Author Archive

Mock Draft Review: RotoBaller Family and Friends Draft

For the fourth consecutive year, my friends at RotoBaller invited me to participate in the RotoBaller Friends and Family mock draft. The draft room was, in a word, terrifying:

  1. Vlad Sedler, Guru Elite
  2. Nick Mariano, RotoBaller
  3. Pierre Camus, RotoBaller
  4. Todd Zola, Mastersball
  5. Tim Heaney, RotoWire
  6. Heath Cummings, CBS Sports
  7. Howard Bender, Fantasy Alarm
  8. Nando Di Fino, The Athletic
  9. Scott Engel, RotoExperts
  10. Alex Chamberlain, RotoGraphs
  11. Ray Flowers, Guru Elite
  12. Real Talk Raph, RotoBaller

I drew the #10 pick (as shown in the draft order above), immediately understanding I might have a difficult decision to make very early in the draft.

This doesn’t need much preamble, but I do want to say one thing: I maintain that a good way to improve as a drafter (for lack of a better word) is to try something you might not ordinarily try or force yourself into an uncomfortable position you might not normally get into. I embraced this discomfort with my first two picks, assembling a base from building blocks I might not normally use given the options available to me. As you’ll see in my concluding remarks, I think I did pretty well.

Also: we were all on a 30-second clock. I don’t know about everyone else, but I was stressed. My internal monologue was utter chaos.

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Re-Contexualizing SwStr% for Efficiency

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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This is the Definitive Mike Tauchman Hype Post

Mike Tauchman deserves nothing less than the clickbaitiest of headlines. He’s my favorite player nearly no one has heard of or cares about, a name I draft that genuinely forces people to Google his name, a Triple-A hitter not only too old to be a prospect but also maybe too old to be a post-hype prospect, if he ever were a prospect, which he never was. No one has heard of or cares about him because of any combination of: (1) he is not and never was a prospect; (2) there are a fair number of actual prospects in Colorado’s actual farm system who are actually exciting; (3) prospect status notwithstanding, he has no path to playing time because the Rockies habitually bury their actually exciting talent. At 28, Tauchman ain’t getting any younger, and I ain’t either. He deserves all the hype he can get, and I’m here to dish it out.

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Year in Review: My Inaugural TGFBI Team

On Monday, I wrote about my first foray into ottoneu. This post carries the same warning: This genre of post may not appeal to most readers. I don’t want to waste your time if it’s not your thing. Hereafter I’ll dissect my performance in the first annual Great Fantasy Baseball Invitational organized by our Justin Mason. For the uninitiated, TGFBI is a multi-league tournament of sorts among fantasy analysts, all competing in separate leagues and also overall (thanks to standings compiled by Smada).

Again, this is all about accountability. It’s easy to chalk up your W’s and ignore your L’s. I also think some folks might be interested in seeing how an analyst might actually implement the advice they offer. I’ll be the first to admit having a platform does not make me an “expert” by any means. I research and write to learn more about baseball and fantasy baseball and to be the best fantasy baseball player I can be. I’m not there yet. I’m my own worst enemy, as I’ll show below. Ultimately, I hope taking a fine-tooth comb to my season might help me grow as an owner and, perhaps, help others as well through insight and reflection. (Or maybe you’re reading just to be entertained! That’s fine, too.)

Same word of advice as last time, to myself and everyone: always, always make sure you understand the league rules and scoring format. This is something I screwed up in ottoneu, and it’s something I screwed up in TGFBI. Namely: TGFBI did not impose an innings limit. That’s a huge deal. In 15-team leagues, it’s difficult to actually blow through a 1,400/1,500/whatever-inning limit while accruing worthwhile ratios, but you could do it if you set your mind to it. I wouldn’t recommend it; it requires nearly or fully punting saves. Still, at a certain point last year, I decided to embrace it when my pursuit for saves proved itself entirely fruitless.

League finish: 4th of 15
Overall finish: 51st of 195

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Year in Review: My First ottoneu Teams

Warning: This genre of post may not appeal to most readers. I don’t want to waste your time if it’s not your thing. Hereafter I’ll pull back the curtains to review of performance in two “expert” leagues: FanGraphs Staff leagues #1 and #2, both constituting my first foray into the ottoneu world.

This is about accountability, which is something I am, as some would say, “all about.” It’s really easy to parade your victories; it’s more difficult to advertise and own your losses, both regading overall performance (league titles and return on investment) and also player-specific analysis. I’m eager to remind readers I called a Jose Ramirez breakout in 2015 (and, again, in 2016, when it actually happened), an Austin Barnes “breakout” in 2017, and Luke Weaver and Madison Bumgarner implosions prior to 2018. What I decline to admit, though, is, in the posts to which I just linked, I declared I’d fade Justin Verlander hard in 2016, when he won 16 games with a 3.04 ERA, or that I thought Chris Davis might out-earn Giancarlo Stanton. Sometimes, The ProcessTM finds diamonds in the rough; other times, it mistakes turd-shaped rough for diamonds.

I can chalk those L’s up to a lack of experience and knowledge. I’ll readily admit some of my analyses from only a couple of years ago make me cringe. But I also know that even great calls can fall victim to variance or misfortune (which is why I refreshed my Ramirez breakout pick from 2015 for 2016, and my Barnes breakout pick from 2016 for 2017 — and, spoiler, probably again for 2019). Some losses are unearned, akin to a quality start with a bullpen implosion. Others are downright bad. But, I stand by them! I once believed them. It’s just how it goes.

It’s my first time doing this. Just figured it was high time to hold myself accountable and try to learn from my league-specific performances, both profitable and otherwise.

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The Truth About Pitch Values

It seems as though each year, fantasy baseball analysts, “professional” and amateur alike, hone in on a new — or, if not new, then relatively untouched — metric or data set for their endlessly eager consumption. In 2015, FanGraphs introduced batted ball data to its leaderboards. In 2016, Statcast data was unveiled, although it arguably didn’t become popular until 2017, and before the 2017 season FanGraphs changed the game with its splits leaderboard. Baseball Prospectus has introduced myriad new metrics, too — DRA in 2015, DRC+ last year, etc. — and we began to lean into pitch-specific performance analysis last year. (The latter-most topic is relevant to what follows here.)

I recently joined Christopher Welsh and Scott Bogman of In This League on their podcast. I thought one of the evening’s questions was particularly topical and prescient (and I paraphrase): What will 2019’s it metric be? The question was asked with pitch values, something I’ve seen garner increasing attention on Twitter, in mind.

You can acquaint yourself with pitch values directly from the man who created them:

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Pitch Type Performance: 2018 Summary

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.)


  • 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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The Biggest Hitter K% Outliers of 2018

Yesterday, I devised a new expected strikeout rate for pitchers and used it to identify qualified starting pitchers who over- or under-performed in 2018. I’m reluctant to make out the exercise to be more than it is. I simply wanted to take the most intuitive approach to describing a pitcher’s strikeout rate (K%): by using the plate discipline exhibited by opposing hitters. Today, I seek to do the same for hitters. I can tell you now the discussion will be much more qualitative than quantitative.

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The Biggest Pitcher K% Outliers of 2018

Mike Foltynewicz, a first-ballot Hall of Namer, immediately strikes me as someone who outperformed his strikeout rate (K%) in 2018. I don’t have to look far for confirmation: his 27.2% strikeout rate outstripped his 10.3% swinging strike rate (SwStr%) by a mile. Because whiff rate correlates so strongly with strikeout rate, it serves as a useful proxy for what one could expect of a pitcher’s strikeout ability.

I generally follow this rule of thumb when I’m reluctant to get too into the weeds when assessing peripherals: SwStr% * 2 = K%. It’s imperfect but useful in a pinch. Folty violates this rule of thumb pretty dramatically. Of 13 qualified pitchers who struck out at least 27% of hitters, his 10.3% swinging strike rate falls well short of the shortlist’s 2nd-lowest mark (Charlie Morton, 11.9%). Foltynewicz’s 2018 performance has already wilted under what amounts to very little duress.

Still, I wanted to allow Foltynewicz the opportunity to redeem himself. Whiff rate does not a pitcher make; there are other components to plate discipline allowed such as chase rate (O-Swing%) and zone rate (Zone%), among others, that describe each pitcher in much finer detail. I broke down a pitcher’s plate discipline allowed into its component pitch outcomes:

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2018 Statcast Park Impacts (Not Quite Factors)

The longer we have Statcast data at our disposal, the more ways we find novel uses for them. What follows is my proposal to use the difference between expected and actual value segmented by batted ball type and venue to determine park factors (and potentially evaluate defensive value, as described in the footnote). Unfortunately, someone smarter than me was already way ahead of me. I’ll get to that in a second.

A typical park factors grid, such as those produced by ESPN or FanGraphs, commonly relies on outcomes — outcomes of plate appearances (ESPN), batted ball categories, or both (FanGraphs). They describe what actually occurred, the way wOBA describes a hitter’s actual production. Conversely, expected wOBA (xwOBA) describes what should have occurred based on a batted ball’s exit velocity (EV) and launch angle (LA). It strips away everything else, holding constant all other environmental factors in order to deliver an otherwise-context-neutral EV/LA-based value.

The difference between wOBA and xwOBA (“wOBA minus xwOBA,” or wOBA—xwOBA for short), therefore, effectively captures all value amassed or lost by other variables. In other words, if wOBA explains what actually happened in a non-neutral environment, and xwOBA explains what should’ve happened in a neutral environment, then the difference between them characterizes the effect of the environment — the ballpark itself.

Unfortunately for me (but fortunately for everyone else), Tony Blengino already did this (which is why he’s a former MLB executive and I’m not). In 2017, he used Statcast data to calculate park factors on the basis of expected outcomes relative to actual league-average production. For all intents and purposes, it’s the same idea.

Consider this post a refresher on the topic.

Let me call your attention back to a simpler time. If you search “Miguel Cabrera xwOBA” on Twitter, you’ll find, well, not a multitude, but at least a sampling, of Tweets from the summer of 2017 lamenting Cabrera’s (and his teammate’s) bad luck by measure of wOBA—xwOBA:

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