2/05/19 DRC+ update- some partial fixes, some new problems

BP released an update to DRC+ yesterday purporting to fix/improve several issues that have been raised on this blog.  One thing didn’t change at all though- DRC+ still isn’t a hitting metric.  It still assigns pitchers artificially low values no matter how well they hit, and the areas of superior projection (where actually true) are largely driven by this.  The update claimed two real areas of improvement.

Valuation

The first is in treating outlier players.  As discussed in C’mon Man- Baseball Prospectus DRC+ Edition by treating player seasons individually and regressing them, instead of treating careers, DRC+ will continually fail to realize that outliers are really outliers. Their fix is, roughly, to make a prior distribution based on all player performances in surrounding years, and hopefully not regress the outliers as much because it realizes something like them might actually exist.  That mitigates the problem a little, sometimes, but it’s still an essentially random fix.  Some cases previously mentioned look better, and others, like Don Kessinger vs. Larry Bowa still don’t make any sense at all.  They’re very similar offensive players, in the same league, overlapping in most of their careers, and yet Kessinger gets wRC-DRC bumped from 72 to 80 while Bowa only goes from 70 to 72, even though Kessinger was *more* TTO-based.

To their credit- or at least to credit their self-awareness, they seem to know that their metric is not reliable at its core for valuation.  Jonathan Judge says

“As always, you should remember that, over the course of a career, a player’s raw stats—even for something like batting average—tend to be much more informative than they are for individual seasons. If a hitter consistently seems to exceed what DRC+ expects for them, at some point, you should feel free to prefer, or at least further account for, the different raw results.”

Roughly translated, “Regressed 1-year performance is a better estimation of talent that 1-year raw performance, but ignoring the rest of a player’s career and re-estimating talent 1 year at a time can cause discrepancies, and if it does, trust the career numbers more.” I have no argument with that.  The question remains how BP will actually use the stat- if we get more fluff pieces on DRC+ outliers who are obviously just the kind career discrepancies Judge and I talked about, that’s bad.  If it is mainly used to de-luck balls in play for players who haven’t demonstrated that they deserve much outlier consideration, that’s basically fine and definitely not the dumbest thing I’ve seen lately.

 

This, on the other hand, well might be.

NAME YEAR PA BB DRC+ DRC+ SD DRAA
Mark Melancon 2011 1 1 -3 2 -0.1
Dan Runzler 2011 1 1 -17 2 -0.1
Matt Guerrier 2011 1 1 -13 2 -0.1
Santiago Casilla 2011 1 1 -12 2 -0.1
Josh Stinson 2011 1 1 -15 2 -0.1
Jose Veras 2011 1 1 -14 2 -0.1
Javy Guerra 2011 1 1 -15 2 -0.1
Joey Gathright 2011 1 1 81 1 0

Not just the blatant cheating (Gathright is the only position player on the list), but the DRC+ SDs make no sense.  Based on one identical PA, DRC+ claims that there’s a 1 in hundreds of thousands chance that Runzler is a better hitter than Melancon and also assigns negative runs to a walk because a pitcher drew it.  The DRC+ SDs were pure nonsense before, but now they’re a new kind of nonsense. These players ranged from 9-31 SD in the previous iteration of DRC+, and while the low end of that was still certainly too low, SDs of 1-2 are beyond absurd, and the fact that they’re that low *only for players with almost no PAs* is a huge red flag that something inside the black box is terribly wrong.  Tango recently explored the SD of wRC+/WAR and found that the SDs should be similar for most players with the same number of PA.  DRC+ SDs done correctly could legitimately show up as slightly lower, because they’re the SD of a regressed stat, but that’s with an emphasis on slightly.  Not SDs of 1 or 2 for anybody, and not lower SDs for pitchers and part-time players who aren’t close to a season full of PAs.

Park Adjustments

I’d observed before that DRC+ still contains a lot of park factor and they’ve taken steps to address this.  They adjusted Colorado hitters more in this iteration while saying there wasn’t anything wrong with their previous park factors.  I’m not sure exactly how that makes sense, unless they just weren’t correcting for park factor before, but they claim to be park-isolated now and show a regression against their park factors to prove it.  Of course the key word in that claim is THEIR park factors.  I reran the numbers from the linked post with the new DRC+s, and while they have made an improvement, they’re still correlated to both Fangraphs park factor and my surrounding-years park factor estimate at the r=0.17-0.18 level, with all that entails (still overrating Rockies hitters, for one, just not by as much).

 

DRC+ and Team Wins

A reader saw a television piece on DRC+, googled and found this site, and asked me a simple question: how does a DRC+ value correlate to a win? I answered that privately, but it occurred to me that team W-L record was a simple way to test DRC+’s claim of superior descriptiveness without having to rely on its false claim of being park-adjusted.

I used seasons from 2010-2018, with all stats below adjusted for year and league- i.e. the 2018 Braves are compared to the 2018 NL average.  Calculations were done with runs/game and win% since not all seasons were 162 games.

Team metric r^2 to team winning %
Run Differential 0.88
wRC+ 0.47
Runs Scored 0.43
OBP 0.38
wOBA 0.37
OPS 0.36
DRC+ 0.35

Run differential is cheating of course, since it’s the only one on the list that knows about runs allowed, but it does show that at the seasonal level, scoring runs and not allowing them is the overwhelming driver of W-L record and that properly matching RS to RA- i.e. not losing 5 1-run games and winning a 5-run game to “balance out”- is a distant second.

Good offense is based on three major things- being good, sequencing well, and playing in a friendly park.  Only the first two help you to outscore your opponent who’s playing the game in the same park, and Runs Scored can’t tell the difference between a good offense and a friendly park.  As it turns out, properly removing park factor noise (wRC+) is more important than capturing sequencing (Runs Scored).

Both clearly beat wOBA, as expected, because wRC+ is basically wOBA without park factor noise, and Runs Scored is basically wOBA with sequencing added.  OBP beating wOBA is kind of an accident- wOBA *differential* would beat OBP *differential*- but because park factor is more prevalent in SLG than OBP, offensive wOBA is more polluted by park noise and comes out slightly worse.

And then there’s DRC+.  Not only does it not know sequencing, it doesn’t even know what component events (BB, 1B, HR, etc) actually happened, and the 25% or so of park factor that it does neutralize is not enough to make up for that.  It’s not a good showing for the fancy new most descriptive metric ever when it’s literally more valuable to know a team’s OBP than its DRC+ to predict its W-L record, especially when wRC+ crushes the competition at the same task.

 

Mashers underperform xwOBA on air balls

Using the same grouping methodology as The Statcast GB speed adjustment seems to capture about 40% of the speed effect, except using barrel% (barrels/batted balls), I got the following for air balls (FB, LD, Popup):

barrel group FB BA-xBA FB wOBA-xwOBA n
high-barrel% 0.006 -0.005 22993
avg 0.006 0.010 22775
low-barrel% -0.002 0.005 18422

These numbers get closer to the noise range (+/- 0.003), but mashers simultaneously OUTPERFORMING on BA while UNDERPERFORMING on wOBA while weak hitters do the opposite is a tough parlay to hit by chance alone because any positive BA event is a positive wOBA event as well.  The obvious explanation to me, which Tango is going with too, is that mashers just get played deeper in the OF, and that that alignment difference is the major driver of what we’ve each measured.

 

The Statcast GB speed adjustment seems to capture about 40% of the speed effect

Statcast recently rolled out an adjustment to its ground ball xwOBA model to account for batter speed, and I set out to test how well that adjustment was doing.  I used 2018 data for players with at least 100 batted balls (n=390).  To get a proxy for sprint speed, I used the average difference between the speed-unadjusted xwOBA and the speed-adjusted xwOBA for ground balls.  Billy Hamilton graded out fast.  Welington Castillo didn’t.  That’s good.  Grouping the players into thirds by their speed-proxy, I got the following

 

speed Actual GB wOBA basic xwOBA speed-adjusted xwOBA Actual-basic Actual- (speed-adjusted) n
slow 0.215 0.226 0.215 -0.011 0.000 14642
avg 0.233 0.217 0.219 0.016 0.014 16481
fast 0.247 0.208 0.218 0.039 0.029 18930

The slower players seem to hit the ball better on the ground according to basic xwOBA, but they still have worse actual outcomes.  We can see that the fast players outperform the slow ones by 50 points in unadjusted wOBA-xwOBA and only 29 points after the speed adjustment.

 

DRC+ isn’t even a hitting metric

At least not as the term is used in baseball.  Hitting metrics can adjust for nothing (box score stats, AVG, OBP, etc), league and park (OPS+, wRC+, etc), or more detailed conditions (opposing pitcher and defense, umpire, color of the uniforms, proximity of Snoop Dogg, whatever).  They don’t adjust for the position played.  Hitting is hitting, regardless of who does it.  Unless it’s not.  While fooling around with the data for DRC+ really isn’t any good at predicting next year’s wOBA for team switchers and The DRC+ team-switcher claim is utter statistical malpractice some more, it looked for all the world like DRC+ had to be cheating, and it is.

To prove that, I looked at seasons with exactly 1 PA and 1 unintentional walk for the entire season, and the DRC+ for those seasons.

NAME
YEAR
TEAM
DRC+
DRC+ SD
Audry Perez
2014
Cardinals
104
20
Spencer Kieboom
2016
Nationals
96
29
John Hester
2013
Angels
93
16
Joey Gathright
2011
Red Sox
89
24
J.c. Boscan
2010
Braves
78
25
Mark Melancon
2011
Astros
15
14
George Sherrill
2010
Dodgers
4
23
Antonio Bastardo
2014
Phillies
3
22
Dan Runzler
2011
Giants
2
19
Jose Veras
2011
Pirates
1
15
Matt Reynolds
2010
Rockies
1
12
Tony Cingrani
2016
Reds
0
25
Antonio Bastardo
2017
Pirates
-1
17
Javy Guerra
2011
Dodgers
-2
31
Josh Stinson
2011
Mets
-10
11
Aaron Thompson
2011
Pirates
-12
14
Brandon League
2013
Dodgers
-13
17
J.j. Hoover
2014
Reds
-14
32
Santiago Casilla
2011
Giants
-15
12
Jason Garcia
2015
Orioles
-16
12
Chris Capuano
2016
Brewers
-17
17
Edubray Ramos
2016
Phillies
-19
15
Matt Guerrier
2011
Dodgers
-22
9
Liam Hendriks
2015
Blue Jays
-24
15
Phillippe Aumont
2015
Phillies
-28
20
Randy Choate
2015
Cardinals
-28
52
Joe Blanton
2017
Nationals
-30
12
Jacob Barnes
2017
Brewers
-31
26
Sean Burnett
2012
Nationals
-33
20
Robert Carson
2013
Mets
-43
7

That’s a pretty good spread.  The top 5 are position players, the rest are pitchers.  DRC+ is blatantly cheating by assigning pitchers very low DRC+ values even when their offensive performance is good and not doing the same for 1-PA position players.  wOBA and wRC+ don’t do this, as evidenced by Kieboom (#5) right there with 3 pitchers with the same seasonal stat line.  It’s also not using data from prior seasons because that was Kieboom’s only career PA to date, and when Livan Hernandez debuted in 1996 for one game with 1 PA and 1 single, he got a DRC+ of -14 for his efforts.  It’s just cheating, period.  And it doesn’t learn either.  Even when Bumgarner was hitting in 2014-2017, his DRC+s were -15, 4, -17, and -19.

I also included the DRC+ SDs here just to show that they’re complete nonsense.  Pitcher Mark Melancon (15 +/- 14) has one career PA. Pitcher Robert Carson (-43 +/- 7) also has one career PA. Pitcher Randy Choate (-28 +/- 52) had one PA that year and 5 a decade earlier.  What in the actual fuck?

The entire DRC+ project is a complete farce at this point.  The outputs are a joke***  The SD values are nonsense (table above). The pillars it stands on are complete bullshit.  It’s more descriptive of the current season than park adjusted stats because it’s not anywhere near a park-adjusted stat, even though it claims to be.  It’s more predictive than park-adjusted stats for next year’s team because it’s somewhat regressed, meaning it basically can’t lose, and it’s also cheating the same way descriptiveness does by keeping a bunch of park factor.  Its claimed “substantial improvement over predicting wOBA for team switchers” is statistical malpractice to begin with, and now we see that the one area where it did predict significantly better than regressed wOBA, very-low-PA players, is driven by (almost) ignoring actual results for pitchers and saying they sucked at the plate no matter how well they really hit (and treating low-PA position players with the exact same stat lines as average-ish).

***Check out DRA- land where Billy Wagner is 26 percent more valuable on a per-inning basis than Mariano Rivera and almost as valuable for his career.  I love Billy Wagner, but still, come on.

RIP 12/29/2018.  Comment F to pay respects.

 

DRC+ still contains a lot of park factor

Required knowledge: DRC+ and park factors

TL;DR read the title above, the rant 3 paragraphs down, and the very bottom

DRC+ is supposed to be a fully park-adjusted metric, but from the initial article, I couldn’t understand how that could be consistent with the reported results without either an exceptional amount of overfitting or extremely good luck.  Team DRC+ was reported to be more reliable than team wRC+ at describing the SAME SEASON’s team runs/PA.  Since wRC+ is based off of wOBA, team wOBA basically is team scoring offense (r=0.94), and DRC+ regresses certain components of wOBA back towards the mean quite significantly (which is why DRC+ is structurally unfit for use in WAR), it made no sense to me that a metric that took away actual hits that created actual runs from teams with good BABIPs and invented hits in place of actual outs for teams with bad BABIPs could possibly correlate better to actual runs scored than a metric that used what happened on the field.  It’s not quite logically impossible for that to be true, but it’s pretty damn close.

It turns out the simple explanation for how a park-adjusted significantly regressed metric beat a park-adjusted unregressed metric is the correct one.  It didn’t. DRC+ keeps in a bunch of park factor and calls itself a park-adjusted metric when it’s simply not one, and not even close to one.  The park factor table near the bottom of the DRC+article should have given anybody who knows anything about baseball serious pause, and of course it fits right in with DRC+’s “great descriptiveness”.

RANT

How in the hell does a park factor of 104 for Coors get published without explanation by any person or institution trying to be serious?  The observed park factors (halved) the last few years, in reverse order: 114 (2018), 115, 116, 117, 120, 109, 123… You can’t throw out a number like Coors 104 like it’s nothing.  If Jonathan Judge could actually justify it somehow- maybe last year we got a fantastic confluence of garbage pitchers and great situational hitting at Coors and the reverse on the road while still somehow only putting up a 114, where you could at least handwave an attempt at a justification, then he should have made that case when he was asked about it, but instead he gave an answer indicative of never having taken a serious look at it.  Spitting out a 104 for Coors should have been like a tornado siren going off in his ear to do basic quality control checks on park effects for the entire model, but it evidently wasn’t, so here I am doing it instead.

/RANT

The basic questions are “how correlated is team DRC+ to home park factor?” and “how correlated should team DRC+ be to home park factor?”.  The naive answer to the second question is “not correlated at all since it’s park adjusted, duh”, but it’s possible that the talented hitters skew towards hitters’ parks, which would cause a legitimate positive correlation, or that they skew towards pitchers’ parks, which would cause a legitimate negative correlation.  As it turns out, over the 2003-2017 timeframe, hitting talent doesn’t skew at all, but that’s an assertion that has to be demonstrated instead of just assumed true, so let’s get to it.

We need a way to make (offensive talent, home park factor) team-season pairs that can measure both components separately without being causally correlated to each other.  Seasonal team road wOBA is a basically unbiased way to measure offensive quality independent of home park factor because the opposing parks played in have to average out pretty similarly for every team in the same league (AL/NL)**.  If we use that, then we need a way to make a park factor for those seasons that can’t include that year’s data, because everything else being equal, an increase in a team’s road wOBA would decrease its home park factor****, and we’re explicitly trying to avoid nonsense like that.  Using the observed park factors from *surrounding years*, not the current year, to estimate the current year’s park factor solves that problem, assuming those estimates don’t suck.

** there’s a tiny bias from not playing road games in a stadium with your park factor, but correcting that by adding a hypothetical 5 road games at estimated home park factor doesn’t change conclusions)

**** some increase will be skill that will, on average, increase home wOBA as well and mostly cancel out, and some increase will be luck that won’t cancel out and would screw the analysis up

Methodology

I used all eligible team-batting-seasons, pitchers included, from 2003-2017.  To estimate park factors, I used the surrounding 2 years (T-2, T-1, T+1, T+2) of observed park factors (for runs) if they were available, the surrounding 1 year (T-1, T+1) otherwise, and threw out the season if I didn’t have those.  That means I threw out all 2018s as well as the first and last years in each park.  I ignored other changes (moved fences, etc).

Because I have no idea what DRC+ is doing with pitcher-batters, how good its AL-NL benchmarking is, and the assumption of nearly equivalent aggregate road parks is only guaranteed to hold between same-league teams, I did the DRC+ analysis separately for AL and NL teams.

To control for changing leaguewide wOBA in the 2003-2017 time period, I used the same wOBA/LgAvGwOBA wOBA% method I used in DRC+ really isn’t any good at predicting next year’s wOBA for team switchers for wOBA and DRC+, just for AL teams and NL teams separately for the reasons above.  After this step, I did analyses with and without Coors because it’s an extreme outlier.  We already know with near certainty that their treatment of Coors is kind of questionable batshit crazy and keeps way too much park effect in DRC+, so I wanted to see how they did everywhere else.

Results

The park factor estimation worked pretty well.  2 surrounding year PF correlated to the  observed PF for the year in question at r=0.54 (0.65 with Coors) and the 1 surrounding year at r=0.52 (0.61 with Coors).  The 5-year FanGraphs PF, WHICH USES THE YEAR IN QUESTION, only correlates at r=0.7 (0.77 with Coors) and the 1 and 2 year park factors correlate to the Fangraphs PF at 0.87 and 0.96 respectively.  This is plenty to work with given the effect sizes later.

Team road wOBA% (squared or linear) correlates to the estimated home park factor at r = -0.03, literally nothing, and with the 5 extra hypothetical games as mentioned in the footnote above, r=0.02, also literally nothing.  It didn’t have to be this way, but it’s convenient that it is.  Just to show that road wOBA isn’t all noise, it correlates to that season’s home wOBA% at r=0.32 (0.35 with the adjustment) even though we’re dealing with half seasons and home wOBA% contains the entire park factor.  Road wOBA% correlates to home wOBA%/sqrt(estimated park factor) at r=0.56 (and wOBA%/park factor at r=0.54).  That’s estimated park factor from surrounding years, not using the home and road wOBA data in question.

Home wOBA% is obviously hugely correlated to estimated park factor (r=0.46 for home wOBA%^2 vs estimated PF), but park adjusting it by correlating

(home wOBA%)^2/estimated park factor TO estimated park factor

has r= -0.00017.  Completely uncorrelated to estimated PF (it’s pure luck that it’s THAT low).

So we’ve established that road wOBA really does contain a lot of information on a team’s offensive talent (that’s a legitimate naive “duh”), that it’s virtually uncorrelated to true home park factor, and that park-adjusted home wOBA% (using PF estimates from other seasons only) is also uncorrelated to true home park factor.  If DRC+ is a correctly park-adjusted metric that measures offensive talent, DRC+% should also have to be virtually uncorrelated to true home park factor.

And… the correlation of DRC+% to estimated park factor is r= 0.38 for AL teams, r=0.29 for NL teams excluding Colorado, r=0.31 including Colorado.  Well then.  That certainly explains how it can be more descriptive than an actually park-adjusted metric.

 

The DRC+ team-switcher claim is utter statistical malpractice

Required knowledge: MUST HAVE READ/SKIMMED DRC+ really isn’t any good at predicting next year’s wOBA for team switchers and a non-technical knowledge of what a correlation coefficient means wouldn’t hurt.

In doing the research for the other post, it was baffling to me what BP could have been doing to come up with the claim that DRC+ was a revolutionary advance for team-switchers.  It became completely obvious that there was nothing particularly meaningful there with respect to switchers and that it would take a totally absurd way of looking at the data to come to a different conclusion.  With that in mind, I clicked some buttons and stumbled into figuring out what they had to be doing wrong.  One would assume that any sophisticated practitioner doing a correlation where some season pairs had 600+ PA each and other season pairs had 5 PA each would weight them differently… and one would be wrong.

I decided to check 4 simple ways of weighting the correlation- unweighted, by year T PA, by year T+1 PA, and by the harmonic mean of year T PA and year T+1 PA.

Table 1.  Correlation coefficients to year T+1 wOBA% by different weighting methods, minimum 400 PAs year T.

400+ PA Harmonic Year T PA Year T+1 PA unweighted N
switch wOBA 0.34 0.35 0.34 0.34 473
switch DRC+ 0.35 0.35 0.34 0.35 473
same wOBA 0.55 0.53 0.55 0.51 1124
same DRC+ 0.57 0.55 0.57 0.54 1124

The way to read this chart is to compare the wOBA and DRC+ correlations for each group of hitters- switch to switch (lines 1 and 2) and same to same (lines 3 and 4).  It’s obvious that wOBA should correlate much better for same than switch because it contains the entire park effect which is maintained in “same” and lost in “switch”, but DRC+ behaves the same way because DRC+ also contains a lot of park factor even though it shouldn’t

In the 400+ year T PA group, the choice of weighting method is almost completely irrelevant. DRC+ correlates marginally better across the board and it has nothing to do with switch or stay.  Let’s add group 2 to the mix and see what we get.

Table 2.  Correlation coefficients to year T+1 wOBA% by different weighting methods, minimum 100 PAs year T.

100+ PA Harmonic Year T PA Year T+1 PA unweighted N
switch wOBA 0.31 0.29 0.29 0.26 1100
switch DRC+ 0.33 0.31 0.32 0.29 1100
same wOBA 0.51 0.47 0.50 0.44 2071
same DRC+ 0.54 0.51 0.53 0.47 2071

The values change, but DRC+’s slight correlation lead doesn’t, and again, nothing is special about switchers except that they’re overall less reliable. Some of the gaps widen by a point or two, but there’s no real sign of the impending disaster when the low-PA stuff that favors DRC+ comes in.  But what a disaster there is….

Table 3.  Correlation coefficients to year T+1 wOBA% by different weighting methods, all season pairs.

1+ PA Harmonic Year T PA Year T+1 PA unweighted N
switch wOBA 0.45 0.41 0.38 0.37 1941
switch DRC+ 0.54 0.47 0.58 0.57 1941
same wOBA 0.62 0.58 0.53 0.52 3639
same DRC+ 0.67 0.62 0.66 0.66 3639

The two weightings (Harmonic and Year T) that minimize the weight of low-data garbage projections stay saner, and the two methods that don’t (year T+1 and unweighted) go bonkers and diverge by around what BP reports, If I had to guess, I have more pitchers in my sample for a slightly bigger effect and regressed DRC+% correlates a bit better.  And to repeat yet again, the effect has nothing to do with stay/switch.  It’s entirely a mirage based on flooding the sample with bunches of low-data garbage projections based on handfuls of PAs and weighting them equally to pairs of qualified seasons.

You might be thinking that that sounds crazy and wondering why I’m confident that’s what really happened.  Well, as it turns out- and I didn’t realize this until after the analysis- they actually freaking told us that’s what they did.  The caption for the chart is “Table 3: Reliability of Team-Switchers, Year 1 to Year 2 wOBA (2010-2018); Normal Pearson Correlations”.  Normal Pearson correlations are unweighted. Mystery confirmed solved.