Archive for tanaka

Pitch modeling isn’t anything particularly unique or groundbreaking. It’s the kind of thing Harry Pavlidis and Jonathan Judge (of Baseball Prospectus) and our once-editor Eno Sarris (now of The Athletic) have investigated for years. I won’t claim to break new ground here. I’m just a nerd who likes testing hypotheses for himself.

Last year, I used velocity and movement, courtesy of PITCHf/x, to model swinging strike and ground ball rates for pitchers. That post was not my best work (easy to say in hindsight), primarily because of limitations with the data. The data, from Baseball Prospectus, was aggregated, such that I couldn’t isolate any single pitch thrown by a pitcher. The advent of Statcast has enabled us to do exactly that, providing publicly accessible hyper-granular pitch-level data and changing how the public sphere of sabermetricians nerd out.

Something I have wanted to do for a long time is refresh my previously-linked analysis, but with (1) Statcast data and (2) a different modeling approach — namely, the use of a probit model rather than a multiple regression model. For most of you, this means nothing. It’s gibberish. I don’t intend to wade too deeply into the weeds of the modeling, lest I disorient or alienate. Mostly, I just want to communicate I think it’s an exciting and different way to answer the everlasting question: how does a pitch’s velocity, movement, and spin rate affect its outcome?

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#3: May 3
#2: April 24
#1: April 13

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It’s episode No. 4 of my Small-Sample Normalization Services, which, in Star Wars terms, means this will be, like… the 3rd-best post of the series? Is that how that works? I know to nothing about what’s believed to be the consensus on the merits of each film. I already regret making this stupid comparison.

Allow me, then, to touch upon (and revisit) some players whose performances through six weeks are worth critiquing. Six weeks is still a considerably small sample when it takes hundreds of plate appearances (or, for ball-in-play metrics, batted balls) for certain standard and advanced metrics to become reliable (or, in common but sometimes misused parlance, “to stabilize”). Check previous posts for the rules, but know that a rating of 1 means Hype City and a rating of 5 means, uh, Alarm City. A 3, therefore, would be neutral.

All graphs pulled prior to yesterday’s games.

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Name: Masahiro Tanaka, NYY SP
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#2: April 24
#1: April 13

If you’ve tuned in before, you know what this is about. If not: the Small-Sample Normalization Service (SSNS) seeks to, ah, normalize a player’s performance in the context of his own previous achievements (or lack thereof). Most of us are human, and our humanity leaves us vulnerable to the biases that cloud rational thought and critical analysis. Such vulnerability is eagerly exploited by the small sample size, never more so than in April. While midseason small samples cower under the cover of hundreds more plate appearances, April performances have no such luxury.

A month’s worth of playing time is certainly more worthwhile to assess than one week’s worth, but 30 innings or 100 plate appearances can still be pretty volatile. Here are a few still-small samples that recently caught my eye.

All graphs pulled prior to yesterday’s games.

Name: Byron Buxton, MIN OF
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