Projections vs. the Fans: Who Won? Hitter Edition
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?
wOBA
3. Joc Pederson, LAD OF
Depth Charts: .323 wOBA
FANS: .352 wOBA
Pederson quickly scaled a very steep cliff in the first half from which, without proper precautionary equipment, he would fall in the second half. Fans were pretty jazzed about the young dynamo prior to April, and rightfully so. Steamer and ZiPS expressed a bit of pessimism, however, likely in light of a minor league strikeout rate that increased dramatically with each graduated level.
Actual: .335 wOBA
Verdict: Depth Charts, by 5 points
It’s a minor victory, but it’s a victory nonetheless. Pederson split the difference, but someone’s gotta win.
2. Jace Peterson, ATL 2B
Depth Charts: .276 wOBA
FANS: .310 wOBA
Peterson’s inclusion could be a genuine misunderstanding on behalf of FanGraphs’ databases. There’s actually no way to absolutely guarantee Jace Peterson and Joc Pederson are not, in fact, the same young baseballer trying to pull a Bruce Wayne on Major League Gotham. They are both zodiacal Tauruses (Taurii?). You can’t make these things up, people!!!
Actual: .287 wOBA
Verdict: Depth Charts, by 12 points
It’s only two results, but I’m starting to sense a trend, one that I’d like to investigate on a rainy day that’s not today because it is, in fact, raining right now: fans exude more optimism for top prospects. I think a lot of undue enthusiasm — although, to be clear, it’s not all unwarranted — can be blamed on an already historically great Mike Trout.
1. Michael Taylor, WAS OF
Depth Charts: .289 wOBA
FANS: .338 wOBA
I won’t hide my man crush for Taylor. A Pedersonian, Steven Souzaian, four-tool hitter, his power-speed combination was too much to resist despite a borderline atrocious minor league strikeout rate.
Actual: .274 wOBA
Verdict: Depth Charts, by 49 points
This one wasn’t even close, as Taylor pulled a Price is Right on everyone and generated offensive value less than what Depth Charts and FANS predicted. Bob. Taylor, like Pederson, is toolsy as all else, but the elusive hit tool sank him like a stone.
Fielding (UZR)
3. Ben Zobrist, OAK/KCR 2B/3B/SS/OF/GM
Depth Charts: 3.9 runs
FANS: 10 runs
Zobrist, who, lauded equally for his versatility and acumen in regard to defense, had generated fewer than 10 defensive runs above replacement only once dating back to 2009, could have been what my uncle calls a mortal lock to generate another 10 defensive runs in value. Yet the computers wavered. Did they know something we didn’t?
Actual: -10.0 runs
Verdict: Depth Charts, by 6.1 runs
Probably, yeah. But the precipitous dip in defensive value seems a bit rash. At 34 years old, Zobrist converted fewer routine plays (90- to 100-percent success) at every position than ever before, per Inside Edge. It may never get better, as people rarely get younger, but the teams courting him this offseason will likely overlook the down year.
2. Brett Gardner, NYY OF
Depth Charts: 10.2 runs
FANS: 4 runs
After a few years of defensive excellence, Gardner’s defensive value tanked in 2012, hovering around league-average before sliding into the red last year. Depth Charts predicted, frankly, a ridiculous 10 runs of value from Gardner despite generating less than one-fourth of that projection the previous three seasons. Fans were rightfully more skeptical, but still optimistic.
Actual: -2.7 runs
Verdict: FANS, by 6.2 runs
Gardner didn’t convert a single play considered “unlikely” (10 to 40 percent success) or worse. Assuming the average unlikely play yielded a 25-percent probability of success (the mean of 10 percent and 40 percent), he should have converted approximately 3.75 of those. As Mr. Burke, my high school US History teacher, probably would have said, “He was beaten by the chimp.” Theoretically, a roomful of chimpanzees, if allowed to complete a standardized test, could be expected to answer roughly one-fourth of the questions, given each question has four options. Theoretically, a chimp could have fielded nearly four of the unlikely plays were he equipped with a mitt and maybe some custom cleats. Theoretically, we’ll never know if Mr. Burke would have been wrong.
Note: Evan Gattis omitted because he inhabited a designated hitter role primarily.
1. Avisail Garcia, CHW OF
Depth Charts: -3.8 runs
FANS: -11 runs
FYI, FanGraphs says Garcia, prior to this year, owes his current and past employers about $6.3 million in damages rendered, aka wins not above replacement.
Actual: -6.3 runs
Verdict: Depth Charts, by 2.2 runs
It was as ugly as last year, which, in a sense, is actually a victory, given Garcia more than doubled his career playing time in 2015. He also generated less value with his bat than his glove — truly a remarkable feat in this particular context.
WAR
Note: Michael Taylor appears twice, so I omit his second appearance. Because I can.
3. Kyle Seager, SEA 3B
Depth Charts: 4.0 WAR
FANS: 5.6 WAR
Seager had a career defensive year in 2014, and he was no slouch in 2015, either. His defense enhanced his overall value, though, and it could have been legitimately expected to regress toward the mean a smidge.
Actual: 3.9 WAR
Verdict: Depth Charts, by 1.7 WAR
Unfortunately for Seager, he posted virtually identical offensive numbers in 2015 as 2014. Yet given the incredibly strong (pun intended) class of young third base talent, his achievements didn’t pack the same punch as usual.
Depth Charts are running away with this.
2. Andrew McCutchen, PIT OF
Depth Charts: 6.0 WAR
FANS: 7.6 WAR
In the preseason, I warned of declining production — not to be mistaken with eroding skills — due to diminished baserunning activity and pitchers evading the zone more often than ever.
Actual: 5.8 WAR
Verdict: Depth Charts, by 1.8 WAR
Pitchers continued to evade the zone, and a slight down-tick in contact impacted his plate discipline and, thus, his batting average. Entering his age-30 season, things won’t get better in regard to the youthful aspects of the game such as baserunning and defense. His bat and eye will have to carry him; fortunately, those aspects have yet to considerably, if at all, decline.
1. Lorenzo Cain, KCR OF
Depth Charts: 2.8 WAR
FANS: 4.6 WAR
What better hitter to finish this post than with 2015’s surprise Most Valuable Player candidate? Relative to the Steamer and ZiPS, fans were friggin’ stoked on Cain for 2015. Cain generated modest value with the bat fueled by an exorbitant .380 batting average on balls in play (BABIP). Can’t fault the computers for regressing to the mean.
Actual: 6.6 WAR
Verdict: FANS, by 3.8 WAR
Tell me with a straight face that you knew Cain would basically double his career home run total in the course of one season. Tell me with a straight face that all signs pointed to the 29-year-old putting hard-hit balls into play almost 50 percent more often than the previous year, during which his hard-hit rate (Hard%) tumbled 3.5 percentage points lower than his previous career-worst Hard%.
Don’t tell me. I’m not listening!
The Verdict
The computers took the humans to the slaughter, winning seven of nine contests. You’re welcome for not abruptly ending this post after Game 6, when the Depth Charts technically clinched the series.
For real, though: it’s a small sample, but the computers seem to fare better on the most-disputed players. Because of the inherently statistical nature of forecasting, the projection systems are generally more conservative overall — and, in light of outliers, they will, more often than not, more adeptly anticipate regression to the mean better than humans could. I know this post was mostly for funsies, but I think it gives an interesting, albeit truncated, glimpse into the cognitive biases from which we fantasy baseball enthusiasts generally suffer.
Two-time FSWA award winner, including 2018 Baseball Writer of the Year, and 8-time award finalist. Featured in the print edition of Baseball Prospectus since 2022 (2026 edition out now). Biased toward a nicely rolled baseball pant.
I read the title as:
Projections vs. the Fans: Who Won? Hitler Edition
The actual title makes a lot more sense.
Nobody ever wants to play Trivial Pursuit: Hitler Edition with me.
Heil-arious.
Fans are always so enthusiastically bullish on everybody, including myself. I’m working on tailoring that, but still…14 WAR for Mookie Betts sounds reasonable, right?
You must be omitting his pitching WAR
Projections tend to be conservative since they rely on regressions. If you select mostly players who disappointed during the season the regressions will beat out fan projections . This is a self serving article with little value but to prove the value of selection bias, but hope it works for you.
I can only speak for myself but my own projections pulled out of the nether regions for Beltran, Arod, Teixeira, Boegarts, Betts, Chris Davis, Josh Donaldson, etc beat the projections
Except I didn’t “select mostly players who disappointed during the season.” I selected players for whom Steamer/ZiPS and fan projections had the greatest disparities. Player performance, let alone certain degrees of player performance, had nothing to do with the sample selected above.
This article was, as stated, for funzies. Relax.
I have some data for projections from before the year that I created for fantasy baseball purposes. I believe most people realize that the fans are more optimistic in their projections than the various systems that are out there. This is shown by the numbers below. The following batting lines are based on a group of 256 fantasy-relevant players that all of these sources had in common and are from before the 2015 season:
.271/.335/.424, 559 PA/player—Fans
.262/.329/.418, 522 PA/player—Steamer
.265/.327/.420, 513 PA/player—ZiPS
The Fans have higher projections for average, on-base percentage, slugging percentage and playing time.
Here is the data for pitchers (180 pitchers, all projected by all three sources):
3.45 ERA, 1.20 WHIP, 8.2 K/9, 2.6 BB/9, 147 IP/pitcher—Fans
3.78 ERA, 1.25 WHIP, 7.9 K/9, 2.8 BB/9, 134 IP/pitcher—Steamer
3.66 ERA, 1.24 WHIP, 8.0 K/9, 2.8 BB/9, 135 IP/pitcher—ZiPS
Again, the fans are the most optimistic in all of the rate categories and playing time. Of course, it’s not logical to project hitters AND pitchers to be better, but that’s what happens.
I think to compare the Fans to Steamer or ZiPS or any other system, they should all be adjusted to the same baseline. If the whole group of players projected by the fans is projected to have a higher AVG by 6 to 9 points and a higher OBP by 6 to 8 points and a higher slugging by 4 to 6 points, as is the case above, then adjust all of the fans projections down. This would make for a better comparison.
You could just throw out the fans projections because they are so much more optimistic than the systems, but the fans might do a good job of ranking players even if they are too optimistic in their projected stats. For fantasy purposes, it’s still good information to know how players are ranked.
Adjusting the fans projections for better comparison entirely defeats the purpose of using them. If fan projections are too optimistic, then they’re too optimistic, making them less reliable — case closed. Let’s not tamper with the evidence.
I agree with the sentiment in your final paragraph: I think it’s a pretty interesting barometer of expectations. ADP essentially offers the same information, however, so it would be interesting to see how FANS and ADP differ.
I think Bobby’s point is that we should be seeing if we can extract some signal, rather than pointing out specific flaws. Markets aren’t always efficient, but there is definitely wisdom in the crowds that do not live in the data. I’m curious how often the (adjusted) fan’s projections are correct, purely from an over/under (i.e. if adjusted fans are higher than steamer and actual is greater than steamer then call it “correct” otherwise “incorrect”). If correct% is greater than 50% and statistically significant, it could potentially be used to improve steamer.
Right. The reason for adjusting fan projections, then, would depend on end-use, and Bobby’s and my uses differed. But I get where you, and probably Bobby, are coming from now — my bad!
If we could identify a systemic bias and correct for it, then it would be interesting to see how fan expectations could be helpful to computerized projections. It appears fans overestimate production, but they may also overestimate (or underestimate) other things such as playing time, or perhaps they misunderstand a more complex metric such as UZR for fielding. There may also be some bias inherent in a fan seeing an already-high (or -low) projection for a player on his page and contributing an equally high (or low) projection because of exposure to it, whether the fan knows it or not. All sorts of interesting cognitive conundrums abound.
All in all, it could be a helpful and particularly interesting tool — just wish more fans actually contributed to each player’s projection to make them potentially less volatile.
I know that this isn’t anything new, but when I see a decent sample for fan projections on a player, I often regress it by rolling the rate stats in with Steamer and ZiPS, and adjust for playing time that the computer might not have had on hand when making the projections (Brad Miller would be a prime candidate for me adding playing time to the computer models this year). I’ve yet to compare and contrast the averaged projections hitting their marks or not, but I would assume that the more information available would mean a better overall projection. Would Fangraphs be able to post something like this on the player pages? I would imagine that it would work to regress the fans projections well.