Archive for management

Kyle Hendricks and Location-Based Contact Management

This month last year, Connor Kurcon of Six Man Rotation set out to quantify the location aspect of command (or “LRP”). By establishing an accounting system that credited and debited pitchers for changes in ball-strike counts based on the attack zone of and hitter’s disposition (take? swing? ball in play?) for every pitch, he effectively created an alternative to Pitch Value (PVal) that rewards optimal movement through ball-strike counts but with much more pitcher and hitter context.

His findings are as you’d expect: Jacob deGrom and Justin Verlander lead the pack, with Gerrit Cole, Max Scherzer, and Clayton Kershaw not far behind. Other budding aces like Jack Flaherty and Mike Clevinger pepper the list, and some pleasant surprises (such as Brendan McKay, Caleb Smith, and, for those still thirsting, Jake Odorizzi) are scattered throughout as well. Out of the bullpen, newly anointed relief ace Nick Anderson led the pack followed by the underrated Emilio Pagán, breakout reliever Giovanny Gallegos, and others.

Near the end of his post, Kurcon includes a subhead dedicated to Kyle Hendricks where he highlights how Hendricks, widely respected as a command artist, fares lukewarmly by measure of LRP. He then reminds us “LRP doesn’t paint the full picture of command.” True that.

Fortunately, Kurcon has left the door open for me to tie up loose ends with find Gs I’ve been meaning to write up for a couple of months now. Never fear, Hendricks is the command artist we know and love — it’s just that he relies heavily on incurring contact in optimal pitch locations. It is a needle very few pitchers can thread, but Hendricks does it masterfully.

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Contact Management Is and Is Not a Myth

If there were ever a baseball question that keeps me up it night, it’s this: how do the physical properties of pitches affect batted ball outcomes? Many researchers have tackled the subject with varying degrees of success and elucidation. My attempts have focused primarily on a pitch’s ability to generate swinging strikes and ground balls, the first of which used pitcher-level PITCHf/x data while the more recent of which used individual pitch-level Statcast data.

While modeling whiffs and grounders is interesting (and important, too), something strikes me as much more compelling and confounding: the relationship, if any, between a pitch’s physical properties and its batted ball outcomes, whether described as exit velocity, launch angle, or total base-run value allowed, as measured by weighted on-base average (wOBA) or even expected wOBA (xwOBA).

The ability to prove “contact management” as a legitimate and shared pitcher skill has long eluded the Sabermetric community. Assumptions of a league-average batting average on balls in play (BABIP) and, for xFIP, home runs per fly ball (HR/FB) pervade the common ERA estimators (FIP, xFIP, SIERA) we use to gauge talent and assign value. Those assumptions regarding BABIP and HR/FB imply a pitcher’s inability to control them — and there isn’t much evidence to suggest otherwise.

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Roark and Hendricks: Kings of Contact Management

If you follow me on Twitter, you know how this ends. Statistically speaking, though, you probably don’t follow on me Twitter, so you probably don’t know how this ends. Then again, maybe you really do know how this ends, because when you clicked this link, you probably had to read the title first. Or maybe you didn’t! Honestly, I don’t want to pigeonhole you. Maybe you’re the kind of person who clicks links all willy nilly with zero regard for content. I’m sure SEO folks love you but also lose their minds trying to understand you.

No matter. Let’s pretend you didn’t read the title. Now you’re presented with blind résumés. Can you guess who Players A and B are?

Blind Résumés
Name IP GS W K/9 BB/9 GB% PU%* Soft% Med% Hard% xFIP WAR
Player A 104.2 17 8 7.65 2.49 52.2% 3.5% 25.8% 51.0% 23.3% 3.86 2.2
Player B 124.2 19 9 7.65 2.60 52.5% 1.1% 26.5% 50.0% 23.5% 3.67 2.7
*pop-up rate (PU%) = FB% * IFFB%

Did you have to cheat? It may actually be more difficult than you thought. You know the names already, but perhaps you got them out of order: Kyle Hendricks is Player A and Tanner Roark is Player B. But look at that! Hendricks and Roark are almost perfectly identical within every metric. Roark even edges Hendricks in xFIP, innings per start, and WAR per start. It’s kind of a big deal, given Hendricks is owned in more Yahoo! leagues than Roark (85% to 78%).

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