Leadoff Hitters, 2014

This post kicks off a series of posts that I write every year, and therefore struggle to infuse with any sort of new perspective. However, they're a tradition on this blog and hold some general interest, so away we go.

First, the offensive performance of teams' leadoff batters. 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 "OAK (Crisp)” this does not mean that the statistic is only based solely on Crisp's performance; it is the total of all Oakland batters in the #1 spot, of which Crisp 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. MIL (Gomez/Gennett), 5.9
2. MIN (Santana/Dozier), 5.8
3. STL (Carpenter), 5.6
Leadoff average, 4.8
28. CHN (Bonifacio/Coghlan), 4.1
ML average, 4.0
29. SD (Cabrera/Solarte/Venable/Denorfia), 3.5
30. SEA (Jackson/Chavez/Jones/Almonte), 3.5

The Twins leading the AL in run scoring rate for leadoff hitters is a surprise--usually leading teams in this category are good offenses or high OBA guys, neither category describes Minnesota. They combined for a .324 OBA,
just a tick above the major league average in the other obvious measure to look at. The figures here exclude HB and SF to be directly comparable to earlier versions of this article, but those categories are available in the spreadsheet if you'd like to include them:

1. STL (Carpenter), .366
2. HOU (Altuve/Grossman/Fowler), .352
3. WAS (Span), .346
Leadoff average, .322
ML average, .310
28. CIN (Hamilton), .295
29. SD (Cabrera/Solarte/Venable/Denorfia), .293
30. SEA (Jackson/Chavez/Jones/Almonte), .287

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.

My 2009 leadoff post was linked to a Cardinals message board, and this metric was the cause of a lot of confusion (this was mostly because the poster in question was thick-headed as could be, but it's still worth addressing). 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, rather than how many it scored:

1. STL (Carpenter), .349
2. HOU (Altuve/Grossman/Fowler), .326
3. WAS (Span), .325
Leadoff average, .294
ML average, .281
27. SEA (Jackson/Chavez/Jones/Almonte), .272
28. MIL (Gomez/Gennett), .270
29. SD (Cabrera/Solarte/Venable/Denorfia), .266
30. CIN (Hamilton), .253

Milwaukee’s leadoff hitters are a good example of why ROBA is not a quality metric. Their .325 OBA was slightly above average, but they also led leadoff hitters with 26 home runs. They also were caught stealing 13 times, which tied for the fourth-most among leadoff hitters, which brought it down some more. It’s CS that really brings down the Reds, as the Hamilton-led leadoff hitters led all teams by getting caught 20 times.

I will also include what I've called Literal OBA here--this is just ROBA with HR subtracted from the denominator so that a homer does not lower LOBA, it simply has no effect. 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, so here goes. LOBA = (H + W - HR - CS)/(AB + W - HR):

1. STL (Carpenter), .353
2. HOU (Altuve/Grossman/Fowler), .331
3. WAS (Span), .328
Leadoff average, .298
ML average, .287
28. SEA (Jackson/Chavez/Jones/Almonte), .274
29. SD (Cabrera/Solarte/Venable/Denorfia), .269
30. CIN (Hamilton), .257

There is a high degree of repetition for the various OBA lists, which shouldn’t come as a surprise since they are just minor variations on each other.

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):

1. LA (Gordon), 2.5
2. PHI (Revere), 2.4
3. BOS (Holt/Pedroia/Betts), 2.3
Leadoff average, 1.7
28. NYA (Gardner/Ellsbury), 1.3
29. DET (Kinsler/Davis/Jackson), 1.3
30. COL (Blackmon), 1.3
ML average, 1.1

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. STL (Carpenter), 1.6
2. LA (Gordon), 1.5
3. PHI (Revere), 1.5
4. KC (Aoki/Cain), 1.5
Leadoff average, 1.0
ML average, .7
28. PIT (Harrison/Polanco/Marte), .6
29. LAA (Calhoun/Cowgill), .5
30. DET (Kinsler/Davis/Jackson), .5

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. PHI (Revere), 34
2. TOR (Reyes), 29
3. LA (Gordon), 23
Leadoff average, 8
ML average, 3
28. LAA (Calhoun/Cowgill), -3
29. CHA (Eaton), -4
30. SD (Cabrera/Solarte/Venable/Denorfia), -7

Last year I noted that since 2007, the percentage of major league stolen base attempts from leadoff hitters has declined. It was up to 28.8% in 2014, so the 2007-14 figures are (2007 is an arbitrary endpoint due to it being the first year I have the data at my finger tips):

30.2%, 29.6%, 27.8%, 25.9%, 27.9%, 25.1%, 25.9%, 28.8%

Shifting back to quality measures, beginning with 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. HOU (Altuve/Grossman/Fowler), 789
2. MIL (Gomez/Gennett), 781
3. WAS (Span), 776
Leadoff average, 724
ML average, 704
28. NYN (Young/Granderson/Lagares), 654
29. SD (Cabrera/Solarte/Venable/Denorfia), 637
30. SEA (Jackson/Chavez/Jones/Almonte), 625

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. HOU (Altuve/Grossman/Fowler), 5.4
2. MIL (Gomez/Gennett), 5.2
3. NYA (Gardner/Ellsbury), 5.2
Leadoff average, 4.4
ML average, 4.1
28. NYN (Young/Granderson/Lagares), 3.6
29. SEA (Jackson/Chavez/Jones/Almonte), 3.1
30. SD (Cabrera/Solarte/Venable/Denorfia), 3.1

You may note that the spread in RG between team leadoff spots is not that great, ranging from just 3.1 to 5.4. This seemed very unusual to me, so I checked the last five years and it was in fact an unusual year (chart shows standard deviation and coefficient of variation of leadoff RG by team):



Originally I just included the most recent five seasons, but I’m glad I dug up the 2009 data, because the COV was similar to that in 2014. However, the history does indicate that this is an unusually small spread in production from the leadoff spot. It seems far more likely to a blip than anything of note, though.

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 -.215, the CS coefficient was -.582, and for other details refer you to that post. I then restate it per the number of PA for an average leadoff spot (736 in 2014):

1. HOU (Altuve/Grossman/Fowler), 16
2. STL (Carpenter), 12
3. NYA (Gardner/Ellsbury), 12
Leadoff average, 0
ML average, -5
28. CHN (Bonifacio/Coghlan), -12
29. SEA (Jackson/Chavez/Jones/Almonte), -21
30. SD (Cabrera/Solarte/Venable/Denorfia), -23

I doubt I would have guessed Houston in ten guesses at the most productive leadoff spot in MLB, but Altuve and Fowler both were very productive when leading off (Robbie Grossman started 43 games as a leadoff hitter but hit .262/.340/.337, and thus was not a major contributor to the Astros’ #1 rank). Seattle managed to contend for a playoff spot despite woeful leadoff production, and attempted to address the issue (and the related center field woes) at the trade deadline by acquiring Austin Jackson. But Jackson hit just .229/.267/.260 in 236 PA in those roles after the trade. A center fielder led off for Seattle in 117 of 162 games, an outfielder in 148 of 162 games.

For the full lists and data, see the spreadsheet here.