Choose Your Own Lineup Adventure: On-Base vs. Slugging

Let’s get right down to the question that all baseball analysis is asking at its core: Which of these two players would you rather have on your team, all else being equal?

Two Mystery Players
Player A .319 .387 .469 .371
Player B .267 .328 .556 .372

It’s a close one, right? That’s largely because I decreed it to be so; these aren’t real players, just stat lines I made up that have the same wOBA. Who would you rather have? They’re extremely different, of course; one gets a ton of value from walks and singles, with some doubles sprinkled in for good measure. You can surmise that the other gets a ton of value from home runs — look at that slugging percentage — but does worse elsewhere.

Oh yeah, a few other caveats. These are underlying talent levels; you might look at Player A and say that the BABIP can’t continue, or Player B and say the HR/FB rate can’t be real, but for our purposes, these are the lines they’ll put up over 1,000 PA, or 10,000, or 1,000,000. This is their real skill level. Given that, in most cases, it doesn’t matter much which one you choose, because they’re about the same. That’s the point of wOBA, after all.

That’s not a very interesting answer, so I decided to go deeper. I constructed a generic American League lineup. I removed intentional walks so that we’re comparing apples to apples. The result looks like this:

Generic Batting Order
1 .261 .328 .423 .325
2 .256 .324 .423 .323
3 .255 .332 .458 .339
4 .255 .325 .453 .333
5 .248 .319 .431 .323
6 .240 .308 .408 .309
7 .233 .294 .399 .299
8 .227 .289 .371 .287
9 .228 .293 .360 .285

I threw that lineup into a lightly modified version of my lineup simulator, a short snippet of code that lets you put in a lineup (based on the probability of each outcome every time they bat) and get an estimate of how many runs they’d score per game. This one comes out to 4.53 runs per contest, which is close enough to the actual AL average for my purposes.

We don’t care about the average AL lineup, though; we want to know how the lineup will do with our two chosen hitters inserted. I plugged each hitter into the fourth spot in the lineup, replacing the team’s second-best hitter. Don’t think our hitters should bat fourth? Tough! That’s where I put them in the lineup, optimal construction notwithstanding (and it wouldn’t be a huge deal either way).

With Player A, the on-base champion, in there, the team scores 4.677 runs per game. Why so many places in the number? Because with Player B, captain home run, the team scores 4.672 runs per game. Those numbers are almost indistinguishable — five runs per 1,000 games, or less than a run per season. Sounds like we’re equally happy having either player, and wOBA works.

But that’s a boring answer, and I’m in search of a better one. What if we have a great offense instead of one that’s league-average? I took the average production of the top three offenses in the AL by wOBA: the Blue Jays, Astros, and Red Sox. These lineups crush. Seriously, take a look:

“Great” Batting Order
1 .259 .329 .463 .339
2 .272 .337 .438 .335
3 .296 .364 .519 .375
4 .292 .356 .513 .369
5 .281 .348 .502 .362
6 .265 .328 .447 .332
7 .249 .306 .410 .308
8 .245 .304 .384 0.298
9 .228 .302 .381 .297

Thrown into my simulation, this blended team scores 5.26 runs per game. In reality, they’ve scored 5.22 runs per game. Seems like we’re on to something here. Next, let’s put in our two prospective players. They won’t be as helpful as before, naturally, because the four hitter in these lineups is already great.

Player A upgrades the lineup slightly — to 5.28 runs per game. Player B actually downgrades the lineup — to 5.25 runs per game. We solved it! Player A is better. That difference comes out to roughly five runs per season — a win if things break well. But that’s for two players with the same wOBA, so it’s an interesting finding. It’s an obvious thing, but lineup construction can change which kind of hitter you’d prefer.

That’s also boring. Let’s really change the lineup construction and see if we can move the needle at all. I went back into that hybrid good-offense lineup and changed each of the spots other than cleanup to emphasize getting on base. The wOBAs are still the same, but now every hitter gets there with meaningfully fewer homers. Here’s our new squad:

High-OBP Batting Order
1 .264 .337 .450 .339
2 .273 .345 .424 .335
3 .299 .371 .508 .375
4 .292 .356 .513 .369
5 .291 .366 .478 .362
6 .270 .338 .429 .332
7 .254 .314 .397 .308
8 .249 .312 .371 .298
9 .235 .311 .366 .297

This lineup scores 5.3 runs per game, which should tell you that stacking OBP is at least a marginally good idea — without even adding our two hypothetical players, emphasizing on-base ability in every player adds roughly 13 runs a season to offensive production. That’s not a huge difference, of course — not enough to make you try to go trade your existing lineup for similar-wOBA, different-shape hitters — but it’s a neat effect nonetheless. It makes sense, too: stacking the bases with more runners makes singles, doubles, and walks more valuable because there’s a higher chance that there will be someone on base, as well as a higher chance that the next hitter up will advance the previous one on the bases.

Even here, the impact of our two equal-wOBA mystery hitters is small. Player A juices things slightly — to 5.32 runs per game. Player B lowers scoring — to 5.28 runs per game. There’s a slight gap between the two again, and it is again not explained by wOBA. Why? The same reason as above: stacking OBP creates synergies with itself.

Next, let’s try a powerful lineup with the same wOBAs instead. That wimpy OBP-heavy lineup we constructed above? It would have hit roughly 170 homers on the year, good for 20th in baseball despite scoring at a ludicrous clip. This one would lead the majors in homers, and trail only the Jays in slugging percentage:

High-SLG Batting Order
1 .253 .324 .472 .339
2 .262 .326 .453 .335
3 .289 .356 .530 .375
4 .292 .356 .513 .369
5 .272 .340 .515 .362
6 .259 .320 .458 .332
7 .247 .302 .417 .308
8 .243 .293 .402 .298
9 .221 .296 .390 .297

On its own, this lineup projects to score 5.23 runs per game. That’s slightly worse than the baseline we started with — and don’t worry, I’ll throw in a table at the end. But for now, let’s add in our two mystery players to see what gives. Player A leads the lineup to 5.25 runs per game. Player B doesn’t move the needle much — he checks in at 5.23 runs per game. That’s essentially no difference, even over a 162-game season.

Here it is in grid form:

Runs Scored, Various Scenarios
Team RS/G w/Player A w/Player B
Average 4.53 4.67 4.68
High Offense 5.26 5.28 5.25
High OBP 5.30 5.32 5.28
High Power 5.23 5.25 5.23

What’s the point of these endless simulations and tables? Partially, I just like asking and answering questions like these. It’s cool to me that two players who look so different — seriously, our two hypothetical guys are 60 points of OBP apart and nearly 100 points of slugging percentage — deliver roughly the same performance. It’s also cool that stacking OBP has an effect — the high-OBP lineup outperforms the high-power lineup comfortably (12 runs a season) despite identical “one-number” statistics.

Why does this effect happen? Isn’t wOBA supposed to handle that? Not so much, actually. Stats like wOBA (and wRC+, which uses the same inputs but adjusts for park) look at the average offensive context of the league. They consider the frequency of each base/out state league-wide when they assign value to each outcome. Our hypothetical offenses definitely don’t have the same frequency for each base/out state. The ones that have no one on base? Those happen a lot less often when your team is running an aggregate .340 OBP.

Who should you prefer between Player A and Player B? It doesn’t really matter. But a full team of Player A or Player B? You should lean into players who rack up hits and walks if you can, because they stack together delightfully. This is a neat effect. It’s also one that few teams can leverage, because in reality, you’re going to put the best players you can on your team, and care more about how many runs they produce on offense than how they go about doing it.

This cool synergistic effect could produce an extra win a year that won’t be obvious in the stats — but you could just find a way to upgrade one spot on your roster by one win, or make a trade that nets you two wins, or any number of more impactful improvements. In fact, even though the conclusion of this article is “the shape of your production matters,” the truth is, the shape of your production mostly doesn’t matter. In a theoretical world where you can bring in an entire team of weirdo, low-ISO BABIP gods (perhaps a cloning facility with Luis Arraez’s DNA?), sure, go pick up your free win. But in reality, wOBA and wRC+ do a pretty good job! Just go find the best players, and the rest will work itself out.

Ben is a writer at FanGraphs. He can be found on Twitter @_Ben_Clemens.

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The Stranger

What happens if you stack slugging rather than OBP? How would that compare to the baseline, and what happens if you add the two hypothetical players to that lineup?

Is there any mix of OBP and power in a lineup that outperforms pure OBP stacking? Does giving your team a good chance for three-run homers outweigh the OBP synergy at some point?


Dave Cameron, I think it was, did a similar analysis as this article here on this site a few years ago. Unsurprisingly, he came to similar conclusions. He found, essentially, that in a lineup already trending towards an extreme (high OBP/low SLG, or the reverse) that like feeds like. A high OBP team benefits more from adding more OBP than SLG, and a high SLG team benefits more from adding more SLG. Seeking balance was unhelpful.

Stoic Stathead

Yes, I thought of that article from Cameron too. Think it was about Trumbo (low OBP/high SLG player) with the Orioles (low OBP/high SLG team). So it was a good match in that regard.

Cave Dameron
Cave Dameron

Dave Cameron? Sounds like a fake name.

Jason B
Jason B

Fake name? Not cool!


Actually, I think his conclusion was slightly different. I believe his conclusion was that a ‘good’ offensive team was better served adding OBP, while a ‘poor’ offensive team preferred to add SLG. This conclusion makes sense when considered. If a team is good offensively, OBP leads to other good hitters being up more (which is more consistent), and thereby having a chance to do what they do, while a bad offensive team needs to score when it can, so having rarer outcomes more often is more valuable.


The difference between the effect on a good and bad team can be observed by looking at the limiting case ad absurdum.

Take Player A’ who literally never makes an out, but only walks. If his teammates do nothing but strike out, the team will score zero runs no matter what despite his performance. On the other hand, if his teammates also never make an out, then the team will score an infinite number of runs with him on it.

Conversely, Player B’ has the same wOBA by hitting a home run 1/3rd of the time and striking out the other two-thirds. On the lousy team, they’ll still score over a run a game (they should bat him leadoff.) But on the great team, his outs will prevent the team from batting around more than 4 or 5 times most innings.


Yeah I was a little surprised to not see the exercise repeated with a “bad” offensive team in the same way it was with the “good” amalgam.