Like my work last year around pitcher aging and velocity decline, I am always looking for reliable indicators or signals of change in players. One thing I’ve been interested in trying to better understand are changes in performance that might signal or herald a large droop-off in performance in the following year.

Projection systems do a very good job of predicting performance, but my thought was there must be some way to better predict the 2011 Adam Dunns of the world.

So, one Saturday morning I decided to do some statistical fishing.

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A small a bigger story sometimes hides behind a bit of information. That bit came in this line I read a few years ago in Larry Tye’s book, Satchel:

In 1960 he [Satchel Paige] threw for the Salina [Kan.] Blue Jays ….

I had no idea. Leroy Robert “Satchel” Paige was arguable one of the best 10-or-so pitchers who played baseball. He was a Hall of Famer on the field, but he was an even better showman. What was one of the greatest players doing playing on a team in Kansas?

I’m a Kansas native. Throughout my life, I’ve had a deep connection with Salina. I lived less than an hour away from the city when I was growing up. Some of my family members still live there. Heck, I was even married there. Because of that, I needed to know what brought Paige to the middle of nowhere to play baseball one summer so long ago.

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Leafing through an old Sports Illustrated, I recently happened upon this stellar article by Mr. Albert Chen entitled “Revenge Of The Base Stealers,” in which Chen analyzed the league’s continued shift towards base-pilfering over base-trotting.

With the whimper-death of the Steroid Era, league strategies have swung towards old-school baseball. Most winning teams now employ some combination of great defense, strong base-runningitudes, and notable pitching-miraculosities. As such, wise teams have found employs for otherwise marginalized speedsters.

The net result has been an uptick in the value of a stolen base, according to linear weights:

This chart shows how the cost of a caught stealing (the red line) is trending towards zero (meaning a caught stealing is costing less — in fact, much than its .400 runs high point in 2000) while the gains from a stolen base (0.161 runs in 2012) have remained strong.

Whither belongs the blame for this change? Simply: Home runs. And where do we wander from here? In short: Deep into the heart of Speedster Kingdom.
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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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Tim Lincecum’s resume contains the following items: 2 time Cy Young award winner, 4 time All-Star and twice World Series Champion. With all the achievements over the last 5 seasons, he was relegated to a long relief once the Giants made the playoffs because he was no longer effective as a starter. Lincecum’s problem is he can no longer just throw the ball across the plate and hope a batter just swings and misses. If he wants any hope of returning to be the starter he once was, he now needs to learn how to pitch.

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The best result from a first pitch? Has to be a dribbler to the mound, right? We spend all this time chasing the swinging strike and drooling on triple-digit velocity, and there’s a future Hall of Fame pitcher who made his living getting first-pitch sawed-off million-hoppers to the second base side — Greg Maddux.

But if you’re not Greg Maddux, the first strike is the nexus for a game of cat and mouse. We’ve found that throwing a first-pitch strike is one of the best ways to get your walk rate down. But if the league throws too many meatballs on 0-0 counts, batters should swing more. It might be the best pitch they see. If the league then throws fewer strikes for the first pitch, batters would find themselves looking more. I don’t know if it’s game theory, but it’s certainly a theory about this game.

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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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Remember when the Playstation 2 came out, and then Sony released a newer, smaller version of the original Playstation, called the PSone? After that, people started calling the original Playstation console the PSX, or Playstation X. Today, we are going back to the original console version of the De-Lucker, so grab your nearest mint copy of Final Fantasy VII and buckle in!

Why DLX?

FanGraphs recently re-did how we calculate wOBA for all the players. In an effort to give base-running its own stand-alone category and run/win value, we reduced wOBA to a hitting-only metric and took out SB and CS. That’s where the problem with the De-Lucker 2.0.

DL 2.0 used the Fielding Independent wOBA formula, which includes stolen bases. In order to keep things parallel, we now must revert back to the Should Hit formula — essentially:

The De-Lucker part comes in when we plop an xBABIP in the place of yonder true BABIP. Jeff Zimmerman and Robert Boden (slash12) have been working on and promoting what I believe is the best xBABIP formula out there, so let us once again use that.

Beneath the jump: More caveats! All sorts of data! Downloadable Excel spreadsheets! Fewer video game references!
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I recently became fascinated with the strike zone and its effective boundaries. The strike zone laid out in baseball’s rule book is simple; it extends a total of 17 inches across the width of home plate, between the hitter’s knee and midsection and covering the entire depth of the plate. The strike zone as it actually gets called by umpires is complex. It shifts quite significantly depending on the handedness of the hitter for one.

That’s not the whole story though, not even close. The dexterity of the hitter isn’t the only significant variable in how the strike zone is called. Compare the following two strike zone heat maps, fitted from 2012 data on called pitches to right-handed hitters.

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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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