-star in a league of all-stars. And now that the Rising Stars game has been around since 2006, we have some data to see exactly what that selection means for a prospect.

Some teams send players to Arizona because they were injured during the year and need to build up arm strength, innings pitched, or plate appearances. Some teams send players to try out a new position. Some teams send fast-track prospects from the low minors so that they preview what play in the high minors will look like. Some teams send polished picks straight from the college ranks so that they can skip a level on their way to the bigs. Some teams send prospects they might like to trade so that they might look better to future trade partners after some time in the offensive-friendly league. Most teams send players that face the Rule 5 draft if they aren’t moved to the forty-man roster.

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Introducing the Interactive Spray Chart Tool

I’ve been working on an interactive tool that allows you to create spray charts using Game Day data from the past two years for a few weeks now. I’ve always loved the Katron Batted Ball tool, and it’s been a great resource of mine for years. However, I wanted to put something together that was a bit more interactive, allowed for more filtering, and made side-by-side comparisons easier.

Our writers here at FanGraphs have been kind enough to play around with it and offer suggestions. After some tweaks I am ready to officially release the tool into the wild so that anyone can use it.

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A Method for Examining Two-Strike Hitting

Let’s talk about why I love Jamey Carroll. He has had — like most of us would like — his best years after the age of 30; he has played every position except catcher, including an inning of scoreless relief in 2013; he’s short; he spells his name humorously; and he plays a cop in this music video (therabouts of 1:10).

But what impresses me most about him is his rare combination of no power and great plate discipline (as seen here here). There is almost no threat of a homer and only a mild threat of a double when he walks to the plate, but he still induces a walk rate near 10%. Carroll walks more than Robinson Cano and Adrian Gonzalez not because pitchers fear him, but because — as anyone who’s watched Carroll can attest — the 5-foot-11 infielder fights off a half-dozen bad pitches until he finds one he can pop for a single.

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More Fun with Markov: Custom Run Expectancies

Before the season, I put up a three-part series (1, 2, and 3) that explained how linearly-weighted stats like wOBA, while useful for comparing players to each other, don’t necessarily reflect each player’s true contribution to their team’s run scoring.  You see, the weights used to calculate wOBA are based on league averages.  So, for a team with league average breakdowns in walk rate, singles rate, home run rate, etc., wOBA (and its offspring, wRC+) ought to work very well in figuring out how valuable a player is (or would be) to an offense.  However, when it comes to particularly bad or good offenses, or to those with unusual breakdowns, wOBA will lose some of its efficacy.

Why?  There are synergistic effects in offenses to consider.  First of all, if a team gets on base a lot, there will be more team plate appearances to go around, which of course gives its batters more chances to contribute.  Second of all, if the team gets on base a lot, a batter’s hits are generally worth more, because they’ll tend to drive in more runs.  And, of course, once the batter gets on base in such a team, it will be likelier that there will be a hit (or series of hits) to drive him in.  The reverse of all three points is true in a team that rarely gets on base.

But it goes even beyond that.  Let’s say Team A gets on base 40% of the time, and Team B gets on only 20%, but their balances of the ways they get on base are equal (e.g. each hits 7x as many singles as they do HRs) .  A home run is going to be worth something like 14% more to Team A, due to more runners being on base.  However, to Team B, a home run is worth over ten times as much as a walk, whereas to Team A, it’s worth only about 5 times as much.  That’s because Team A has a much better chance of sustaining a rally that will eventually drive in that walked batter.  Team B will be much more reliant on home runs for scoring runs.

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Does the Braves’ Stuff Work in the Playoffs?

“We know we’re going to strike out. That’s just a given with guys who have power. And we have a lot of guys who can hit the ball out of the park. And that kind of goes hand in hand. But you look at some of the studies — and our guys have looked at them — and there’s not a direct correlation with strikeouts and offense.”

— Atlanta general manager Frank Wren, interviewed by Jayson Stark on 2/18/13

This quote comes from Alex Remington’s piece on these very pages back in April. When the Braves finished constructing their roster — a roster similar to what we see now — there were questions as to whether the team would strike out too much to make a run at the postseason. Well, we’ve now reached the postseason, and the Braves are still here. And they’re still striking out too, averaging over 10 Ks a game so far. They also led the NL in home runs, an achievement they were expected to sniff given their lineup. This was kind of the plan from the beginning — strike out a fair amount, but counter that with a good deal of power.

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Freddie Freeman, the Cardinals, and Coming Through When it Counts

A few weeks ago, Dave Cameron wrote a piece on RE24, explaining that, because RE24 measures offensive production with respect to the specific base-out state, one could compare it to a context-neutral offensive metric, such as Batting runs, in order to measure the effects of situational hitting.

Situational hitting is a vague term often used to laud making outs as long as it moves the runner up a base, but as I see it, all the phrase means is hitting differently depending on the situation. That is, good “situational hitting” is distributing your hits and extra base hits into the times that you hit when runners are on base, and especially in scoring position.

Subtracting Batting Runs (or Bat) from RE24 works as a good measure of situational hitting because it compares the value of the context-neutral event (single, strikeout, home run, etc) with the value of the actual change in base-out state. A single is worth more in certain situations; that “more” is measured using this method.

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Pinch Hitting Report Card: Reds Pass, Orioles Fail

Monday night, Rays manager Joe Maddon pinch hit James Loney for right-handed Sean Rodriguez. After a foul knubber to the right, Loney went all walk-off on Tommy Hunter.

But as much as pinch-hit walk-off home runs are the soup of Hollywood executives, they are the rarest of meats in the MLB reality. In fact, pinch hitting is most often a choice between lesser evils — a choice between a bad wOBA or a terrible wOBA.

A closer look at the last five seasons of pinch hitting reveals success has not between distributed evenly, and the effectiveness of of some pinch-hitting efforts may be a product of systematic choices rather just tough breaks.
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Why Strikeouts Secretly Matter for Batters

I got my start at FanGraphs by writing Community Research articles. As you may have noticed, community authors have been very busy this season, cranking out a lot of interesting articles. One that caught my eye the other day was triple_r’s piece on the importance of strikeouts for hitters. The piece correctly pointed out, as other studies have, that there’s basically no correlation between a hitter’s strikeout rate and his overall offensive production. Strikeouts don’t matter; case closed, right? Well, not exactly.

Let me present a hypothetical situation. Say there’s a group of players who go to an “anti-aging” clinic in Florida and pick up some anabolic steroids. Let’s say these hypothetical players are named Bryan Raun, Ralex Odriguez, Tiguel Mejada, Phonny Jeralta, Celson Nruz, and Barry Bon… nevermind. Yet, after using the steroids, it appears that the group of them, on average, has not improved. The steroids didn’t improve their performance, right? But, wait — let’s also say that while visiting Florida, some of them contracted syphilis, which spread to their brains, causing delusions and severely impacting their judgment, strike-zone and otherwise. The players whose brains aren’t syphilis-addled have actually improved quite a bit, but their gains are completely offset by the losses suffered by those whose central nervous systems are raging with syphilis. So, the fact that the steroids actually do improve performance has been completely obscured by another factor that is somewhat — but not necessarily — associated with the steroids.

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First Inning Home Field Advantage

The home team has consistently, on a year-to-year basis, won 54% of its games. Several reasons have been explored for the disparity, such as familiarity to the home field and the umpire’s biased strike zone. Another aspect that comes into play is a first-inning discrepancy in favor of the home teams’ pitchers. They have an abnormally large advantage in strikeout and walk rates, partially because of a higher fastball velocity.

Note: For consistency throughout the article, when I refer to K/BB, it will be in reference to pitchers.

With better use of bullpens and more patient hitters, strikeout and walk rates have climbed in recent years. Since 1950 (the extent of Retrosheet’s data set), the home team has always maintained a higher K/BB ratio than the away team.

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2013′s Top Batteries at Preventing the Running Game

Over the last two months, I have been working on quantifying which of the two battery mates deserves credit — or rather blame — for the running game and the passed ball and wild pitch. Note: It’s not dire that you read those articles to comprehend and enjoy this one.

The main take away from my research is that I have found a pitcher has more statistical correlation, with the caught stealing percentage, wild pitches, and passed balls of a battery, than the catcher. While none of this is revolutionary, it is important to note that neither the pitcher nor the catcher is solely to blame for any outcome in a battery, rather it is a combination of both. However, considering the strong correlations we discovered in the pitchers favor, we can now recognize that conventional wisdom underestimates the impact a pitcher can have on the outcomes of a battery — especially in regards to the running game.

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Relief Pitching in Context

If you recall, last week, I talked about one approach that we can take for evaluating starting pitcher performance. Today, I’d like to continue on that vein, this time taking a look at relief pitching.

With regards to evaluating both player performance and player talent, relief pitching is one of the least understood aspects of baseball. There are a few factors that lead me to believe this, but the only one I’d like to talk about today is the problem of mid-inning pitching changes.

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The Odds of Matt Harvey Breaking Down

Yesterday, it was reported Matt Harvey may need Tommy John surgery because of a torn UCL in his right elbow. Some people may say they saw the injury coming and the Mets were crazy to let him throw over 175 innings this season, but the evidence doesn’t really support those ideas. After looking over the history of other 24-year-olds, it appears that the pitcher’s ability to throw hard and recent small velocity drop were the only identifiable injury indicators.

Myself and others have looked at many indications of a pitchers chances of getting hurt. High increase in innings for a young pitcher (Verducci Effect). Velocity and Zone% drop (PAIN Index). Inconsistency in release points and velocity late in a game. High breaking ball usage. Bad Mechanics. High fastball velocity.

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Simulating the Impact of Pitcher Inconsistency

I thought Matt Hunter’s FanGraphs debut article last week was really interesting.  So interesting, in fact, that I’m going to rip it off right now.  The difference is I’ll be using a Monte Carlo simulator I made for this sort of situation, which I’ll let you play with after you’re done reading (it’s at the bottom).

Matt posed the question of whether inconsistency could be a good thing for a pitcher.  He brought up the example of Jered Weaver vs. Matt Cain in 2012 — two pitchers with nearly identical overall stats, except that Weaver was a lot less consistent.  However, Weaver had a bit of an advantage in Win Probability Added (WPA), Matt points out.  WPA factors in a bunch of things, e.g. how close the game is and how many outs are left in the game when events occur.  Because of that, it’s a pretty noisy stat, heavily influenced by factors the pitcher doesn’t control much.  It’s not a predictive stat.  For that reason, I figured simulations might be fun and enlightening on the subject.  They sort of accomplish the same thing that WPA does, except that they allow you to base conclusions off of a lot more possible conditions and outcomes than you’d see in a handful of starts (i.e., they can help de-noise the situation).

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Matt Moore and Others Likely to Lose Velocity

As some of you might remember from previous articles, velocity trends in July provide the strongest signal in terms of whether a pitcher is likely to experience “true” velocity loss over the course of a full season.

Yes, I know, we are more than halfway through August. However, between work, vacation, and Saber Seminar (which, if you didn’t attend you really missed out. You can still purchase posters and t-shirts, so get on that. It’s for a good cause) I’ve struggled to sit down and run the numbers. Better late than never.

Again, for reference, the table below breaks out the percent of pitchers who experience at least a 1 mph drop in their four-seam fastball velocity in a month relative to that same month a year ago and who also went on to finish the season down a full 1 mph. It also shows the relative risk and odds ratios for each month — meaning, the increased likelihood (or odds) that a pitcher will experience a true velocity loss at season’s end when compared to those pitchers that didn’t lose 1 mph in that month.

Month 1 mph Drop No 1 mph Drop Relative Risk Odds Ratios
April 38% 9% 4.2 6.2
May 47% 6% 7.8 13.9
June 55% 5% 11 23.2
July 56% 4% 14 30.6
August 53% 6% 8.8 17.7

So while the overall rate of velocity loss based on a loss in June and July look pretty even, the relative risk and odds ratios increase by a solid amount in July.

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Exploring the Battery Effect

Today’s article will concern the “battery effect” and its far reaching influences on passed balls and wild pitches. However, before we delve in, I will fill you in on the details of my previous research as a reference point for today’s research.

The “Battery Effect”

The “battery effect” is most easily explained as the relationship between the pitcher and the catcher and how they affect each other. The effect is often subtle, but still significant in the big picture.

Let’s dive into the details. My previous study on battery combinations included investigating which of the two battery mates — the pitcher or the catcher — deserved the credit for catching a runner. The basic take away from this research was, surprisingly, that the pitcher had more of  a profound effect on the caught stealing percent of the battery. To measure this effect I ran a regression of the pitcher’s CS% — caught stealing percentage — on the battery’s CS%, and vice versa for catchers.  

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Finding Value in Pitcher Inconsistency

I’d like to talk to you today about pitcher evaluation.

I don’t mean evaluation in the sense of determining a pitcher’s talent level, or evaluation in the sense of determining a pitcher’s future value — or even evaluation in the sense of determining a pitcher’s market value. I mean a pitcher’s past value. Or, perhaps, because value is so often misunderstood and misinterpreted, we’d be better off speaking in terms of contribution. That’s how do we determine the extent to which a player contributed to his team’s success (or failure)?

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