Archive for Meta Analysis

On Tuesday, I tried to figure out why Jose Ramirez‘ home run power has disappeared. After much research that led to multiple dead ends, I discovered that his fly ball launch angle (LA) had increased significantly this season to a its highest mark in the Statcast era. I opined that perhaps his fly balls had become overly elevated and were too close to a pop-up than a line drive, driving down his HR/FB rate. This discovery piqued my interest in fly ball LA, so I decided to embark on a not-too-scientific study.

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Yesterday, I analyzed Ketel Marte’s power breakout, identifying what has driven his 20 home run total. Today, we’re going to flip to the sad side of 2019 performance. Jose Ramirez has been one of the season’s biggest busts, after he delivered both elite fantasy and real baseball production over the past two years. Now, nearly half way through the season, he’s sitting with just four dingers. He’s managed to swipe 16 bases, despite a sub-.300 OBP, though, so at least us fantasy owners are getting something for our hefty investment. Let’s find out what happened here.

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On Tuesday, I published the results of my research calculating correlations for various pitching metrics as pitchers transition from the minors to the Majors. Today, we’ll look at the hitters. Just like for pitchers making the jump from the minors to the Majors, hitter correlations are very similar to what Matt Klaassen calculated for MLB batters year-over-year. That may be a surprise, but it does mostly suggest that there’s not as much development from the minors to the Majors as we might expect. A baseball player is who he is, for the most part, on average. Now let’s get to the details.

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Last season at BaseballHQ ($$), I found the expected results for hitters returning from Tommy John surgery (TJS). The research was completed with traditional stats and no StatCast information was utilized. The reason was that only one regular, Christian Vazquez, had Tommy John surgery since there was publicly available StatCast data. That changed when five hitters had the surgery last year. With the return of Didi Gregorius, all have made it to the majors. Sadly the early returns are unspectacular with Corey Seager and Shohei Ohtani struggling.

In his first three seasons, Seager posted a .876 OPS but past two seasons it has only been a .793 OPS. I included last season because he struggled while trying to play through the injury. For Ohtani, he posted a .925 OPS last year and just a .742 OPS this season. These values line up with the original TJS article.

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Yesterday, I identified and discussed 11 starting pitchers significantly outperforming their SIERA marks, suggesting potentially major impending regression. One such pitcher on the list, ranking second in SIERA outperformance, was Mike Soroka. Through 10 starts and 65.1 innings, Soroka has allowed an amazing microscopic HR/FB rate of just 2.9%, which easily leads all qualified pitchers in baseball.

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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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Today’s data dump is brought to you by Paul Sporer and Justin Mason. In a recent Sleeper and the Bust episode, they were frustrated with not knowing how bidding in a 12-team league compared to a 15-teamer. I decided to go ahead and dive into the bidding in the NFBC’s 12-team Online Championship and the 15-team Main Event.

Note: I worked on this article over the weekend and only had this season’s FAAB data up to week 10. By the time the article publishes, week 11 will be available. The results wouldn’t change much with one additional week.

First, here is some background information on the league types. They are exactly identical in these ways:

• 23 total starters, 9 pitchers, 14 hitters
• $1000 in FAAB to spend with no Vickrey bidding and the results run on Sunday evening.
• No $0 bids.
• Seven bench spots.
• Both have a league and overall prize.

As for the differences, one is obvious, one has 12 teams and the other 15 teams. The less obvious one is that the 12-team leagues, on average, start drafting earlier in the preseason. For this reason, MLB teams have more time to churn their rosters and deal with injuries making the first FAAB period a little more aggressive compared to the 15-team leagues.

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Last week, I recommended an improvement for expected fielding independent pitching (xFIP) without dismantling the original FIP framework upon which it was built. FIP describes the relationship between ERA and strikeouts, walks, and home runs allowed; xFIP does the same but attempts to remove the luck component from home runs by multiplying the number of fly balls a pitcher allows by the league-average rate of home runs to fly balls (HR/FB) — the rationale being HR/FB is notoriously fickle to project year to year.

The recommendation: change HR/FB to include line drives (LDs) and exclude infield fly balls (IFFBs, aka pop-ups). It’s worth noting our dark overlord David Appelman once explained how removing pop-ups from aggregate fly balls insignificantly affects xFIP. Additionally, less than 1% of line drives result in home runs. The recommendation, then, seems like the merging of two separate but equally fruitless endeavors, given the facts.

Yet changing the HR/FB component in xFIP to be “HR/(oFB + LD)” substantially improved the metric’s correlation with same-year ERA. Adjusted r², which measure the strength of relationship from 0 to 1, increased from 0.42 to 0.55 using Statcast data (0.44 to 0.53 using FanGraphs data). I hypothesize that, when added to fly balls, line drives (despite resulting in very few home runs) give a more holistic indication of the average contact quality and launch angle a pitcher allows.

Today’s recommendation: account for start/relief splits.

Although I thought of this independently, the idea itself is far from an original one. Read the rest of this entry »

In Tout Wars this year, have been graced with four suspect closers but they’ve caused me to ponder how to handle the “riches”. The issue is that they are not stable and I’d not be surprised if all four were out of a job next month. I polled my Twitter followers to see which one they had the most faith in.

I have the following four closers in @ToutWars. Who do you think will have the most Saves at your season's end?

— Jeff Zimmerman (@jeffwzimmerman) April 24, 2019

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Last Thursday, I identified nine starting pitchers whose Statcast xwOBA marks were significantly better than their actual wOBA marks, suggesting improved results over the rest of the season. Today, let’s discuss the guys who have been the most fortunate according to xwOBA.

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