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Jonathan Lucroy: A No-Brainer for NL MVP

Jonathan Lucroy deserves the NL MVP.  I’ll try to make this short, but first I’ll need to discuss the factors I consider important for MVP candidacy.

Beyond WAR

WAR is a good starting point, but does not give the full picture of a player’s performance in a given year.  It does a great job at combining a hitter’s contributions (hitting, baserunning, defense) across the same units (runs and wins) to allow us to compare players who impact the game and add value in different ways, as well as adjusting for park and league factors, etc.  I also like talking about players in terms of how many wins they add, and the notion of comparing players to replacement level (readily available talent, in theory) as opposed to average has a lot of merit.  That said, there are still things that go uncaptured in WAR (in some cases, as with context/sequencing, this is by design) that make it incomplete when evaluating a player’s MVP candidacy.

For starters, WAR is context-neutral.  In my opinion, context matters.  Others may disagree, and do every time I bring this up, even though I’m saying precisely what others have acknowledged, which is that context is relevant for a backward-looking evaluation of value added to a team.  Take two guys of equal “true talent” levels; if the first guy happens to get more opportunities in high-leverage situations, and/or happens to cluster more of his offensive production in said situations, he’s adding more value than the second guy, if the second guy comes up in the proportionally expected number of high-leverage situations and performs no differently in those situations than low-leverage situations.  Do I project them to repeat their same trends the next year?  No.  But I’m pretty firm in my take that the first guy added more value over the season in question.

Furthermore, WAR does not capture all elements of a player’s contribution.  The most glaring omission at the current time is pitch framing.  Whether or not you believe pitch framing should be a part of the game (which I don’t — use a computer to call a consistent zone already!), it is part of the game, it does have value, and teams do appear to factor it into their evaluation of players.

Let’s look at the top NL position players, starting out with WAR:

Name Batting Base Running Offense Defense RAR WAR WPA Clutch
Andrew McCutchen 49.6 1.5 51.1 -8.6 61.7 6.8 5.22 -0.3
Anthony Rendon 23.2 7.4 30.7 9.2 60.1 6.6 1.42 -1.55
Jonathan Lucroy 24.3 -1 23.4 14.6 57.4 6.3 3.84 1.65
Giancarlo Stanton 42.1 -0.6 41.5 -5.1 55.3 6.1 5.56 -0.66
Carlos Gomez 23.5 3.3 26.8 7.8 53.7 5.9 1.22 -2.04
Buster Posey 29.8 -3 26.8 7 51.8 5.7 4.87 1.75

Lucroy’s got some ground to make up in the WAR department.


Taking some context into account though, he was significantly more clutch than the other candidates — in fact, the only other player with a positive Clutch score is Posey. The two catchers were the only ones who turned in better performances in higher-leverage situations.  It should be noted that other hitters (Stanton and McCutchen) put up a higher WPA, but this is expected for players whose value comes almost entirely from hitting (WPA measures hitting almost exclusively).  Posey and Lucroy added more value (created more runs) than their WAR represents due to sequencing; all the other candidates added less value.

Pitch Framing

Using Baseball Prospectus’s numbers, Lucroy added 23.3 runs through framing and blocking (almost entirely from framing; in fact his blocking was just slightly negative).  Posey added 13.7.  If we make a back-of-the-envelope calculation of 9.1 Runs Per Win in the NL this year, those come out to 2.6 Wins for Lucroy and 1.5 Wins for Posey.

Add it all up

Taking both context and pitch framing into account easily vaults Lucroy past the other contenders.  I don’t claim to have a perfect method of converting “Clutch” to the same units as WAR (Runs or Wins); one could use something like, the difference between “expected” WPA given a player’s Batting (based on league-wide correlation between Batting and WPA), and then look at their actual WPA, and add the difference to their WAR.  Such a system would give both Lucroy and Posey a bump of 1-1.5 Wins, while penalizing Rendon and Gomez pretty heavily.

Likewise for pitch framing, I’m not comfortable giving the catcher 100% credit for runs saved via framing (which by extension means removing the associated WAR from the pitcher), but based on my subjective opinion from watching good framers and bad framers and the skills they possess, I’m certainly comfortable giving at least half the value to the catcher, probably more.  So again, we’re talking another 1-2 Wins for Lucroy.  By my count, that puts him at upwards of 8-9 Wins, with the rest of the field not coming close.  Posey also sets himself apart from the non-catchers by virtue of both framing and clutching, but not enough to catch Lucroy.

It’s important to call out that using framing isn’t always going to mean a catcher will inevitably win the MVP.  There are plenty of years that the best catcher is not particularly close to the best position players in terms of WAR.  In 2013, Yadier Molina was 2.7 WAR behind MVP McCutchen, a gap too large for pitch framing to cover.  In 2012, Posey had the highest WAR even without framing.  In 2011, Molina had the highest WAR among catchers at 4.4, a full 4.0 behind Matt Kemp (who didn’t win the MVP…).  The same is true in the AL.  Using pitch framing doesn’t mean the MVP is suddenly going to start vaulting catchers over 10+ WAR guys like 2012-13 Mike Trout (we’ll leave that to other position players…).  It just happens that this year, we have two NL catchers who both happen to have exhibited clutch hitting (and who are good hitters in their own right), and who add significant value with their ability to receive the ball in such a way as to convince the umpire to call a strike more often than average.

That other guy

I’m not the type to say pitchers can’t win the MVP, and won’t resort to the “they only play every 5 days!” argument.  Clayton Kershaw has been dominant.  And, if you’re the type, it can be argued he led his team to win their division, while Lucroy’s headed home in September.  Bottom line for me though: Lucroy added more value to his team, using the units of Wins.  He’s a no-brainer for MVP.

All that said, Lucroy has absolutely no chance of winning the MVP.  The rationale in this post is in no way the mindset of the voters and he doesn’t stand a chance.

Why King Felix Will Win the Cy Young, But Shouldn’t

Corey Kluber deserves the AL Cy Young.  Corey Kluber will not win the AL Cy Young.  Felix Hernandez got off to a hot start, establishing himself early as the best pitcher in the AL, earning himself the starting job in the All-Star game (Kluber was not even an All-Star), and even inserting himself into the MVP discussion as late as mid-August, which will be enough to carry him to this year’s award.  The first half comparison:

Felix Hernandez 11 2 20 144.1 9.6 1.56 0.31 0.271 73.00% 54.30% 5.20% 2.12 2.03 2.4 4.9
Corey Kluber 9 6 20 131.2 9.71 2.19 0.68 0.326 75.70% 48.50% 8.90% 3.01 2.78 2.85 3.3

Felix was the best in the AL.  Since then, Kluber has been the best in the AL:

Corey Kluber 9 3 14 104 10.99 1.64 0.35 0.302 83.00% 47.30% 5.20% 1.73 1.8 2.21 4.1
Felix Hernandez 4 4 14 91.2 9.23 2.06 1.08 0.237 84.00% 59.10% 17.50% 2.16 3.39 2.68 1.4

And the season totals:

Corey Kluber 18 9 34 235.2 10.27 1.95 0.53 0.316 78.60% 48.00% 7.40% 2.44 2.35 2.57 7.3
Felix Hernandez 15 6 34 236 9.46 1.75 0.61 0.258 77.00% 56.20% 10.10% 2.14 2.56 2.51 6.2

Both the Indians and Mariners were teams in the playoff hunt that ultimately fell short.  If you’re into narratives (and/or small sample sizes), here’s September, just for kicks:

Corey Kluber 5 1 6 43 11.72 1.47 0.63 0.34 83.70% 44.90% 10.30% 2.09 1.92 1.85 1.6
Felix Hernandez 2 1 6 38 10.18 2.61 0.71 0.239 78.60% 55.90% 13.00% 1.66 2.76 2.49 0.8

Felix certainly didn’t do the Mariners any favors down the stretch; the Mariners shuffled their rotation around based on opponents and off days specifically to ensure they’d have Felix going on 9/8, 9/13, 9/18, 9/23, and 9/28 (the final game of the year) in anticipation of needing him that final game, and he did not deliver.  Felix’s ERA was held down thanks to a scoring change in the 9/23 game (based on an error he himself made, no less) that turned 4 earned runs into unearned runs – hence the far higher FIP – but overall Felix underperformed in September.

Looking at the overall body of work in 2014, we see very similar lines.  Their IP, GS, and xFIP are almost identical.  We see a slightly better FIP for Kluber, and a better ERA for Felix, primarily explainable by Safeco Field and his lower BABIP (which in turn is primarily explainable by the Mariners’ superior defense and the subpar Indians’ defense).  We see a significantly better strikeout rate for Kluber which more than makes up for his slightly higher walk rate, and a markedly higher HR rate for Felix despite playing in HR-suppressing Safeco Field.

Add it all up, and Kluber’s performance ends up markedly better than Felix’s.  Even if you don’t care about the narrative and Felix’s choking down the stretch, Kluber was the best pitcher in the AL this year.

King Felix will win the Cy Young because of his hot start, the media exposure he got throughout the season, his All-Star performance, and his ERA title (for which he should thank Safeco Field, his defense, the league scorers, and to a lesser extent his bullpen) – but he won’t deserve it.

The Straw Man of the Pitcher-for-MVP “Debate”

There has been much discussion lately regarding the people who hold the belief that pitchers are not deserving candidates for the MVP award.  What I don’t see is very many people who actually come out and say pitchers don’t deserve the MVP award.  Perhaps, in my daily consumption of hours of baseball news, analysis, and commentary across various media, I am somehow missing out on a significant sector or demographic that holds this belief, and so it is in fact more prevalent than what I observe, but in reality it appears that very few consider it to be such a black-and-white issue.

In fact, I would argue that both the sabermetric community and the less-analytically-inclined community both agree that it is a gray area, but approach it in different ways.

In Ken Rosenthal’s recent post on the topic, he points out that it is far from black-and-white; the last time we had a pitcher named MVP (Verlander in 2011), he was on 27 of 28 ballots.  So maybe there is one sportswriter in 28 or so who believes pitchers shouldn’t be MVPs.  Although, we shouldn’t even assume said writer would never vote for a pitcher; maybe he just felt it wasn’t Verlander’s year.

In fact 2011 was an interesting year (especially for those WAR-lubbers), in that (non-MVP) Roy Halladay in the NL had a WAR of 8.1, which was ahead of NL MVP Ryan Braun’s 7.2 (though not ahead of non-MVP and non-cheater Matt Kemp’s 8.4!).  Over in the AL, Ellsbury’s WAR was 9.1 compared to Verlander’s 6.9.  In fact 10 AL hitters had a WAR of 6.3 or greater.

On the flip side, take Jeff Sullivan’s recent post:

Say the best position player comes in around 8. Say the best pitcher comes in around 8. Say, for simplicity, that all of the different WARs are even in agreement. Doesn’t that function as a conversation-ender? You can always debate a given individual’s WAR, but doesn’t that rather matter-of-factly put pitchers and position players on the same scale?

Overall I’m very much in the camp that pitchers deserve the MVP.  But we do need to acknowledge that WAR is based an up-front division of the 1000 WAR given out per season, with 43% going to pitchers and 57% going to hitters.  It’s not that these numbers are arbitrary; a great deal of thought has been put into how to value the relative contributions of various positions (WAR’s positional adjustments are in a similar vein), and this is an interesting problem across all team sports.

Nevertheless, it holds true that in any given year, the top WAR leaders tend to be position players.  When people make sweeping statements like “position players play every day, starters only play every 5 days,” I don’t think (many of) those people are unwilling to acknowledge that starters’ contributions on the day they pitch are far more impactful than position players’ contributions; they’re just saying that in general, they see more cases where the best position players are the most valuable to their teams than the best starting pitchers — which is exactly what the WAR leaderboards say as well.

Regarding the valuation of different positions in team sports: often times, the nature of the game is such that certain positions are inherently more impactful; this ends up being a great example of why replacement level is an invaluable tool.  Consider the case of kickers in the NFL.  Suppose we modified the rules so that touchdowns didn’t immediately award 6 points; rather, it gave the scoring team the opportunity to kick an extra point that was worth 7 points.  Would this make kickers more valuable?  It certainly would make them more important, but I’m not convinced kickers’ salaries would change much.  The difference between the success rates of the best kicker in the league and the worst kicker in the league (or a replacement-level kicker) would be very small — they all make extra points about 99.7% of the time.  You’d still care more about having offensive players who can score those touchdowns (and defensive players who can prevent touchdowns).

Now, if the rules were different, and that “7-point-extra-point” actually had to be kicked from 58 yards deep, then there would suddenly be a huge difference between the success rates of the best kickers and the replacement-level kickers.  The kickers capable of hitting those 7-pointers at a high success rate would suddenly command enormous contracts and be kings of the league.

To me this is the essence of the Pitcher-for-MVP Debate: almost everyone agrees that as a whole, pitchers are less valuable than hitters.  We give hitters more WAR and bigger contracts.  That doesn’t mean there aren’t years where the best pitcher isn’t better than the best hitter, but almost everyone, sabermetrically-inclined or not, seems to come to the conclusions that in general, “position players have more impact.”

Mike Trout and the MVP

In 2012 and 2013, Mike Trout was considered by most in the sabermetric community to be the most valuable player in the American League.  That Miguel Cabrera ended up winning in both years was the source of much debate and consternation, to say the least.  Analytically-inclined fans and writers were fed up, frustrated, and outright angry with the “old school” writers voting for Cabrera based on a different set of values.  Now, in an amusing twist, it appears that this year Trout has his best chance yet to wind up with the award, in large part by having a season that is less aligned with what the sabermetric community values, and more aligned with what the majority of the voting population values.  I took a look at the changes in various aspects of Trout’s game and analyzed how the regressions/improvements will impact his candidacy, based on what voters traditionally have cared about.


A large part of Trout’s previous MVP candidacy (particularly in 2012) centered on his defense — an area that traditionally has had fewer metrics to quantify a player’s value (as compared to say, hitting).  In 2012, DRS had Trout as worth 21 runs above average; UZR had him at 13.3.

In 2013, Trout’s defensive value declined to the point where he was worth -9 runs by DRS and +4.4 runs by UZR.  This discrepancy was a major reason why Baseball-Reference’s DRS-based WAR for Trout was 8.9 while FanGraphs’ UZR-based WAR was 10.5.

This year, Trout’s worth -6 by DRS and -7.2 by UZR.

In actuality, it didn’t take a rocket scientist to predict this regression; Trout’s arm has been consistently slightly below average, and his range ended up over-contributing in 2012 thanks to a handful of plays that broke his way.  Interestingly enough, the sabermetric crowd didn’t call any attention to this detail in 2012, choosing instead to use Trout’s defensive numbers to bolster their MVP case; now this year they’re bending over backwards to try to discredit Alex Gordon’s defensive numbers so they can justify giving the MVP to Trout as they’ve hoped to be able to do all season long…but that’s a post for a different day.


Likewise in 2012, Trout’s baserunning was valued at 12 runs above average, which included his other-worldly 49 SB and 5 CS.  In 2013, his baserunning added 8.1 runs, including 33 SB and 7 CS — still a great 82.5% success rate.

This year, Trout’s been worth all of 1.5 runs on the bases, with just 13 SB and 2 CS.


Trout’s offense is down slightly, but not nearly to the extent that his defense and baserunning have been.  Like his defense, this regression was fairly predictable, given Trout’s unsustainably high BABIP in 2012 and 2013.  His OPS is down to 0.934 compared to 0.963 and 0.988 in 2012 and 2013, but he still has plenty else to hang his hat on: he leads the league in total bases; he’s already hit 30 homers, a total he hasn’t surpassed before; and, with 94 RBIs, he’ll easily pass that magical/meaningless 100 threshold soon as well.  The voters as a whole still like HRs, RBIs, and round numbers.

Clutch Hitting

In previous years, Trout was criticized (at least by me!) for not getting hits in key situations.  Here are Trout’s offensive splits with Bases Empty versus with Runners on Base:

 Year  Split  BABIP  OPS  tOPS+
 2012  Empty  0.403  0.985
 2012  RoB  0.343  0.917  90
 2013  Empty  0.399  1.023
 2013  RoB  0.339  0.934  90
 2014  Empty  0.343  0.916
 2014  RoB  0.348  0.944  104

In 2012-2013, he performed significantly worse with runners on.  Presumably most folks here would no doubt cling to the notion that this is entirely luck, and that sequencing like this is entirely unpredictable and out of players’ control.  I argue that even if so, if we’re talking about how much value a player added to his team in a given year, he’s adding more value in years when he gets clutch hits than in years when he doesn’t.  And this year, he’s actually reversed the trend.  His 2014 WPA of 5.52 has already exceeded his 2012 and 2013 marks of 5.32 and 4.60.

The Field

Fortunately for Trout this year, there haven’t been many other position players giving him a run for his money.  Josh Donaldson has cooled off as expected after a hot start.  Alex Gordon’s case is even more heavily dependent on defensive metrics than Trout’s was in 2012, and I don’t see many voters slotting him above Trout.  After that, I just don’t see the award going to Robinson Cano or Kyle Seager (the only other 2 AL players in the top 10 for position player WAR as of this writing), unless Cano truly catches fire in September and leads the Mariners to the playoffs.  In fact Trout’s best competition for the MVP may well end up being a pitcher (another Mariner, no less!), Felix Hernandez.  And we know how hard it is for a pitcher to win the MVP even when his WAR outpaces that of position players (“They only pitch every 5 days!”).


Last and perhaps most importantly, I present the Angels’ records and division finishes over the past 3 seasons:

2012: 89-73, 3rd

2013: 78-84, 3rd

2014: 81-53, 1st (through 8/30)

FanGraphs gives the Angels a 99.9% chance of making the playoffs.  In fact, as of this writing, no other team in baseball has more than 78 wins, while the Angels have 81.  This should finally appease the “MVPs should lead their team to the playoffs” voters.

The Vote

So Trout’s hitting is slightly down and his defense and baserunning are way down from when he had his previous “MVP-caliber” seasons.  Fortunately for Trout, the voters by and large don’t value defense and baserunning as much as they probably should (though that’s starting to change, albeit slowly).  And as for hitting being down, 2014 Trout is doing more of what they value: hitting homers and driving in runs.  The only thing that might work against him is if he doesn’t bat .300 (he’s at .290 as of now), and the voters like nice round numbers (and they value BA over newfangled mumbo-jumbo like OBP and OPS).  Overall though, with the Angels in line for their first playoff spot since 2009 and no other traditional MVP-makeup players in the field, Trout seems like a shoo-in.

 Criteria  As Compared to 2012-2013  Do Voters care?
 Defense  Way Down  Not much
 Baserunning  Way Down  Not much
 Overall Hitting  Somewhat down  Somewhat
 HRs, RBIs  Up  Yes
 Playoffs  Angels in much better position  Yes
 Field  Not as many standouts as 2012-2013(Alex Gordon != Miguel Cabrera)  Yes

So there you have it: Trout will win the AL MVP award for all the wrong reasons.

The A’s and Hitting With Men On Base

Earlier this month I wrote about how the A’s front office is currently outpacing their competition when it comes to roster construction.  I focused primarily on how they’ve taken the platoon advantage to another level, loading up on defensively versatile players to allow for day-to-day lineup construction that maximizes the number of plate appearances where their hitters have the platoon advantage.  As a result of this, they get 70% of their PAs with the platoon advantage, as compared to the league average of 55%.  As part of my investigation into the platoon splits of A’s players, I also noticed another split of interest: offensive performance with runners on base as compared to with the bases empty.  After investigation, I’ve concluded that the A’s have identified and targeted players that have higher offensive production with runners on base.

League-wide trends
First, it should be noted that in general, everyone hits better with runners on base.  There are two primary reasons for this.  The first is sampling bias: if runners are on base, you’re more likely to be facing an inferior pitcher, as such pitchers allow more baserunners and hence face proportionally more batters with runners already on base.  Second, the defense is concerned with more than just the current batter.  With the bases empty, the defense presumably aligns themselves to maximize the chances of getting the batter out (or, more precisely, to minimize the overall output of the batter).  With runners on, there are other considerations – ensuring that the runners don’t steal, for example – that change the defensive alignment.  As a result, a given ball in play is more likely to be a hit if there are runners on base.  League-wide in 2014, the numbers look like this:

  PA OPS BAbip tOPS+
Bases Empty 80375 0.687 0.296 95
Runners on Base 61905 0.725 0.302 106

tOPS+ is a measure of the split, relative to average.  Roughly speaking, the above numbers mean that on average, hitters’ OPS is 6% higher (tOPS+ = 106) with runners on base compared to OPS in all scenarios.

Some teams have been better than others when it comes to hitting with runners on base:

Team OPS (Empty) OPS (RoB) OPS Diff BAbip (Empty) BAbip (RoB) BAbip Diff tOPS+
OAK 0.672 0.789 0.117 0.264 0.306 0.042 118
SEA 0.633 0.740 0.107 0.281 0.312 0.031 118
NYM 0.622 0.713 0.091 0.284 0.288 0.004 116
COL 0.740 0.820 0.080 0.319 0.332 0.013 112
CIN 0.648 0.719 0.071 0.291 0.288 -0.003 112
CLE 0.688 0.756 0.068 0.288 0.304 0.016 111
BAL 0.705 0.771 0.066 0.288 0.310 0.022 111
ATL 0.662 0.716 0.054 0.296 0.317 0.021 109
BOS 0.664 0.713 0.049 0.294 0.297 0.003 108
MIA 0.675 0.724 0.049 0.313 0.318 0.005 108
PHI 0.650 0.694 0.044 0.294 0.290 -0.004 107
CHW 0.700 0.743 0.043 0.308 0.311 0.003 107
LAA 0.717 0.752 0.035 0.290 0.327 0.037 106
PIT 0.710 0.744 0.034 0.302 0.313 0.011 106
CHC 0.666 0.700 0.034 0.300 0.279 -0.021 106
KCR 0.681 0.715 0.034 0.306 0.297 -0.009 106
MIL 0.710 0.740 0.030 0.299 0.295 -0.004 106
ARI 0.677 0.709 0.032 0.293 0.298 0.005 105
SFG 0.670 0.698 0.028 0.283 0.310 0.027 105
WSN 0.691 0.718 0.027 0.303 0.302 -0.001 104
MIN 0.691 0.710 0.019 0.293 0.308 0.015 103
HOU 0.696 0.711 0.015 0.292 0.294 0.002 102
NYY 0.686 0.697 0.011 0.282 0.293 0.011 102
DET 0.750 0.760 0.010 0.309 0.319 0.010 102
TBR 0.698 0.707 0.009 0.298 0.287 -0.011 102
SDP 0.637 0.644 0.007 0.278 0.274 -0.004 102
TEX 0.694 0.689 -0.005 0.308 0.299 -0.009 99
TOR 0.746 0.740 -0.006 0.303 0.291 -0.012 99
LAD 0.726 0.715 -0.011 0.313 0.310 -0.003 99
STL 0.699 0.688 -0.011 0.307 0.290 -0.017 98

Here, tOPS+ is the measure of the split relative to that team’s average.  So for example, the Tigers’ OPS with Runners on Base (RoB) is 0.760, vs. 0.750 with Bases Empty for a tOPS+ of 102.  The Reds on the other hand have a split of 0.648 vs. 0.719 for a tOPS+ of 112.  The Tigers are a better offensive team overall than the Reds, but the Reds’ split with runners on base is larger.

The A’s
The A’s and Mariners top the list as having the largest split with runners on base.  Let’s take a look at the A’s individual players and how they perform with RoB:

Name PA OPS BAbip tOPS+
Josh Donaldson 242 0.953 0.318 138
Brandon Moss 239 0.933 0.348 130
Yoenis Cespedes 208 0.798 0.310 114
Jed Lowrie 202 0.563 0.250 69
Alberto Callaspo 183 0.656 0.264 116
Derek Norris 147 0.878 0.316 109
John Jaso 144 0.842 0.351 120
Coco Crisp 143 0.857 0.333 130
Josh Reddick 134 0.837 0.283 122
Eric Sogard 115 0.587 0.258 108
Stephen Vogt 96 0.887 0.338 106
Nick Punto 94 0.679 0.368 135
Craig Gentry 85 0.676 0.333 116

Again, the tOPS+ column represents how well the player performs with runners on base relative to that player’s average performance.  We can see that across the board, with the notable exception of Jed Lowrie, all the A’s have been performing better with runners on this year.

Now typically this is where you’d say the A’s are just getting lucky, and expect them to regress to the mean.  Certainly some regression is expected, but I’m not sold on the idea that this is entirely luck-driven.  We know that there are some players who routinely and consistently perform better with runners on base – sometimes dramatically so.  Let’s take a look at these players’ career numbers to see if they might be such players:

Name PA OPS BAbip tOPS+
Donaldson – Empty 861 0.701 0.259 74
Donaldson – RoB 675 0.945 0.351 134
Moss – Empty 1084 0.737 0.263 85
Moss – RoB 944 0.864 0.348 117
Cespedes – Empty 844 0.746 0.277 90
Cespedes – RoB 768 0.824 0.304 111
Lowrie – Empty 1338 0.732 0.283 98
Lowrie – RoB 1096 0.756 0.299 104
Callaspo – Empty 2045 0.678 0.281 92
Callaspo – RoB 1580 0.741 0.287 110
Norris – Empty 471 0.694 0.292 87
Norris – RoB 390 0.813 0.309 116
Jaso – Empty 940 0.702 0.275 85
Jaso – RoB 697 0.835 0.308 120
Crisp – Empty 3609 0.742 0.298 100
Crisp – RoB 2237 0.739 0.291 100
Reddick – Empty 992 0.761 0.291 109
Reddick – RoB 820 0.692 0.249 89
Sogard – Empty 488 0.591 0.253 91
Sogard – RoB 362 0.654 0.274 112
Vogt – Empty 206 0.716 0.288 93
Vogt – RoB 183 0.773 0.300 107
Punto – Empty 2087 0.633 0.298 96
Punto – RoB 1627 0.664 0.298 106
Gentry – Empty 549 0.692 0.350 98
Gentry – RoB 432 0.709 0.325 103

Almost all of them have put up large splits with runners on.  Of course, it can take upwards of 1000 PAs for something like BABIP to stabilize (and even then you still need to account for regression to the mean), and many of these players aren’t at that threshold.  Nevertheless, taking these players’ careers in aggregate gives us 27,000 plate appearances; across these, the players show in an increase of 14 points of BABIP and 53 points of OPS with runners aboard.  When compared to league average (6 points of BABIP and 38 points of OPS), it really looks like the A’s are targeting players that have some inherent, non-random ability to perform better with runners on base (to a greater extent than average).

A quick look at the Mariners
The other team leading the league in the split is the Mariners.  What’s going on there?  A look at the individual players’ splits shows:

Name PA OPS BAbip tOPS+
Robinson Cano 221 1.032 0.327 137
Kyle Seager 219 0.905 0.336 120
Dustin Ackley 177 0.702 0.310 104
Mike Zunino 167 0.640 0.247 88
Brad Miller 139 0.619 0.293 108
Justin Smoak 117 0.697 0.268 119
James Jones 115 0.634 0.366 112
Logan Morrison 106 0.671 0.244 106
Corey Hart 101 0.580 0.269 97

The two biggest contributors, by far, are Cano and Seager.  If a genie were to give you one very specific wish which was, you get to pick 2 players on your team to magically perform dramatically better with runners on base, you’d want to pick the 2 guys who a) are clearly the best hitters on your team and b) get the most plate appearances.  For the Mariners, that’s Cano and Seager.

Here, I absolutely expect regression to the mean.  I don’t think the Mariners keep this up.  In fact, looking at Cano’s career numbers (over 6000 PA’s), he’s actually been better with the bases empty: OPS of .873 vs. 0.845, and BABIP of 0.335 vs. 0.313 — but for some reason so far this year he’s been far better with runners on.

What does it all mean?
The A’s have figured it out.  The Mariners have been lucky.  The Mariners will regress heavily to the mean for the remainder of the season.  The A’s might regress somewhat, but they’re on to something.  By building a roster of players that are more productive with runners on base, they score more runs.

This explains why the A’s are outperforming their Expected Runs, or BaseRuns.  BaseRuns predicts how many runs a team scores based purely on their aggregate totals (hits, homers, total bases, etc.), removing all sequencing from the picture entirely.  Based on BaseRuns, FanGraphs says they “should have” only scored 4.54 runs per game, when they’ve actually been scoring 4.82 runs per game.  If we can do a better job quantifying how much of this sequencing is luck-based versus skill-based, we can do a better job projecting run scoring, and by extension, win percentages.

The A’s: Taking Roster Construction to the Next Level

I started writing this post prior to the trade deadline; viewed through this lens, the Lester-and-Gomes-for-Cespedes trade makes even more sense for the A’s than it already did, especially considering their parallel acquisition of Sam Fuld from the Twins.

The A’s are ahead of the curve again.  This time it’s not just about better overall player evaluation (concentrating on certain metrics that other teams undervalue), but about building a roster that maximizes each player’s skill set to get the most out of the talent on your roster.  I wrote a while back that WAR is not the be-all, end-all of player evaluation, emphasizing that there is more to Wins than WAR.

WAR is great at certain things; it’s useful to remove factors that are outside a player’s immediate control: park factors, sequencing, etc.; it’s useful for comparing players across eras by controlling for run scoring environments and translating Runs to Wins; it’s also useful because it encompasses multiple aspects of a player’s skill set (hitting, defense, baserunning), and uses the same units (Runs and Wins) to combine these into a single number.  It’s a nice package.

But if I’m a GM, I don’t evaluate each player in a vacuum.  I want to know how he fits into my system, my lineup, my park, etc.  A given player will bring different value to different teams.  Some examples:

  • Certain players might be more tailored to certain parks based on their offensive profile.  A contact hitter who hits a lot of infield singles and steals a lot of bases isn’t worth as much (compared to a team playing in league-average conditions) to a team filled with roided-up sluggers playing in pre-humidor Coors field – the value of those stolen bases and infield singles just isn’t as high.  WAR does normalize for park factors, but it assumes all players are affected by a given park equally, which on its face isn’t true.
  • A player’s contribution varies based on how his team uses him.  If a team platoons a player so that he often has the advantage, his offensive contribution (per plate appearance) will be increased, whereas if he faces a more standard distribution of pitchers, his contribution would be lower.  Likewise if he plays a position he’s not as used to for the good of the team, his own contribution (as measured by WAR) will be less than if he plays his primary position.
  • Likewise, defensive versatility has value to a team.  A player who can play multiple positions allows his team more flexibility in roster construction and in-game management; setting the daily lineup, platooning, and late-game substitutions (matchups when pinch-hitting, or defensive replacements – especially double-switches in the NL).

As a GM, you don’t just add up each player’s projected WAR (and add in the replacement-level constant) and say that’s how many you project to win that year.  There are all kinds of interrelated variables at play that will determine how your team performs.

The A’s are the epitome of this philosophy and appear to be better at this optimization of roster construction.  They’ve loaded up on defensively versatile players with outsized platoon splits and are the king of the platoon.  They’ve started doing this in the last few years, and this year even more so.  Take a look at MLB averages for platoon splits as compared to the A’s:


Matchup PA OPS
vs RHP as RHB 45802 0.686
vs RHP as LHB 44345 0.719
vs LHP as RHB 22940 0.739
vs LHP as LHB 9951 0.651
With Platoon Advantage 67285 0.726
Without Advantage 55753 0.680


Matchup PA OPS
vs RHP as RHB 998 0.714
vs RHP as LHB 2021 0.755
vs LHP as RHB 925 0.751
vs LHP as LHB 260 0.584
With Platoon Advantage 2946 0.754
Without Advantage 1258 0.687

We notice two things: first, the A’s splits are a bit wider than the league splits: their righties hit better against lefties by about the same split as righties league-wide, but their lefties really hammer righties: a .171 OPS split for A’s lefties, as compared to a 0.068 split for lefties league-wide.  They’ve made a conscious effort to go after this style of player.  Second is the distribution of plate appearances: the average team gets 55% of its plate appearances with the platoon advantage.  The A’s get 70% of their plate appearances with the platoon advantage.  They’ve constructed their roster in such a way that they can alter their day-to-day lineup as much as possible to maximize the platoon advantage.

What allows the A’s to do this?  Defensive versatility (and the DH).  They’ve got guys like Brandon Moss playing LF/RF/1B/DH; Craig Gentry playing all OF spots; Stephen Vogt playing RF/C/1B; Alberto Callaspo, John Jaso, Josh Donaldson, and Bud Norris dividing time between DH/1B/3B/C; and so on.  All this versatility allows them to mix and match their lineup to get as many plate appearances with the platoon advantage as possible.  And, by not being pulled down by having any full-time DH, they get additional flexibility.

Cespedes didn’t really fit in with this philosophy.  Nearly all his appearances came in LF – 343 PAs, compared to 17 as a CF and 69 as a DH.  With the exception of Donaldson (423 PAs as 3B), he had the highest concentration of PAs at a single position.  The next-highest was Crisp, a switch-hitter, with 306 PAs as a CF.  Everyone else is playing all over the field.

Cespedes has a bit of a platoon split (0.844 OPS vs. 0.765), but not as much as other A’s like Reddick (0.843 vs. 0.398 this year), Donaldson (1.098 vs. 0.704), or Norris (1.031 vs. 0.771).  Gomes’ platoon split: 0.875 vs. 0.722.

So maybe the A’s think there isn’t that big a difference between Gomes and Cespedes, especially considering that Cespedes’ defense can be partially replaced by Fuld’s, his performance against lefties can be replaced by Gomes, and his performance against righties can be replaced by the left-handed Vogt, who stands to get more appearances in LF now.  If they play their cards right, Fuld/Gomes/Vogt is a better player than Cespedes.

The A’s appear to have a leg up on the competition.  Rather than evaluating players in a vacuum and estimating “How many wins we will get if we add player X and remove player Y?”, they’re looking at “What does our lineup look like with player X?”  “How will his presence affect the number of plate appearances players A, B, C, and D get (with platooning taken into account)?”  “How will our various defensive alignments look?”  “How does his presence affect the availability of late-game pinch-hit and defensive replacement options?”  And for each of those questions, they boil it down to the impact on expected runs, expected runs allowed, and expected wins.  They’re all-in for this year, and they’re pulling out all the stops to optimize their lineup.

Next up, I want to look at whether there are any signs of the A’s trying to get a similar edge based on:

  • Park factors – targeting players who fit in with their park
  • Clutch hitting ability; the A’s lead the league in the split between hitting with runners on base vs. with the bases empty; why?  Is it just luck, or have they found a way to get players who are better at hitting with runners on base?

Performance With and Without Runners On, and Hitter Valuation

The increased prevalence of defensive shifts, as well as recent stories touting certain players as “shift-proof,” got me thinking: Is it a good thing to be shift-proof?  Is it inherently better to be a player against whom defensive shifting is less effective, or is there room for different players with different make-ups?  A downstream effect of defensive shifts is that, because teams shift less often (and shifts are less exaggerated) with runners on base, we start to see differences in a hitter’s performance with runners on versus with the bases empty.  We also notice other effects of players performing differently based on the number of baserunners.  In this post we’ll take a look at how we observe significant changes offensive performance (often fueled by changes in BABIP) of a few sample players when there are runners on base, versus with the bases empty.

Let’s take 3 players with very high similarity scores to each other: David Ortiz, Jason Giambi, and Carlos Delgado.  First, a look at their career stats:

Delgado 2035 8657 473 0.266 0.303 0.280 0.383 0.546 0.391 135 43.5
Ortiz 2020 8467 443 0.261 0.304 0.286 0.381 0.548 0.392 138 41.7
Giambi 2242 8864 440 0.241 0.294 0.277 0.400 0.518 0.395 140 49.3

Pretty comparable overall.  Giambi has accumulated more WAR, primarily through having a few more plate appearances, but also from having a better walk rate, which drives up his OBP, wOBA, and wRC+ significantly as well.

Now let’s look at their splits with runners on vs. bases empty:


Delgado Bases Empty 1932 4430 255 5.8% 11.7% 21.4% 0.275 0.374 0.273 0.922 0.303
Delgado Men On 1895 4227 218 5.2% 14.0% 18.9% 0.286 0.393 0.258 0.936 0.304
Ortiz Bases Empty 1862 4193 262 6.2% 11.2% 19.1% 0.271 0.356 0.282 0.908 0.281
Ortiz Men On 1851 4274 181 4.2% 15.2% 16.6% 0.302 0.406 0.240 0.948 0.327
Giambi Bases Empty 1999 4513 224 5.0% 13.0% 18.1% 0.256 0.367 0.228 0.851 0.271
Giambi Men On 2020 4351 216 5.0% 17.8% 17.1% 0.302 0.434 0.255 0.991 0.320

Here we start to see a lot of divergence.  With Ortiz and Giambi, we see a large increase in BABIP when there are runners on base (and corresponding increases to AVG and OPS).  With Delgado, there is only a trivial increase in BABIP, and a much smaller increase in OPS.

Here’s the difference in BABIP and OPS each player shows in the split between {bases empty} and {runners on}:

Player BABIP(runners on) – BABIP(empty) OPS(runners on) – OPS(empty)
Delgado 0.001 0.014
Ortiz 0.046 0.040
Giambi 0.049 0.140

Note that to some extent, all hitters tend to put up better numbers with runners on due to sampling bias – in an average “runners on” situation, a batter is more likely to be facing an inferior pitcher than in an average bases-empty situation.  Delgado’s splits are in line with the league-average splits for {bases empty} vs. {runners on}; in a given league season, the league-wide runners-on-vs.-bases-empty split in BABIP tends to range from 0.000-0.005; for OPS, the increase ranges from 0.010-0.030.  Ortiz and Giambi on the other hand show splits well outside this range that indicate there are other factors at play causing these effects.

Does this mean Ortiz and Giambi are tapping into some part of their psyche that allows them to suddenly transform into better players when runners are aboard?  Unlikely.  Ortiz and Giambi are pretty heavy pull hitters, especially looking at their ground ball spray charts, against whom defenses have often employed dramatic shifts to great effect.  However, with runners on base, these shifts tend to be less dramatic and less effective.  This is likely the primary reason for the large increases in BABIP with runners on (a 0.046 increase for Ortiz, 0.049 with Giambi).

Beyond this, although Ortiz and Giambi both show similar BABIP splits, they still differ greatly from each other in terms of their production with runners on.  Giambi’s OPS increases a whopping 140 points, while Ortiz’s only increases by 40 points.  This is largely due to Ortiz’s dramatic decrease in home run rate with runners on.  While Ortiz’s HR% drops by nearly 33%, Giambi has managed to continue hitting homers at the same rate when runners are aboard.  Do pitchers change their approach when facing Ortiz with runners on to “minimize the damage” and try to prevent him from hitting home runs?  Likewise Ortiz (based on the knowledge that pitchers will approach him differently) may change his approach at the plate as well.  The splits for other stats seem to bear this out, as Ortiz increases his walk rate and decreases his strikeout rate; this isn’t particularly revelatory, and in fact these trends are present for Giambi and even Delgado as well.

This has profound implications for player valuation.  Given 3 players who put up similar aggregate numbers over the course of the season, would you rather have the player who is going to produce at roughly the same level (similar AVG / BABIP / OPS) regardless of whether there are runners on base, or the player who is going to overproduce with runners on and underproduce with bases empty?  I’d go with the latter.  I’d prefer Ortiz to Delgado.  And then, since the decrease in Ortiz’s HR% with runners on is curious (and warrants further investigation), I’d prefer Giambi to Ortiz, Giambi being the even more extreme example of increased production with runners on.

As we start to see more and more defensive shifts (and if the assumption holds that shifts cannot be employed as effectively with runners on base), there will be more and more players who demonstrate these splits in performance.  WAR, for example, does not take this into account at all.  If a player is dramatically more productive (e.g. a 140-point increase in OPS!) with runners on, you would project his team to score more runs and win more games than if that player was replaced by a player who puts up equivalent full-season numbers (and hence, has the same WAR) but did not have the same splits.

It would be interesting to run some simulations (probably using Markov models) to more precisely quantify the impact a given player’s splits have on team run production.  Said impact would likely vary based on the team too (e.g. overall team OBP).  This could be similar to the analysis comparing how 2 players with similar wRC+ but different makeup (an OBP guy versus an ISO guy) can impact expected run totals for different teams in different ways.