I wrote last week about Max Scherzer, who, in 2015, reached new heights. It was, is, painfully cliché, but it’s true. The same could be said for Chris Sale, who also (1) reached new heights and (2) suffered the misfortune of languishing in the rotation of a ballclub that ultimately would not contend.

Except Sale didn’t throw two no-hitters, nor did he almost throw three no-hitters, nor did he almost throw back-to-back no-hitters. Because those are all things Scherzer did. What Sale did do, yes, is give up 13 runs in fewer than nine innings across two starts in late April and early May.

People kind of freaked, and understandably so — the sabermetrically inclined readership at FanGraphs is not necessarily representative of the greater population of baseball fans. And the greater population of baseball fans saw a 5.93 ERA through 27.1 innings — the epitome of a small sample size, but nonetheless a sample to which a fan is entitled to react.

If you stayed tuned, you know the narrative: in the 26 starts after his two-game disaster, Sale struck out more than a third of the batters he faced. More than a third. In four of those games, he struck out more than half of them. That’s insane. Even in an era of baseball when we yawn at a strikeout rate lower than 8.0 per nine innings, that’s still insane.

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Steamer, a reputed projection system, offers predictions of the reliability for each player’s projected stat line. (These can’t be found in FanGraphs’ database, but they are available in the raw download from Steamer’s website.) James Shields‘ Streamer projection scored the highest reliability probability for the 2015 season. Shields was basically a 4-WAR starting pitcher for the eight years following his 2006 debut; when the San Diego Padres acquired him, they expected to acquire consistency. Alas, Shields’ MLB-highest 80.8% reliability was, and still is, understandable.

Except Shields cashed in a most unusual age-33 performance, and that’s why more, not fewer, numbers make the sport more special: you can marvel at and appreciate the game through any of an infinite number of lenses, and it never gets old. Neil Weinberg astutely detailed this anomaly, so I will politely not rip open San Diego’s wound as it heals.

I will, instead, turn my focus to Max Scherzer who, similarly to Shields, disrupted an 11th-best 79.3% reliability score on his projection. Unlike Shields, however, Scherzer made even better on his promise.

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A month ago, I compared Depth Chart projections — a composite of the reputed Steamer and ZiPS projection systems — to fan projections, compiled on each player’s page as “FANS.” I proceeded to look at the largest differences between the two systems — loosely considering the aggregation of independent fan projections a “system” — and identify which system better projected that particular player.

For hitters, the Depth Charts won by a landslide, 7-2. Turns out that fans frequently projected better production from prospects than what said prospects actually produced. It’s the cognitive bias I expected to see; I anticipated that fan(tasy owner)s associate hype with swift, robust production. I call it the Mike Trout Syndrome, named after the generational talent who has conditioned fans to expect immediate and immense production from all top prospects.

So how did the fans fare versus the Depth Charts regarding pitchers? Funny you ask. Here are the five starting pitchers whose Depth Charts FIPs varied most dramatically from their FANS FIPs:

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2015 Visualized: Outfield
2015 Visualized: Shortstop
2015 Visualized: Third Base
2015 Visualized: Second Base
2015 Visualized: First Base
2015 Visualized: Catcher

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As it has done for the past several weeks, the RotoGraphs staff will devote an entire week to a particular defensive position. After a three-week lull during which outfielders (and the hot stove) dominated our conversations, we will turn our collective attention to starting pitchers.

In past posts, I utilized Depth Charts projections, which combined two premier player projection systems (Steamer and ZiPS) while using playing times allocated by FanGraphs staff. However, I missed the boat on accessing pitcher Depth Chart projections from 2015. So, instead, I pulled them from Steamer’s website, on which they hosted a Google Doc of 2015’s preseason projections.

Using these projections, I compared projected xFIP (expected fielding independent pitching) to actual xFIP (1) by team and (2) by player within team. Because xFIP is a rate metric, I did not need to scale performances by playing time. I hope FanGraphs’ database split performances by starting and relieving because that’s what I asked it to do; if a pitcher who split time between the rotation and bullpen appears to have wonky numbers, it could be his splits, or it could be I’m an idiot.

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Baseball, in all its glorious history, bears as its fruit some remarkable encounters between legends of the game. Allow me to recount a charming, wholly undocumented, 100%-real encounter between some of baseball’s greats:

…And then Billy Hamilton, to whom his colleagues and comrades affectionately referred as “Billy Hams,” appealed to the Baseball Gods:

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Carlos Gomez stunk in 2015. That statement is relative, of course — Gomez is a good ballplayer, and 2.6 wins above replacement (WAR) in 115 games ain’t so shabby — but for a consensus top-10 fantasy pick in 2015, he was a bust.

For anyone who hasn’t followed the trajectory of Gomez’s career over the years, it has been a tumultuous one. A top-100 prospect who bounced from team to team early on, Gomez hit his stride in a half-season sample for the Brewers in 2011 during which his isolated power (ISO) spiked 60 points alongside season-long paces (600 plate appearances) of 19 home runs and 37 stolen bases. Fantasy owners who noticed the improvement and expressed skepticism toward a depressed batting average on balls in play (BABIP) were rewarded handsomely in 2012. Gomez paid even more dividends in 2013 as he demonstrated the legitimacy of his gains.

We’ve reached a strange point in his trajectory, however. Gomez, having turned 30 three days ago (happy birthday, Carlos!!!!) and coming off his worst offensive season in half a decade, may be declining or, at the very least, have fallen from his elite offensive perch for good. Gomez’s production in terms of weighted runs created (wRC+) depicts a sharp and sustained increase in production followed by a two-year plateau and then a profound leap off said plateau in 2015.

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So there’s this table I’m going to show you. Actually, you can already see it. I know you can. But humor me and pretend that only the next word appears after you read this one, and that the table simply hasn’t manifested yet. This table holds within it Christian Yelich’s career statistics, parsed by year. It’s unlike a normal table because I prorated all of the counting stats to 600 plate appearances. The rest, however, are rate statistics, or component metrics presented as rate statistics. Basically, everything is comparable on a playing time basis. What it will all tell you is what ended up being a discarded title for this post:

Christian Yelich is a remarkably consistent hitter.

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Back in May, I introduced an equation that would calculate expected isolated power (thus, “xISO”) numbers for hitters based on their batted ball profile. The idea was to generate an equation that could accurately describe for how much power a hitter should be hitting based entirely on publicly available data (provided to FanGraphs by Baseball Info Solutions), as opposed to proprietary data, so all fantasy baseball enthusiasts could use it.

I won’t get into the nitty gritty again — you can click on the link in the first sentence if you want to open that can of worms — but I will provide the equation again for posterity:
a
xISO = –.1396 + .1814*Pull% + .5136*Hard% + .2344*FB%

I’ll provide a table of xISOs for all qualified hitters below and deliver some insight regarding potential buy-lows and sell-highs.

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2015 Visualized: Shortstop
2015 Visualized: Third Base
2015 Visualized: Second Base
2015 Visualized: First Base
2015 Visualized: Catcher

* * *

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 outfielders.

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 outfield 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.

I compared 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.

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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?

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