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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One might argue that xFIP is slightly misaligned. One might make that argument in blog form, on the website FanGraphs, today, here, now.

One only might argue that xFIP is slightly misaligned because xFIP is commonly understood to serve a purpose distinct from FIP. FIP, aka Fielding Independent Pitching, is calculated as a function of strikeouts, walks, and home runs — that is, outcomes over which fielders bear no influence. The equation that underpins FIP is derived from a linear regression equation intended to resemble ERA, for ease of interpretation. Because it is based exclusively on outcomes, its purpose is more descriptive than predictive. In other word, it finds greater purpose describing what should have happened but not necessarily what will happen.

xFIP, on the other hand, seeks to achieve the inverse. A large swath of evidence exists to suggest home run-to-fly ball rate (HR/FB) for pitchers is incredibly noisy season to season. Sure, certain pitchers might anecdotally buck the norm — apparently, Michael Pineda was born to be a cafeteria lunch lady, serving up meatballs and taters — but, by and large, HR/FB is a fool’s errand to predict. Accordingly, xFIP replaced home runs with expected home runs, by way of multiplying the number of fly balls allowed by a pitcher by the league-average HR/FB, thereby normalizing home run damage, making it, in theory, a better descriptor (and perhaps a better predictor) of pitcher performance over time.

And therein lies the rub, although, if you missed it, you mustn’t be blamed.

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A while back, I brought forward my thoughts on being 100% transparent about my weekly player acquisitions. The article started many discussions, but my thoughts kept coming back to one point. Does it matter that I have some skin in the game? I lucked into a position to provide fantasy content, does it matter if my ideas are successful when there is quite a bit on the line?

Note. For this article, I’ll use tout in place of an industry expert for consistency’s sake. Also, for the game’s participants, I will use owners.

I felt every tout should have some skin in the game and let their results do some of the talking. Some touts may disagree on this take, but I needed to find out and joined a few NFBC leagues. Besides just rostering a team, I felt I needed to be competitive.

Some of this belief may come from my own insecurities. I know I’m not close to the most entertaining personality, so I think my results may give me credibility. I have no idea if this is true.

Part of the belief comes from Ray Dalio’s book, Principles: Life and Work. In one section he states (p. 376):

“Remember that believable opinions are most likely to come from people 1) who have successfully accomplished the thing in question three times and 2) who have great explanations of the cause-effect relationships that lead them to the conclusions.”

So, do I need to be successful in all formats for others to believe me? So many questions.

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Yesterday, I shared and discussed the hitters who were leading in barrels per fly ball + line drive rate. Let’s now take a peek at the bottom dwellers. Of course, there are going to be your typical suspects, but there are some names that are surprising.

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With about a quarter of the season now in the books, let’s dive into some Statcast data. We know that power could ebb and flow each month and we have witnessed time and time again a hitter whose power is down early, but ends up going absolutely bonkers at some point and making up for the slow start. One of the best, most easily calculable metrics is barrels per fly ball + line drive rate. The problem with the barrels per batted ball event on the Statcast leaderboard is that it counts all batted ball types. It therefore ends up punishing ground ball hitters and doubly benefiting fly ball hitters. While that’s fine if you want to project isolated slugging, it’s a flawed metric if we only care about HR/FB rate. Barrels per plate appearance is even worse, as batters are now punished for walks and strikeouts, as those particular times to the plate failed to result in a barrel…well, duh! So Barrels/FB+LD it is.

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On Tuesday, I identified and discussed the hitters who have enjoyed the largest spikes in fly ball rate, firmly entrenching them as new or more senior members of the fly ball revolution. Today, let’s find out which hitters have departed the revolution as their fly ball rates have plummeted.

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