Archive for pitch

Using my Pitch Leaderboard, I identified every “new” pitch* thrown during MLB’s glorious first weekend. Then, using my Pitch Comps tool, which uses pitch specs (like velocity, spin rate, movement, and release points) to compare pitches to one another, I wanted to see if I could make any quantifiable declarations about the quality of these pitches in small samples. I can’t write about everyone, so I’ll select the most interesting ones (in my humble opinion).

(*Including existing pitches from rookies for whom we now finally have MLB statcast data as well as existing pitches thrown by players who missed all of 2021 due to injury. It will be interesting to see if the latter group looks measurably different post-injury than they did pre-injury.)

At the end of the season, I’ll revisit to find out these comps were actually indicative (i.e., “predictive,” in a sense) of quality, but it’s also strongly possible the comps will change as samples grow. This is very experimental, but it’s something I’ve wanted to try in the past but hadn’t found the motivation to do.

Why pitch comps? I know I am prone to bias watching a handful of pitches from a pitcher. I can’t scout because I can deceive myself into just about any conclusion (and I think the same can be said for most of us, whether we like it or not).

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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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At the beginning of last season, I contextualized the swinging strike rate (SwStr%) (and refreshed those numbers after the season concluded). I had seen other analysts call certain pitches “above-average,” “below-average,” “elite,” etc. using the league-average whiff rate as a baseline. This is neither a criticism nor a judgment, as I absolutely did this before I had my statistically-driven epiphany. But understanding the average four-seamer’s or slider’s or cutter’s whiff rate lends additional context to any assertion one might make about the “elite-ness” of a pitch.

More recently, I wanted to convert discrete outcomes by pitch type into fielding independent pitching (FIP) statistics — namely, FIP and xFIP (expected FIP, which substitutes a pitcher’s rate of home runs per fly ball for the league-average rate). Let me warn you now: the results are very imperfect. It took some brute force on my part to get there, but I got there. I would wager that the the extreme (lowest and highest) values are probably a bit exaggerated. Regardless, it’s an interesting table to ingest:

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Shortly after the onset of last season, I dug into pitch-level statistics to see how much swinging strike rate (SwStr%), ground ball rate (GB%), and isolated power (ISO) varied by pitch type. I felt inspired after analyzing Madison Bumgarner before the 2018 season and noticed his fastball, once elite, was utterly broken after his dirt bike accident. (See his 2018 player caption and this July post in which I followed up MadBum’s lack of progress.) I felt encouraged by the praise the post received from readers and fellow analysts alike for the clarity it provided. I’d like to think it helped move the needle, even if only slightly, in terms of how we evaluate pitchers.

I wanted to refresh the guts of that post for the 2018 season with additional metrics. There’s not much else to discuss; this’ll be short and sweet. (I’ll toss in some gratuitous high-level analysis following these tables.)

Notes:

• All data is courtesy of PITCHf/x via Baseball Prospectus
• All tables present average rates for starting pitchers only
• Due to pitch tracking/stringing not being perfectly precise, the numbers below are highly accurate but not completely so and may not align exactly with FanGraphs’ batted ball data (for example, Baseball Info Solution strings far fewer line drives than does PITCHf/x)
• Click headers to sort!

Batted ball outcomes by pitch:

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