Archive for 1B

Jake Bauers entered the 2019 season a kind of wide-awake sleeper to many fantasy analysts looking to target cheap value at first base or in the outfield. Per National Fantasy Baseball Championship (NFBC) average draft position (ADP) data, Bauers was drafted, on average, 230th overall, 24th among first basemen, and 66th among outfielders — effectively your last corner infielder or outfielder, or your first bench bat. You weren’t depending on him too gravely for production; the cost to acquire Bauers was typically low, making any sunk costs a bit easier to swallow.

Still, it has been disappointing to see Bauers follow up 2018’s 11-homer, 6-steal half-season with mediocrity. Yes, his rookie campaign featured a miserable .201/.316/.384 line, but it was marred by a meager .252 batting average on balls in play (BABIP) and a 26.8% strikeout rate (K%) that seemed far out of whack relative to his roughly league-average 11.0% swinging strike rate (SwStr%). It stood to reason, at first glance, Bauers would cash in on some positive regression to post a fairly solid slash line while posting double-digit home runs and stolen bases — and it didn’t seem altogether far-fetched to hope so.

Ultimately, it never came to fruition. In almost an identical number of plate appearances, Bauers replicated his home run tally but with a lower isolated power (.146 ISO to .183 ISO), a feeble stolen base rate (two steals on five attempts, versus six on 12 last year), and worse plate discipline despite a better whiff rate. Cleveland had enough and eventually optioned Bauers with his career line standing at 22 homers, eight steals, and a .218/.312/.381 line in 771 plate appearances.

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I planned to include Rhys Hoskins in my blind résumés post from Monday, but I couldn’t find any realistic comps for him. Part of the problem is no one does for a full season what Hoskins did for 50 games. Part of the problem, also, is no one does for a full season what Hoskins would be expected to do for a full season, based on his peripherals. It’s a fairly unique skill set (although let’s not conflate “unique” with “the best” or any kind of superlative like that… yet).

Hoskins had himself a real, real nice debut. This isn’t the first time you’ve read about him in the last couple of months and it will be far from the last. Andrew Perpetua, for all intents and purposes, regressed his batted balls from 2017 and he still would’ve had an awesome season. In Eno Sarris’ heart, as well as mine, Hoskins was the runner-up National League Rookie of the Year to Cody Bellinger.

Hoskins had himself a real, real conveniently sized debut as well. His playing exactly 50 games prevents me from arbitrarily choosing a cutoff and having to justify it. A cutoff for what, you ask? Well, Hoskins, in exactly 50 games, posted a .359 isolated power (ISO) while swinging and missing only 7.1% of the time. He struck out a fair deal, but he also walked a ton. Take this snapshot of a season and, as aforementioned, you’ll be hard-pressed to find comps.

Which is exactly why I set out on a very pseudo-scientific quest to find any of Hoskins’ contemporaries who have done this — this, being the aforementioned 50 games of a .350-ish ISO and a 7%-ish swinging strike rate (SwStr%) — at any point in their careers (or within windows of their careers that I’ve curated). I’m winging it here, plucking names from my brain who have elite power and at least above-average plate discipline (assuming Hoskins might, but it’s not a foregone conclusion) and scouring their careers for similar streaks. Any omitted hitters are a product of my lack of memory or imagination, not of malice. Except for Giancarlo Stanton.

Mike Trout

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I’ve done this before — compare similar players, one of whom is “name-brand,” the other “generic-brand,” using blind résumés — as have many others. Ben Kaspick carried the torch a while this year, but he credited Joe Douglas with the idea. So let’s say it’s a group effort to which I’ll contribute once again.

In anticipation of 2018 drafts, I wanted to carry out a “buying generic” style of analysis, borrowing in part from too-early mock draft average draft position (ADP) data. I do not intend to construe the following comparisons as rigorous analysis. I do, however, intend to highlight some potential bargains that, if the too-early mock ADP information is concerned, warrant your attention on draft day.

Comparison #1: Outfielders

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I’m already thinking about 2018. It’s not that my teams are doing poorly; they’re fine, for the most part. It’s that the economist nerd in me, when thinking abut fantasy baseball, most often evaluates the disparities between perceived and actual values, and how long, if ever, it takes for the market (aka fantasy owners) to come to equilibrium, to use economic parlance.

For example: you may or may not be aware that Kevin Gausman, despite his atrocious start to the season, has been magnificent the last five weeks. In seven starts from July 2 onward, he’s posted a 3.24 ERA (supported peripherally by a 2.81 xFIP and 3.40 FIP) with 11.4 strikeouts and 2.6 walks per nine innings. The strikeout rate is fueled by a 15% swinging strike rate (SwStr%), which have come consistently, ascending into double-digit percentages in all seven starts (and in eight of his last nine). His strikeout-to-walk differential (K-BB%) by month: 2.0%, 8.8%, 9.2%, 23.4%, and, in one August start, 28.0%.

Meanwhile, he’s inducing ground balls almost half the time (49.5% GB). You could say he’s due for batting average on balls in play (BABIP) regression, and he probably still is. His BABIP constantly hovering above .349 does not inspire confidence, but few pitchers have ever been BABIP’d so hard in a single season — I discussed this phenomenon in regard to Robbie Ray. All said, while there’s no guarantee his BABIP regresses before October, Gausman still shows the promise we once expected of him — perhaps more — and it’s going largely unnoticed because of his downright repulsive first half. (He’s baseball’s #12 starter the last month.)

Such is the gist of this post, in which I’ll briefly touch upon players I anticipate to have average draft positions (ADPs) in 2018 that will lend themselves to relatively low-risk, high-reward opportunities in standard mixed leagues. Whether such expectations become reality is another story; that’s why I’m relying on ownership levels as a proxy for perceived value. All ownership levels likely retain some amount of draft day inertia, for better or for worse — in other words, leftover ownership (or lack thereof) in abandoned leagues — so take it all with a grain of salt.

Please note this is, by no means, an exhaustive list — just the first few players who come to mind, mostly because I’ve paid close attention to them all season.

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2015 Visualized: Catcher

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For the next few weeks, the RotoGraphs staff will devote an entire week to each defensive position, including spotlights on particular players as well as trends throughout the 2015 season. This week, we’re highlighting first basemen.

I don’t claim to be a Tableau (or data visualization) whiz by any means, but I thought it would be cool to visually represent the first base landscape in 2015 — with some analysis sprinkled in.

Steamer and ZiPS represent premier player projection systems; FanGraphs’ Depth Charts combines the two, and the writing staff allocate playing time accordingly. The playing time part is less important relative to the combined projections, as aggregated projections tend to perform better than standalones.

Previously, I compared actual WAR (wins above replacement) to projected WAR. This is not entirely helpful in a fantasy context, however, given WAR is a catch-all component metric for offense and defense. Defense doesn’t do us a whole lot of good for fantasy purposes.

Thus, I figured out a way to compare projected wOBA (weighted on-base average) from the preseason to actual wOBA (1) by team and (2) by player within team. Unlike WAR, wOBA is a rate metric, so it does not need to be scaled according to playing time.

First: the difference between a team’s wOBA generated by first basemen and their projected wOBA. Blue represents the highest expected wOBA; yellow represents lowest expected wOBA. The size of the bar above (below) the line represents actual wOBA above (below) expectations.

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