Walk Like a Sabermetrician

I will try to make this as clear as possible: the statistics are based on the players that hit in the #1 slot in the batting order, whether they were actually leading off an inning or not. It includes the performance of all players who batted in that spot, including substitutes like pinch-hitters. 

Listed in parentheses after a team are all players that started in twenty or more games in the leadoff slot--while you may see a listing like "HOU (Springer)" this does not mean that the statistic is only based solely on Springers's performance; it is the total of all Houston batters in the #1 spot, of which Springer was the only one to start in that spot in twenty or more games. I will list the top and bottom three teams in each category (plus the top/bottom team from each league if they don't make the ML top/bottom three); complete data is available in a spreadsheet linked at the end of the article. There are also no park factors applied anywhere in this article.

That's as clear as I can make it, and I hope it will suffice. I always feel obligated to point out that as a sabermetrician, I think that the importance of the batting order is often overstated, and that the best leadoff hitters would generally be the best cleanup hitters, the best #9 hitters, etc. However, since the leadoff spot gets a lot of attention, and teams pay particular attention to the spot, it is instructive to look at how each team fared there.

The conventional wisdom is that the primary job of the leadoff hitter is to get on base, and most simply, score runs. It should go without saying on this blog that runs scored are heavily dependent on the performance of one’s teammates, but when writing on the internet it’s usually best to assume nothing. So let's start by looking at runs scored per 25.5 outs (AB - H + CS):

1. BOS (Betts/Benintendi), 9.0
2. STL (Carpenter/Pham), 7.1
3. NYA (Gardner/Hicks/McCutchen), 6.8
Leadoff average, 5.4
ML average, 4.4
28. SF (Hernandez/McCutchen/Blanco/Panik), 4.1
29. SD (Jankowski, Margot), 4.1
30. BAL (Mancini/Mullens/Beckham), 4.0

In the years I’ve been writing this post, I’m not sure I’ve since the same player show up as a member of a leading team and a atrailing team, but there is Andrew McCutchen, part-time leadoff hitter for both the group that scored runs as the third-highest clip and at the third-lowest. Leading off just 28 times for the Giants and 21 times for the Yankees, he wasn’t the driving force behind either performance.

The most basic team independent category that we could look at is OBA (figured as (H + W + HB)/(AB + W + HB)):

1. BOS (Betts/Benintendi), .421
2. CHN (Almora/Rizzo/Murphy/Zobrist), .367
3. KC (Merrifield/Jay), .365
Leadoff average, .335
ML average, .320
28. BAL (Mancini/Mullens/Beckham), .297
29. DET (Martin/Candelario), .296
30. SF (Hernandez/McCutchen/Blanco/Panik), .294

I’m still lamenting the loss of “Esky Magic” as a punchline for every leaderboard in this post, even though it’s been two years since the Royals leadoff spot was making outs in bunches thanks to their magical shortstop. Luckily Whit Merrifield gives off scrappy player vibes that media narrative makers can get behind...well, could if anyone still cared about the Royals.,

The next statistic is what I call Runners On Base Average. The genesis for ROBA is the A factor of Base Runs. It measures the number of times a batter reaches base per PA--excluding homers, since a batter that hits a home run never actually runs the bases. It also subtracts caught stealing here because the BsR version I often use does as well, but BsR versions based on initial baserunners rather than final baserunners do not. Here ROBA = (H + W + HB - HR - CS)/(AB + W + HB).

This metric has caused some confusion, so I’ll expound. ROBA, like several other methods that follow, is not really a quality metric, it is a descriptive metric. A high ROBA is a good thing, but it's not necessarily better than a slightly lower ROBA plus a higher home run rate (which would produce a higher OBA and more runs). Listing ROBA is not in any way, shape or form a statement that hitting home runs is bad for a leadoff hitter. It is simply a recognition of the fact that a batter that hits a home run is not a baserunner. Base Runs is an excellent model of offense and ROBA is one of its components, and thus it holds some interest in describing how a team scored its runs. As such it is more a measure of shape than of quality:

1. BOS (Betts/Benintendi), .360
2. KC (Merrifield/Jay), .338
3. CHN (Almora/Rizzo/Murphy/Zobrist), .334
Leadoff average, .298
ML average, .285
28. BAL (Mancini/Mullens/Beckham), .264
29. SF (Hernandez/McCutchen/Blanco/Panik), .259
30. LAA (Calhoun/Kinsler/Cozart), .257

The Angels are the only change from the top/bottom three on the OBA list; they were fourth-last at .298 but their 26 homers eighth and drove their ROBA down to the bottom.

I also include what I've called Literal OBA--this is just ROBA with HR subtracted from the denominator so that a homer does not lower LOBA, it simply has no effect. It “literally” (not really, thanks to errors, out stretching, caught stealing after subsequent plate appearances, etc.) is the proportion of plate appearances in which the batter becomes a baserunner able to be advanced by his teammates. You don't really need ROBA and LOBA (or either, for that matter), but this might save some poor message board out there twenty posts, by not implying that I think home runs are bad. LOBA = (H + W + HB - HR - CS)/(AB + W + HB - HR):

1. BOS (Betts/Benintendi), .379
2. KC (Merrifield/Jay), .343
3. CHN (Almora/Rizzo/Murphy/Zobrist), .343
Leadoff average, .306
ML average, .294
28. BAL (Mancini/Mullens/Beckham), .271
29. LAA (Calhoun/Kinsler/Cozart), .267
30. SF (Hernandez/McCutchen/Blanco/Panik), .266

The next two categories are most definitely categories of shape, not value. The first is the ratio of runs scored to RBI. Leadoff hitters as a group score many more runs than they drive in, partly due to their skills and partly due to lineup dynamics. Those with low ratios don’t fit the traditional leadoff profile as closely as those with high ratios (at least in the way their seasons played out, and of course using R and RBI incorporates the quality and style of the hitters in the adjacent lineup spots rather then attributes of the leadoff hitters’ performance in isolation):

1. SEA (Gordon/Haniger), 2.0
2. MIL (Cain/Thames), 1.9
3. MIA (Dietrich/Castro/Ortega), 1.9
Leadoff average, 1.6
28. WAS (Eaton/Turner), 1.3
29. ATL (Acuna/Inciarte/Albies), 1.3
30. TOR (Granderson/McKinney), 1.2
ML average, 1.0

I don’t know about you, but if you’d told me that leadoff spots led by Cain/Thames and Eaton/Turner would both appear as extreme on this list, I would have guessed that the former would be the one tilted to RBI.

A similar gauge, but one that doesn't rely on the teammate-dependent R and RBI totals, is Bill James' Run Element Ratio. RER was described by James as the ratio between those things that were especially helpful at the beginning of an inning (walks and stolen bases) to those that were especially helpful at the end of an inning (extra bases). It is a ratio of "setup" events to "cleanup" events. Singles aren't included because they often function in both roles. 

Of course, there are RBI walks and doubles are a great way to start an inning, but RER classifies events based on when they have the highest relative value, at least from a simple analysis:

1. PHI (Hernandez), 1.4
2. KC (Merrifield/Jay), 1.3
3. TB (Smith/Kiermaier/Span), 1.1
Leadoff average, .8
ML average, .7
28. LA (Taylor/Pederson), .6
29. PIT (Frazier/Harrison/Dickerson), .5
30. TOR (Granderson/McKinney), .5

I should note that in the context-neutral RER, the two teams with seemingly backwards placement on the R/RBI list are closer to where you’d expect--the Nats were sixth at 1.0 while the Brewers were still forwardly placed but much closer to average (ranking tenth with .9).

Since stealing bases is part of the traditional skill set for a leadoff hitter, I've included the ranking for what some analysts call net steals, SB - 2*CS. I'm not going to worry about the precise breakeven rate, which is probably closer to 75% than 67%, but is also variable based on situation. The ML and leadoff averages in this case are per team lineup slot:

1. WAS (Eaton/Turner), 26
2. KC (Merrifield/Jay), 25
3. TB (Smith/Kiermaier/Span), 17
Leadoff average, 5
ML average, 2
27. BAL (Mancini/Mullens/Beckham), -6
27. CHN (Almora/Rizzo/Murphy/Zobrist), -6
27. LA (Taylor/Pederson), -6
30. CIN (Peraza/Schebler/Winker/Hamilton), -9

Shifting back to quality measures, first up is one that David Smyth proposed when I first wrote this annual leadoff review. Since the optimal weight for OBA in a x*OBA + SLG metric is generally something like 1.7, David suggested figuring 2*OBA + SLG for leadoff hitters, as a way to give a little extra boost to OBA while not distorting things too much, or even suffering an accuracy decline from standard OPS. Since this is a unitless measure anyway, I multiply it by .7 to approximate the standard OPS scale and call it 2OPS:

1. BOS (Betts/Benintendi), 1017
2. CLE (Lindor), 847
3. STL (Carpenter/Pham), 842
Leadoff average, 762
ML average, 735
28. SF (Hernandez/McCutchen/Blanco/Panik), 669
29. DET (Martin/Candelario), 652
30. BAL (Mancini/Mullens/Beckham), 649

Along the same lines, one can also evaluate leadoff hitters in the same way I'd go about evaluating any hitter, and just use Runs Created per Game with standard weights (this will include SB and CS, which are ignored by 2OPS):

1. BOS (Betts/Benintendi), 8.7
2. CLE (Lindor), 5.9
3. STL (Carpenter/Pham), 5.7
Leadoff average, 4.7
ML average, 4.3
28. SF (Hernandez/McCutchen/Blanco/Panik), 3.5
29. DET (Martin/Candelario), 3.3
30. BAL (Mancini/Mullens/Beckham), 3.1

Seeing the same six extreme teams in perfect order overstates the correlation, but naturally there is a very strong relationship between the last two metrics. The biggest difference in any team’s ranks in the two was four spots.

Allow me to close with a crude theoretical measure of linear weights supposing that the player always led off an inning (that is, batted in the bases empty, no outs state). There are weights out there (see The Book) for the leadoff slot in its average situation, but this variation is much easier to calculate (although also based on a silly and impossible premise). 

The weights I used were based on the 2010 run expectancy table from Baseball Prospectus. Ideally I would have used multiple seasons but this is a seat-of-the-pants metric. The 2010 post goes into the detail of how this measure is figured; this year, I’ll just tell you that the out coefficient was -.234, the CS coefficient was -.601, and for other details refer you to that post. I then restate it per the number of PA for an average leadoff spot (748 in 2017):

1. BOS (Betts/Benintendi), 57
2. STL (Carpenter/Pham), 20
3. CLE (Lindor), 17
Leadoff average, 0
ML average, -7
28. SF (Hernandez/McCutchen/Blanco/Panik), -22
29. DET (Martin/Candelario), -24
30. BAL (Mancini/Mullens/Beckham), -27

Boston completely dominated the quality metrics for leadoff hitters in 2018, due mostly of course to the superlative season of Mookie Betts, who was the second-best offensive player in the game in 2018. Put one of the top hitters in the leadoff spot and you can expect to lead in a lot of categories - BOS led easily not just in categories that reflect quality without any shape distortion (not necessarily context-free) like R/G, OBA, 2OPS, RG, and LE, but also in ROBA and LOBA, which are designed to not measure value but rather the rate of leadoff hitters reaching base for their teammates to drive in. What’s more, it’s not as if Boston achieved this by having a really good OBA out of the leadoff spot without a lot of power - the Red Sox and Cardinals tied for the ML league with 38 homers out of the leadoff spot. The Indians ranked third with 37, and those teams ranked 1-2-3 in all of the overall quality measures. One can argue about optimal lineup construction, but in 2018, leadoff hitters hit dingers like everyone else. Every team was in double digits in homers out of the leadoff spot; in 2017 there were only two, but one of those teams hit just three homers.

The spreadsheet with full data is available here.

I tend to think I’m pretty objective when it comes to baseball analysis. Someone reading my blog or Twitter feed (RIP, mostly) with a critical eye might beg to differ: I like the Indians, players accused of using steroids, hate the Royals, and oh yeah I really love Mike Trout. The latter is certainly not unique to me -- how could you not like Mike Trout? -- but it is pronounced enough that my objectivity could be called into question when (for once) Mike Trout is engaged in a close race for AL MVP.

I think Mike Trout was most likely the most valuable player in baseball in 2018, and I firmly believe I would say that even if I was not a huge fan. While Baseball-Reference and Fangraphs’ WAR would disagree, Baseball Prospectus’ WARP agrees, so I’m not completely on an island.

The key consideration for me is that Trout was markedly superior offensively to Mookie Betts once you properly weight offensive events (read: more credit to Trout for his walks than metrics of the OPS family would allow) and adjust for the big difference in park factors between Angels Stadium and Fenway Park (97 and 105 PF respectively). I estimate that, adjusting for park, Trout created six more runs than Betts while making twenty fewer outs. That’s about a nine run difference. Then there is the position adjustment, which is worth another four.

Betts does cut into this lead with his defensive value: going in the order FRAA/UZR/DRS, Betts (11/15/20) has an average twelve runs higher than Trout (-2/4/8). I don’t credit the full difference, but even if I did, Trout would still have a one run edge. Give Betts a couple extra runs for baserunning (a debatable point)? I’m still going with the player with a clear advantage in offensive value. Regress the defense 50%? It’s close but the choice is much clearer.

The rest of my ballot is pretty self-explanatory if you look at my RAR estimates. I could justify just about any order of 6-9; I’m not at all convinced that JD Martinez was more valuable than Jose Ramirez, but chalk that one up to avoiding the indication of bias. Francisco Lindor rises based on excellent fielding metrics (6/14/14):

1. CF Mike Trout, LAA
2. RF Mookie Betts, BOS
3. SP Justin Verlander, HOU
4. 3B Alex Bregman, HOU
5. SP Chris Sale, BOS
6. DH JD Martinez, BOS
7. SP Blake Snell, TB
8. SS Francisco Lindor, CLE
9. 3B Jose Ramirez, CLE
10. SP Corey Kluber, CLE

The NL MVP race is weird. Christian Yelich had an eighteen RAR lead over the next closest position player (Javier Baez), which is typically an indication of a historically great season. Triple crown bid aside, Yelich did not have a historically great season, “merely” a typical MVP-type season. In the AL, he would have been well behind Trout and Betts with Bregman and Martinez right on his heels.

Thus the only meaningful comparison for the top of the ballot is the top hitter (Yelich) against the top pitcher (Jacob deGrom). When it comes to an MVP race between a hitter and a pitcher, I usually try to give the former the benefit of the doubt. Specifically, while there is one primary way in which I evaluate the offensive contribution of a hitter (runs created based on their statistics, converted to RAR), there are three obvious ways using the traditional stat line to calculate RAR for a pitcher. The first is based on actual runs allowed; the second on peripheral statistics (this one is most similar to the comparable calculation for batters); the third based on DIPS principle. In order for me to support a pitcher for MVP, ideally he would be more valuable using each of these perspectives on evaluating performance. deGrom achieved this, with his lowest RAR total (72 based on DIPS principles) exceeding Yelich’s 69 RAR (and with Yelich’s -5/-2/4 fielding metrics, 69 is as good as it gets).

Given the huge gap between Yelich and Baez, starting pitchers dominate the top of my ballot. The movers upward when considering fielding are a pair of first basemen (Freddie Freeman and Paul Goldschmidt) and Nolan Arenado, while Bryce Harper’s fielding metrics were dreadful (-12/-14/-26) and drop him all the way off the ballot:

1. SP Jacob deGrom, NYN
2. LF Christian Yelich, MIL
3. SP Max Scherzer, WAS
4. SP Aaron Nola, PHI
5. SP Kyle Freeland, COL
6. SP Patrick Corbin, ARI
7. SS Javier Baez, CHN
8. 1B Freddie Freeman, ATL
9. 1B Paul Goldschmidt, ARI
10. 3B Nolan Arenado, COL

The AL Cy Young race is extremely close due to the two candidates who appeared to be battling it out for the award much of the season missing significant time in the second half. Despite their injuries, Chris Sale and Trevor Bauer had logged enough innings preventing enough runs on a rate basis to still be legitimate contenders in the end. Justin Verlander and Blake Snell each tied with 74 RAR based on actual runs allowed adjusted for bullpen support, an eight run lead over Sale in third. But when you look at metrics based on eRA (based on “components”) and dRA (based on DIPS concepts), Sale, Bauer, Corey Kluber, and Gerrit Cole all cut into that gap.

In fact, using a crude weighting of 50% RA-based, 25% eRA-based, and 25% dRA-based RAR, there are six pitchers separated by seven RAR. A seventh, Mike Clevinger, had 65 standard RAR but worse peripherals to drop four runs behind the bottom of that pack.

There are any number of reasonable ways to fill out one’s ballot, but I think the best choice for across-the-board excellence is Verlander. He pitched just one fewer inning than league leader Kluber, tied for the league lead in standard RAR, was second one run behind Kluber in eRA-based RAR, and was third by five runs to Sale in dRA-based RAR. Chris Sale sneaks into second for me as he led across the board in RA; even pitching just 158 innings, seventeen fewer than even Bauer, his excellence allowed him to accrue a great deal of value. Snell and the Indians round out my ballot; I’ve provided the statistics I considered below as evidence of how close this is:

1. Justin Verlander, HOU
2. Chris Sale, BOS
3. Blake Snell, TB
4. Corey Kluber, CLE
5. Trevor Bauer, CLE



The NL race is not nearly as close, as Jacob deGrom was second in innings (by just three to Max Scherzer) and led in all of the RA categories, plus Quality Start % and probably a whole bunch of equally suspect measures of performance.

Behind him I see no particular reason to deviate from the order suggested by RAR; Scherzer over Aaron Nola is an easy choice due to the former’s superior peripherals, and while Patrick Corbin had superior peripherals to Kyle Freeland, the latter’s 13 RAR lead is a lot to ignore, although Corbin should be recognized for having an eRA and dRA quite similar to Max Scherzer and otherwise lapping the rest of the field. With the exception of course of Jacob deGrom, the author a season that is worthy of considerable discussion in the next installment of “meaningless hypothetical award ballots”:

1. Jacob deGrom, NYN
2. Max Scherzer, WAS
3. Aaron Nola, PHI
4. Kyle Freeland, COL
5. Patrick Corbin, ARI