Author Archive

The Difference Pitching on the Edge Makes

Note: I found some errors in the data. Data below has been corrected, as well as some conclusions — BP

Yesterday, Jeff Zimmerman examined how Tim Lincecum‘s performance has depended to some extent on his ability to pitch to the edges of the plate. Last year, Lincecum was one of the worst starters in the game in terms of the percentage of his pitches thrown to the black. Coincidently (or not so coincidently), Lincecum suffered through his worst season as a professional.

As with many things, Jeff and I happened to be investigating this issue of the edge simultaneously. Of course, we were not the first to dabble in this area. Back in 2009, Dave Allen noted that differences in pitch location–specifically horizontal location–led to differences in BABIP.

Like Dave, I was curious about the overall impact that throwing to the edges–or the black–has on overall performance. My thinking about pitchers throwing to the edges naturally led to some hypotheses:

  1. Throwing a higher percentage of pitches on the edges leads to lower FIP.
  2. Throwing a higher percentage of pitches on the edges leads to lower ERA.
  3. Throwing a higher percentage of pitches on the edges leads to lower BABIP.
  4. Throwing a higher percentage of pitches on the edges is associated with lower four-seam fastball velocity.

I think the first three hypotheses are intuitive, but the last one stems from the idea that as a pitcher ages and loses zip on their fastball they cannot remain successful unless they increase their avoidance of the heart of the strike zone.

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Offensive Volatility and Beating Win Expectancy

Armed with a new measure for offensive volatility (VOL), I wanted to revisit research I conducted  last year about the value of a consistent offense.

In general, the literature has suggested if you’re comparing two similar offenses, the more consistent offense is preferable throughout the season. The reason has to do with the potential advantages a team can gain when they don’t “waste runs” in blow-out victories. The more evenly a team can distribute their runs, the better than chances of winning more games.

I decided to take my new volatility (VOL) metric and apply it to team-level offense to see if it conformed to this general consensus*.

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(Re) Introducing Hitter Volatility

I suspect many researchers and writers have their own white whale or unicorn; an idea or concept that they are always chasing, regardless of how fruitless or costly that search may ultimately be.

My unicorn is the concept of volatility. I spent a large part of my tenure at Beyond the Box Score exploring the topic for both hitters and pitchers. I even looked at the concept in relation to team performance earlier this year at FanGraphs and other outlets.

Essentially, the idea is to understand whether there are appreciable differences in how players distribute their daily performances over the course of a season. For example, if you have two hitters that are roughly equal in terms of overall skill (i.e. both are 25% better offensively than the league average) is there a difference in terms of how much each is likely to vary from their overall performance on a game to game basis? Is one hitter more consistent day in and day out, while the other mixes in phenomenal performances with countless 0-4 days?

My initial work had some problematic issues (as most initial work does), but thanks to some great feedback from readers and colleagues alike I am ready to roll out the new and improved version of Volatility (VOL), starting with hitters.

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Optimizing Batting Orders Across MLB

Of the many topics discussed in The Book is lineup optimization; essentially, the degree to which teams can extract extra runs throughout a season through better lineup construction.

The general consensus seems to be teams don’t do a great job at optimizing lineups. But the gains from proper optimization aren’t that great, anyway.

That being said, I was curious whether there’s evidence for league-wide changes in the ways players are deployed throughout lineups. Given the statistical research in the past few decades, is the league any more in line with setting lineups with the expressed idea of simply avoid outs?

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David Wright Deal a Solid Bet for Mets

Multiple sources are reporting that David Wright and the New York Mets have reached agreement on a contract extension that essentially makes the third baseman a Met for life.

Initial reports have the deal at 7 years/$122 million. This is on top of next year’s $16 million team option, taking the total years and value of the contract  to 8/$138.

It always pays to be skeptical of long-term deals for players on the wrong side of 30, simply because we know — on average — that performance only declines from this point on.

Let’s take a look at how this might play out for the club.

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When You Really Need a Fly Ball

It’s the bottom of the eighth inning. Men are on first and third base, there’s one out and your team is down by one run. The opposing team has one of the best ground-ball pitchers on the hill, and the infield is playing back and is looking for a double play. All you need is a fly ball to tie the game and significantly swing your chances of winning.

So who do you want at the plate?

It’s likely that the opposing manager will either bring in a ground-ball specialist or just tell the pitcher to stay away from pitches that could be hit in the air to the outfield. Knowing who you’d want to hit requires an understanding of what pitches are the most likely to induce a ground ball — and what hitters manage to hit fly balls against those pitches most often.

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The Most Backward Starters in MLB

So much of what makes pitchers effective at the major league level is their ability to keep hitters off-balance. Sure, a 95 mph fastball with movement and a Lord Charles curveball help, but even these physical tools are only as effective as a pitcher’s ability to create uncertainly in the hitters mind from pitch to pitch.

One — admittedly crude — way of looking at this is whether a pitcher throws the type of pitch that’s expected in a given count. Does a pitcher throw fastballs in “fastball counts”, or do they throw off-speed pitches? Pitchers that throw counter to expectations are often said to “pitch backwards”. The Rays’ James Shields is someone that has been referenced as such a pitcher over the past few years.

But exactly how backwards does Shields pitch? And who are some other pitchers that fit into this category?

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David Wright: Swinging Off — But Near — the Black

David Wright experienced a resurgence of sorts in 2012. After four straight outstanding offense seasons, Wright’s offensive production dipped significantly in 2009 — from a 141 wRC+ to 125. In 2011, Wright’s wRC+ declined all the way to 116.

But this year, the old David Wright reappeared and the 29-year-old third basemen posted a 140 wRC+. The Mets, encouraged by Wright’s year at the plate, have not only picked up his 2013 option (which was predictable), but have also continued discussions for a long-term contract extension.

How likely we are to see Wright put up similar numbers in the future is debatable.

Regardless, one thing was clear: Wright was making better decisions at the plate in 2012. And while his plate discipline numbers were positive (e.g. -2.1% O-Swing), the overall change didn’t seem to capture how well Wright’s plate approach improved.

In an effort to tease this out beyond the basic plate discipline metrics, R. J. Anderson used Mike Fast’s “correct” decision-making approach to look at how Wright’s decision-making improved in the past three season. Anderson calculated the percentage of “correct” pitches Wright swung at in 2012, compared to the two previous seasons. He found Wright had improved his decision-making by 7%.

I decided to take an even narrower view than Anderson and focused only on the location of balls Wright swung at that were just off of the plate, or that were off the black.

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Velocity Trends and Pitchers to Watch in 2013

I’ve written quite a bit this year about pitcher aging — specifically, trends in velocity loss for pitchers. There are two general findings that I want to revisit today and apply to pitchers from 2012; the predictive power of velocity loss in July and end of season velocity, and the impact of losing velocity in one season on next season’s velocity.

First, a pitcher’s velocity will tend to vary throughout the year. Trying to get a read on whether a pitcher is having trouble velocity-wise during a season is difficult if you simply compare to last year’s overall velocity. So I compared a pitcher’s velocity in each month to their velocity the previous year in that same month and found that pitchers who lose at least 1 mph of velocity in July are 13.7 times more likely to finish the entire year down at least 1 mph.

Second, 91% of pitchers that do finish a season down at least 1 mph compared to the previous season will lose additional velocity the following season (average decline of 1.6 mph), with only 7% regaining some (but, likely, not all) of that velocity back.

With the close of the 2012 season, I checked back on how well July-over-July velocity trends predicted full season declines as well as which pitchers ended the season losing over 1 mph off of their fastball.

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Pitching Inside: The Best of 2012

One of the oldest truisms in baseball is that, to be successful, pitchers need to pitch inside. Establishing the inside of the plate allows pitchers to more effectively use the outer-half of the plate — and get batters to swing and miss or make weak contact more often on pitches thrown to the outer part of the zone. But it isn’t easy to pitch inside. Pitchers who lack the ability to get away with throwing inside tend to stay away from that part of the plate for fear that hitters will drive those pitches for extra-base hits. This can lead to hitters cheating on outside pitches and can force pitchers to throw fatter pitches as a result of throwing behind in the count.

So who were this year’s best pitchers when it came to throwing inside? I dove into our PITCHf/x data and found out.

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Hitter Aging Curves: Plate Discipline

Jeff Zimmerman and I have done lots of work on player aging curves in the past 12 to 18 months. Jeff started things off with a series of hitter aging curves, which focused mostly on standard outcomes and WAR components. Jeff and I then joined forces this year for a series focused on pitcher aging.

This time around, I wanted to know how a hitter’s plate discipline changes over his career. We already know plate discipline statistics are easily the most stable, year over year. That said, I wondered whether I’d see meaningful patterns as players age. Often times, scouts and commentators mention how a hitter’s approach changes over time: less disciplined, less contact as a young player; better bat control and better strike-zone awareness as a hitter matures. But does the data confirm this thinking?

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Revisiting Last Year’s Free-Agent Signings

Before all our attention is focused on the post-season, I thought I’s take a quick look back at free agent signings from the past year and how those deals worked out in 2012. The focus here is just on what teams got for their money. In other words: Did the players meet or exceed the expected value of the contracts they signed?

I focused on major league signings only, so the analysis does not include myriad minor league deals — many of which resulted in players accumulating playing time in the majors this year.

To get a sense of the how the deals turned out, I compared players’ expected values — which are based on their positions and the annual average value (AAV) of their contracts — to their actual values. I uses Matt Swartz’s research on the differences in dollars per Wins Above Replacement (WAR) by position, rather than assume an average dollar-per-WAR, as is typically done.

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Production Per Swing in 2012

There are rate stats for just about every kind of opportunity a hitter faces in a game. Batting average tells you how often a player reaches base via a hit. On-base percentage tells you how often a player avoids making an out per plate appearance. But what about swings as opportunities?

Last year, I played around with the idea of production per swing. The idea was to examine what hitters gave the most value when they took a swing. The methodology was pretty simple: calculate the Weighted On-base Average (wOBA) each hitter generated using their swings — instead of plate appearances — as the denominator*.

Of course, there is a healthy correlation between actual wOBA and wOBA per swing (.83 in 2012), but less so Isolated Power (ISO). (wOBA/swing and ISO share only a .53 correlation.) Some of the results may not be all that surprising, but many certainly are.

Let’s first look at the top-25 so far this year:

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Andrew Werner Was Pulling the String Last Night

As I was flipping channels last night I managed to stumble on the Padres – Dodgers game. Andrew Werner, a 25 year-old rookie making only his third career start, was on the hill for the Padres. It only took me a few pitches to determine I should stick with the game for a little while.

Although the Dodgers would eventually win the game in extra-innings, Werner pitched a great game as his final line can attest to (6 IP, 8Ks, 1BB). And although he posted an equally dominant performance in his previous game against the Atlanta Braves (6 IP, 7Ks, 0BBs), the way he went about shutting down the Dodgers was quite different.

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Greatest September Call-Ups

We’re only three days from the expansion of major league rosters. On Sept. 1, all players on a team’s 40-man roster will be eligible to play in the big leagues without an accompanying move. Often times, baseball fans are treated to a sneak preview of teams’ top minor league talent as a result of September call-ups; or they’re surprised by a relatively unknown player who manages to contribute over the season’s final month.

In preparation for this year’s roster expansion, I thought it would be interesting to look back at the greatest-ever September call-ups, defined here as players that made their major league debut during the month of September.

There are, of course, two ways to look at this: The first is to look at players — position players and pitchers — who generated the most value for their clubs during their call-up. The second is to look at players whose careers began as a September call-up and then went on to have great careers.

I’m looking at both. Read the rest of this entry »

Slowly Back Away from the Pythag Expectation

Updated: Thanks to the commentors, especially Evan, for double checking my work. I had an issue in Excel that messed up the results for May and June. Charts have been updated.

For most of 2012, the Baltimore Orioles have been playing over their heads. Well, at least when it comes to their expected win-loss record.

Based on the run differential the team has generated, the O’s have amassed 10 more wins than we would expect based on their Pythagorean winning percentage. The team has outplayed its cumulative expected winning percentage throughout the year and — since April — picked up two additional wins at the end of each month. If they sustain this performance and finish August with at least 10 more wins then their Pythagorean winning percentage would predict, they would be just the third team to do so since 2001 (the 2004 Yankees and the 2007 Diamondbacks are the other two).

Some might point to this glaring discrepancy between Baltimore’s actual winning percentage and Pythagorean winning percentage as evidence that the Orioles cannot sustain their winning ways. Of course, this raises the question of whether we really gain anything from a predictive standpoint heading into September if we focus on a team’s expected winning percentage rather than their actual performance.

The answer based on a review of the past decade seems to be no.

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When to Believe Velocity Gain

Last week, I wrote about some findings regarding in-season fastball velocity loss and how experiencing a loss in different months affects a pitcher’s chances of finishing a season with diminished pitch speed. The general takeaway was that June and July were the most telling months.

But what about velocity gain? We know that, generally speaking, pitchers lose velocity more than they gain it. So while velocity loss isn’t good, it’s to be expected — and starting pitchers seem to be able to deal with that loss better than relievers. Pitchers who can stave off velocity loss (year-over-year change between +/- .5 mph) perform even better. Moreover, if a pitcher gains at least 1 mph on their fastball in a season they are twice as likely to maintain some or all of that gain the following year.

Gaining velocity, while not a guarantee of better performance, is certainly a boon to a pitcher and his organization. But given that velocity varies for all sorts of reasons, when can a team have confidence that the increase they’re seeing is real and sustainable?

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At What Point Should We Worry About Velocity Loss?

I’ve written quite a bit this year on trends in pitcher aging, specifically velocity loss and gain. In the last iteration I focused on the odds of pitchers gaining velocity back after a season where their fastball dropped by at least 1 mph.

In that piece I listed a few pitchers to keep your eye on given that their velocity was down from 2011. In June, I wrote about CC Sabathia for ESPN and noted that the big lefty is likely beginning to “age”, as the odds are quite a bit higher that pitchers over the age of 30 do not gain their velocity back once they’ve lost it.

After thinking about it a while it occurred to me that there is of course the chance that these pitchers will gain their velocity back by the end of the year (as I noted in both pieces). We know that, generally speaking, pitchers gain velocity as the season goes on. Temperatures rise, and so too do fastball velocities. If this is the case I wondered at what point in the season we can say with greater certainty that a pitcher is throwing as hard as he is going to throw. Is there a particular month where a velocity decline is more likely to translate to or predict a full season velocity decline?
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Identifying First Half MVP Candidates

With yet another day to go before actual baseball returns to the field, I thought I would take a quick look at some of the potential MVP candidates in both leagues based on the first half of the season.

Identifying MVP candidates is certainly not a straightforward process, nor is the criteria universally agreed upon. Knowing this I will not begin or end this article with any claim to have identified the “proper” candidates. These are my candidates based on my way of looking at the term “valuable”.

So what is my criteria? Well, I like to think of MVPs as players that provide an exceptional amount of production in both an absolute and relative sense. This means identifying players that lead or are close to leading the league in production, but where there is also a sizable gap between their production and that of the second best player on their own team. This means that I do tend to discount great performances by players that happen to share the same uniform as equally great players. Is it their fault? Absolutely not. In fact, those players could likely be the best all around players in the entire league. But when it comes to value I think there is a relative component that should be considered. This isn’t to necessarily give credit to the player (i.e. they don’t “step it up” to make up for the gap in talent on the team), but rather to the performance itself.

Like I said, this is my criteria and I don’t claim that it should trump all others, nor would I say it is complete on it’s own. Rather, I think it’ a useful starting place.

Okay, enough with the preamble. Let’s get to the data.

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Stephen Strasburg, Dazzy Vance and Context

Eric Seidmen wrote an interesting article last Thursday about Atlanta reliever Craig Kimbrel‘s historic strikeout pace. So far, Kimbrel is sporting a blistering 42.7% strikeout rate (K%). Even for a relief pitcher in this era, that’s incredibly impressive. But one person who commented on the story noted that there was a non-reliever approaching the same level of whiff greatness (i.e. > 30% strikeout rate).

Nationals phenom Stephen Strasburg has thrown 182 innings in the big leagues and has struck out 32.5% of the batters he’s faced. No starting pitcher who lasted any significant amount of time ever finished his career with a strikeout rate higher than 30%. The closest  is Randy Johnson and his 28.5% strikeout rate. This season, Strasburg has a 33% strikeout rate. If he were to maintain that pace, he’d be the 10th starting pitcher in history to achieve the feat and would have the 23rd such season since 1916. But take a look at that list and you’ll note that the oldest instance came back in 1984.

The problem we run into with strikeouts — like many statistics in baseball — is that the playing environment has changed over time.

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