Archive for value

Oftentimes, I write out of inspiration. This time, I write simply to write, because the subject happened to creep up into my thick ol’ skull without provocation, which I guess is a type of inspiration in and of itself but not wholly what I had in mind. No one specifically needs this post right now, or maybe everyone does. I don’t know.

Something I do see and have seen before, however, with frequency, are mentions of such-and-such player rising or falling in the ranks, usually by virtue of average draft position (ADP). ADP is a measure of a player’s rank by aggregating data for a whole boatload of snake drafts. It’s a good way of assessing a player’s market value.

The problem with ADP is, unless you have completed research nearly identical to this, you can’t possibly be expected to know how a player’s ADP rank might equate to a dollar value at auction. Having this knowledge, this intuition, is arguably helpful in understanding how much you’re staking on any particular player. Moreover, changes in ADP become easier to digest. Possibly. For me, it does. If you’ve never participated in an auction draft before, maybe it doesn’t.

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I’ve had the question written on my whiteboard for ever: are foul balls good or bad? It’s a glass-half-empty, glass-half-full conundrum. The former group might think a foul ball is simply a barely-missed opportunity at in-play contact. The latter group might view that same event as a positive — that the poor quality of contact on a foul ball is indicative of an ability to induce poor contact quality in general, and it’s not inherently different from a swinging strike.

In my heart of hearts, it makes more sense to me that a foul ball is closer to in-play contact than not. Considering the diameter of both a bat and a ball, and the nearly physically impossible feat of connecting the two in motion, a foul tip has a margin of error of mere inches, whereas a swinging strike, fully sans contact, can have a margin of error measured in feet. Yes, it seems like getting a piece of the ball suggests, from the pitcher standpoint, makes the glass appear more half-empty than otherwise.

I wanted to finally tackle the subject, but I didn’t really know how. I first looked at the outcome of the pitch directly following foul and non-foul pitches, but it was a bit noisy (although, to be fair, I may have missed clear patterns in that noise). I imagine the effects spawning from a foul ball are not exclusive to the next pitch; rather, they may manifest two or three or even four pitches deeper into the plate appearance. In other words, a pitch-sequencing analysis might be prohibitively difficult, at least for someone like me who lacks the brainpower or mental stamina to pull it off.

Instead, I opted for something a little easier yet arguably just as telling. Read the rest of this entry »

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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The third in an ongoing series (Pt. 1) (Pt. 2), I’ll continue to compare hitters using blind résumés to highlight “generic-brand” players who could be drafted as substitutes for “name-brand” players in later rounds of your draft. This entry will differ slightly in substance; I’ll focus on 2018 projections to identify similarly skilled players rather than use hindsight to rationalize (or poke holes in) how we’ve valued past performance.

I will use National Fantasy Baseball Championship average draft position (NFBC ADP). Reminder: we host them here on FanGraphs, too. It’s worth noting I pulled these projections this past weekend, so the numbers will likely differ slightly but not unconscionably. The ADPs are current as of yesterday.

Comparison #1: Power-Speed Outfielders, Mostly

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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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Playing time can make or break a baseball player’s fantasy value. An elite player may not finish above replacement level if he suffers an injury and plays only half the season, and a lackluster player could finish above replacement level simply by playing every single day. This is all intuitive, and the fantasy community generally approaches these kinds of things rationally. In other words, most players are appropriately valued, outside of the market inefficiencies that inevitably warp player values.

One-dimensional speedsters — dudes who steal a bunch of bases and do little else — are much harder to peg. Their value is tied up primarily in one category, as stolen bases (SBs) do not directly correlate with other categories the way home runs would with runs and RBI, for example. The issue becomes all the more confounding when one considers the contemporaneous scarcity of SBs relative to home runs. There’s more to value than just SBs and plate appearances (PAs), but the fact of the matter is the two statistics by themselves correlate very strongly with a player’s end-of-season (EOS) value (which, here, are informed by Razzball’s Player Rater).

In the last five years, baseball has seen 75 player-seasons of 30-plus SBs — 15 steals a year on average, a trend that didn’t fundamentally change in 2016 (although that doesn’t mean SBs aren’t scarce). A simple linear regression of SBs and PAs, the latter of which serves as a proxy for other counting stats such as runs and RBI, against EOS value produces a remarkable 0.71 adjusted R²:

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