Leadoff Hitters, 2017

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 "COL (Blackmon)" this does not mean that the statistic is only based solely on Blackmon's performance; it is the total of all Colorado batters in the #1 spot, of which Blackmon 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. COL (Blackmon), 7.9
2. HOU (Springer), 7.0
3. STL (Carpenter/Fowler), 6.7
Leadoff average, 5.5
ML average, 4.6
28. CHA (Garcia/Anderson/Sanchez), 4.4
29. KC (Merrifield/Escobar), 4.3
30. SD (Margot/Pirela), 3.9

That’s Leury Garcia for the White Sox, in case you were wondering. One of my favorite little tidbits from the 2017 season was their all-Garcia outfield: Willy, Leury, and Avasail. Sadly, one of my favorite tidbits from the last few season was no more as Kansas City finally decided that Esky Magic had run its course. Alcides Escobar only got 25 starts leading off, with Whit Merrifield leading the way with 115. The Royals still made plenty of appearances in the trailer portions of these lists.

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

1. COL (Blackmon), .399
2. STL (Carpenter/Fowler), .374
3. HOU (Springer), .374
Leadoff average, .333
ML average, .327
28. CIN (Hamilton), .295
29. TOR (Pillar/Bautista), .287
30. KC (Merrifield/Escobar), .282

Even if we were to park-adjust Colorado’s .399, they’d be at .370, so it was a fine performance by Blackmon and company (mostly Blackmon, with 156 starts), but not the best in the league. There’s no good reason I don’t park-adjust, although my excuse is that park adjustments don’t apply (or at least can’t be based off the runs park factor) for some of the metrics presented here. Of the categories mentioned in this post, R/G, OBA, 2OPS, RG, and LE could be if one was so inclined.

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. STL (Carpenter/Fowler), .341
2. COL (Blackmon), .336
3. PHI (Hernandez), .327
5. SEA (Segura/Gamel), .319
Leadoff average, .295
ML average, .288
28. CIN (Hamilton), .265
29. KC (Merrifield/Escobar), .248
30. TOR (Pillar/Bautista), .241

With the exception of Houston, the top and bottom three are the same as the OBA list, just in different order (HOU was eighth at .313, their 38 homers out of the leadoff spot tied with Colorado; Minnesota, the Mets, Cleveland, and Tampa Bay also got 30 homers out of the top spot).

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. COL (Blackmon), .354
2. STL (Carpenter/Fowler), .352
3. PHI (Hernandez), .332
4. HOU (Springer), .330
Leadoff average, .303
ML average, .298
28. CIN (Hamilton), .268
29. KC (Merrifield/Escobar), .253
30. TOR (Pillar/Bautista), .251

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. MIA (Gordon), 2.5
2. TEX (DeShields/Choo/Gomez), 2.2
3. CIN (Hamilton), 2.2
Leadoff average, 1.5
ML average, 1.0
27. SD (Margot/Pirela), 1.3
28. KC (Merrifield/Escobar), 1.2
29. MIN (Dozier), 1.1
30. CLE (Kipnis/Lindor/Santana), 1.1

Cleveland only settled on a permanent leadoff fixture (Lindor) late in the season, but all three of their 20+ game leadoff men were of the same general type. They didn’t really have a player who saw regular time who fit anything like the leadoff profile. It worked OK; their .328 OBA was lower than the leadoff average, but as we’ll see later their 5.2 RG was above average.

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. CIN (Hamilton), 1.7
2. MIA (Gordon), 1.6
3. TEX (DeShields/Choo/Gomez), 1.3
Leadoff average, .8
ML average, .6
28. TB (Dickerson/Kiermaier/Smith/Souza), .5
29. BAL (Smith/Beckham/Jones/Rickard), .5
30. COL (Blackmon), .5

Both Tampa Bay and Baltimore followed the Cleveland pattern of using multiple leadoff hitters, although one of the four for each (Mallex Smith and Joey Rickard) fit more of a traditional profile. The Rays got 5.1 RG out of this hodgepodge, which is above-average; the Orioles’ 4.3 was not. For the record, I’m basing my assessment of Joey Rickard’s traditional leadoff style bona fides on his career minor league line (.280/.388/.392), and not his major league line (.255/.298/.361) for which the only stylistic interpretation is “bad”.

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 (Turner/Goodwin), 37
2. CIN (Hamilton), 36
3. MIA (Gordon), 25
4. NYA (Gardner), 21
Leadoff average, 8
ML average, 2
28. CHN (Jay/Zobrist/Schwarber), -2
29. COL (Blackmon), -5
30. HOU (Springer), -7

A lot of the leaders and trailers are flipped on this list from the overall quality measures.

Shifting back to said 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. COL (Blackmon), 979
2. HOU (Springer), 887
3. MIN (Dozier), 865
Leadoff average, 763
ML average, 755
28. TOR (Pillar/Bautista), 678
29. KC (Merrifield/Escobar), 658
30. CIN (Hamilton), 651

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. COL (Blackmon), 7.8
2. HOU (Springer), 6.4
3. MIN (Dozier), 6.1
Leadoff average, 4.8
ML average, 4.6
28. TOR (Pillar/Bautista), 3.7
29. CIN (Hamilton), 3.6
30. KC (Merrifield/Escobar), 3.5

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 -.230, the CS coefficient was -.597, and for other details refer you to that post. I then restate it per the number of PA for an average leadoff spot (750 in 2017):


1. COL (Blackmon), 45
2. HOU (Springer), 26
3. MIN (Dozier), 25
Leadoff average, 2
ML average, 0
28. CIN (Hamilton), -17
29. TOR (Pillar/Bautista), -18
30. KC (Merrifield/Escobar), -24

Esky Magic has residual effects, apparently. I don’t recall seeing the same teams in the leaders and trailers list for 2OPS, RG, and LE before, but they are all very similar in terms of their construction, with 2OPS arbitrarily but logically tilted towards OBA and LE attempting to isolate run value that would be contributed if all plate appearances came in a leadoff situation. RG represents the approximate run value of a player’s performance in an “average” situation on an average team.

The spreadsheet with full data is available here.