In case you missed the announcement in Paul Sporer’s latest post:
NFBC ADP data used to be hosted at Stats, Inc. Prior to last week, 2018 data had only been available to NFBC contestants.
Anticipated FAQs:
Yesterday, I compared hitters’ FANS projections to their Depth Charts projections in order to identify the biggest discrepancies between the two. For more information on why I’m doing this or what FANS and Depth Charts projection entail, I cordially invite you to click here.
I’m here to repeat the exercise but with pitchers instead. Focusing on playing time (as measured by innings pitched), K/9, BB/9 and saves. I’ll mix in starters and relievers at my discretion or where obviously necessary, like for saves. Like I did yesterday, I’ll try to limit each blurb to three sentences.
Last year, I performed this very exercise, in which I compared FANS projections — projections generated by fans — to the Depth Charts projections — a composite of Steamer and ZiPS with playing time allocated by generally informed FanGraphs staff. I intended to highlight the largest discrepancies and offer a quick take on them.
I explain my interest in FANS during the inaugural of this exercise. Said interest pertains largely to anticipated versus most likely outcomes for a player and how those disparities manifest themselves in price distortions on draft day.
This time around, instead of discussing five National League outfielders at length, I’ll focus on the largest differences between FANS and Depth Charts projections in playing time, home runs, stolen bases, wOBA and WAR for a couple of players per category. I’ve set a personal goal for no more than three sentences per player so I don’t spend all day doing this. Because I could, and nobody wants that.
Back in February, I compared preseason projections between reputed projection system Steamer to those submitted by FanGraphs readers, dubbed “FANS.” The concept was simple: identify National League outfielders whose Steamer and FANS projections varied wildly and predict a “winning” projection. (In the same vein, Community poster Bobby Mueller compiled some nice summary statistics.)
Alas, I needlessly task myself with determining who fared better: the Depth Charts — which are Steamer and other reputable projection system ZiPS, with playing time allocated by FanGraphs staff — versus the fans.
Because the present author, whose analytic capacity is debatable but authorship of this piece is absolute, retains sole proprietorship of quasi-analysis that has a moderate to high probability of spiraling out of control, he has chosen three statistics with which to compare qualified major league hitters: weighted on-base average (wOBA), an offensive rate statistic (not that it offends anyone, per se, but, well, you know); Fielding (Fld), a defensive statistic, probably; and wins above replacement (WAR), an overall performance metric.
Yours truly has elected to discuss only the most egregious differences in projections and declare winners between them accordingly. Granted three nominees within three categories, an outcome in which a winner is not declared is highly improbable. Alas, a true, rightful and, above all, 100-percent authoritative champion may very well be crowned in due time. So, who will it be: the wisdom of the masses, or the wisdom of two computers?
I have never thought much about, or given much attention to, FanGraphs’ fan projections. It’s primarily a matter of stubbornness: I run my own projections, and I develop my own strategy, so why should I listen to you? At least there’s a trace of rationale behind it: I know how I created my projections (whether or not they’re any good is a topic for another day), but I have no idea how you created yours. Thus, I am more likely to blindly trust a computer-generated projection system such as Steamer instead of random fan projections.
Still, there is a sort of bizarre, secondhand wisdom to fan projections. For every person who is high on a particular player, there could be another person who is equally-and-oppositely down on him. Solicit and aggregate enough fan projections and you could produce a very reasonable prediction of a player’s performance by sheer chance.
Which is why fan projections intrigue me. If, for example, Steamer predicts the most likely outcome from a wide range of possible outcomes for a player, then the fans convey the anticipated outcome for a player. The difference between them, you could say, is what amounts to a market inefficiency (aka a price distortion). The larger the difference, the greater the inefficiency. We see these inefficiencies arise every year — 2014’s most prevalent example is probably Corey Kluber — and they typically manifest because of a lack of information about certain players.
