Archive for 2018

Trevor Bauer, the National Fantasy Baseball Championship’s No.-9 starting pitcher and No.-31 player overall, has pitched to the tune of a 4.12 ERA this year. All things considered (“things” being, primarily, the juiced ball), Bauer hasn’t been awful. But after compiling a pristine 2.21 ERA in last year’s breakout with equally pristine ERA estimators to boot (3.14 xFIP, 3.21 SIERA), this year’s peripherals (4.35 xFIP, 4.21 SIERA) are far less inspiring, even when adjusted for context.

The easiest way to write off Bauer’s 2018 season as an aberration is, well, to look at everything else he has ever done. He sports a career 3.97 ERA, with just one season (2018) with an ERA under 4.00. The blind squirrel who took an approach as simplistic as this in 2019 would have invariably found a nut.

Such an approach, however, would grossly undersell Bauer’s gains in 2018, which were quite legitimate. Using the most basic of peripherals, Bauer’s swinging strike rate (SwStr%) took the best 3rd-biggest step forward in nominal terms, behind only Patrick Corbin (and his slider) and Gerrit Cole (and his fastball).

Yet 2018 gains do not necessarily beget sustained excellence. Bauer’s narrative is a fairly complex one, so let’s give it proper attention.

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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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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 Process^TM 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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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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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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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 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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Fantasy analysts and enthusiasts alike are still searching for ways to use Statcast’s expected wOBA (xwOBA) metric meaningfully to gain an edge. Unfortunately, beyond leveraging the difference between xwOBA and actual wOBA (what I, and probably countless others, refer to as the “wOBA minus xwOBA differential”), I don’t know yet how else you can use xwOBA effectively. Given already-widespread use of the metric, the minimal edge you can glean will come from interpretation.

I discussed the interpretation of xwOBA multiple times in 2018. In May, I highlighted hitters on whom to buy low because of their extreme/outlier wOBA–xwOBA differentials. In July, I called out xwOBA’s inability to account for what appeared to be the ball becoming un-juiced, thereby overestimating xwOBA across the league. In September, I investigated the predictiveness of xwOBA in-season (that is, the predictiveness of first-half xwOBA on second-half wOBA).

I discussed all of these topics during my presentation at BaseballHQ’s annual First Pitch Arizona forum, especially the former-most. Basically all of the hitters I tabbed as buy-lows outgained their prior performance by substantial margins — all of them, that is, except for Victor Martinez. Could he be considered a miss? Sure, except he was different from the rest of his fellow underachievers: he perennially underperforms his xwOBA. Perhaps the better question, then, is: Why was he a miss?

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When introducing my 2018 bold predictions, I talked about “not being bold just to be bold.” I sought to demonstrate that, by “abiding by The Process ^TM,” I could make bold predictions that, frankly, didn’t feel all htat bold to me. In hindsight, it’s easy to say how they really weren’t bold. But back when I made them, based on available average draft position (ADP) data, they were, by definition, bold.

It would sound arrogant for me to say my bold predictions this year could be used as a clinic on extracting value in drafts (especially with the social baggage that “putting on a clinic” carries these days). However, it might not be untrue: I hit three of my five predictions, all of them relating to guys who played key roles in the drafts of successful teams this year. Please, do forgive the arrogance, then; I’ll do my best to explain how the strategies I employed this year are repeatable.

All end-of-season values will rely on ESPN’s Player Rater.

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You don’t care about this part! You care about the predictions.

I originally wrote my bold predictions for 2018 here; they arrived late and incomplete, but they arrived in some capacity, and that’s all we can ask for at this point.

I make bold predictions not for the sake of being bold but, rather, (1) using earnest judgments of player abilities and market inefficiencies and (2) to create teachable moments. My better bold predictions include prescient forecasts for Jose Ramirez and Austin Barnes; my worse ones have typically revolved around Giancarlo Stanton and Chris Davis in some capacity.

Let’s see how everything’s going so far.

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