There’s an awful lot of stuff in baseball analysis that’s just a complete waste of time. Some people love doing studies that take a look at something either esoteric, rare, or with no potential practical application when it comes to the actual game of baseball. That’s great; there’s nothing wrong with those kinds of diversions. We’ve all got those kinds of activities in our lives. But in terms of practical application on a real life baseball team, a “sabermetric” biography of the 1952 Yankees isn’t particularly useful. That sort of stuff has never spun my wheels, and it’s one reason I tend to yell and scream at BP writers who mention ballplayers from before Kristy Swanson was born.

Historians and fans of sepia tones will undoubtedly pipe in with: “Of course you can learn something from history!” (Derisively insert sound of adults in Charlie Brown cartoons here.) No one’s saying that’s not the case. But we prefer to focus on ideas that actually have practical applications on the field, and can directly and visibly translate into more wins, which means more championships, more money, etc. We’ve taken a fair amount of flak over the years for not making more things public, and not fully embracing an academic model for the serious study of baseball. Some of the criticism is well-deserved, some of it’s simply a disagreement over what people in the field are really doing. We like the idea of innovating to gain a competitive advantage and beat the snot out of opponents, rather than having the material published in some peer-reviewed journal.

When Rany Jazayerli came back from a Pizza Feed a few weeks back and mentioned that he had talked to a couple of front office guys about a different kind of platoon, my chin hit the virtual floor. The idea he had mentioned, and which was apparently perceived as novel, was at least 20 years old, and Gary Huckabay had been approached about studying the idea by a major league club back in 1998. (Even more surprising is that the club that wanted this issue studied is not largely perceived as a progressive organization.) This supposedly novel idea had also been mentioned in one of the old Elias Analysts, but was never really fleshed out in those pages.

What kind of platoon are we talking about? Using the groundball/flyball tendencies of pitchers and hitters to determine and acquire the most favorable possible matchups. Lefty/Righty platooning is as firmly entrenched in “the book” as any tactic, on both sides of the ball. Why? Because it works, albeit probably not as well as people think. Hitters hit better against pitchers who throw from their opposite side. Here’s the Left/Right/Switch outcomes from the 2000 season. This dataset consists only of hitters with 300 PA or more, and pitchers who faced 300 batters or more.

Throws  Bats    BA/OBP/SLG
L         B       .285

L         L       .281

L         R       .290

R         B       .278

R         L       .285

R         R       .277

Aggregate         .280

Clearly, there are some distortions to consider here. This dataset contains primarily starting offensive players, and omits a large number of relievers, so it’s not perfect by any means. But there’s clearly an advantage in using a lefty pitcher against a lefty batter. (Probably not enough to justify the largely reflexive and bad tactics of modern bullpen management, but that’s another column.)

Optimizing lefty/righty matchups is a major goal of in-game tactics in baseball. Lineups are set to minimize or eliminate long strings of batters who bat from the same side, in order to avoid vulnerability to a particular reliever. Pinch-hitters with no prayer of getting into the game are run out to the on-deck circle in order to force the other manager into a reflexive tactical move. At the end of the day, it’s not clear that the tactical advantage gained from all these moves outweighs the strategic price often paid to acquire the tactical options. How many really good righties are in the minors so a team can have a second or third lefty in the pen? How many valuable hitters are at Triple-A so that some indifferent lefty can have a roster spot?

Is there a comparable tactical advantage to be gained by optimizing the groundball/flyball matchups between pitchers and hitters? Let’s look at the same dataset as above. Instead of trying to create a continuum, let’s keep it simple, and break the players into quartiles, based on their GB/FB ratios. Each cell contains BA/OBP/SLG, and the quartiles go from Qrt 1 (Most Fly Balls) to Qtr 4 (Fewest Fly Balls) for pitchers and hitters. Again, the aggregate (baseline) for these matchups is .280/.354/.460.

2000 Season  Hitter      Hitter      Hitter      Hitter       Aggregate:
             Quartile 1  Quartile 2  Quartile 3         Quartile 4    Pitchers

Quartile 1   0.263         0.285              0.265          0.276               0.273
             0.348         0.362              0.337          0.347               0.349
             0.504         0.501              0.452          0.438               0.474
Quartile 2   0.282         0.285              0.288          0.286               0.285
             0.363         0.362              0.355          0.354               0.359
             0.496         0.487              0.466          0.448               0.475
Quartile 3   0.288         0.275              0.278          0.289               0.282
             0.364         0.353              0.346          0.356               0.355
             0.495         0.456              0.439          0.437               0.457
Quartile 4   0.291         0.275              0.280          0.278               0.281
             0.368         0.351              0.346          0.341               0.352
             0.487         0.434              0.417          0.403               0.436
Hitters             0.282         0.280              0.276          0.282        
             0.361         0.357              0.346          0.350        
             0.495       0.469        0.443       0.431

First off, the painfully obvious–groundballs don’t go over the fence, which is more instructive on the offensive side of the ball than on the mound. But let’s take a look at the matchups more carefully:

For extreme flyball hitters, it doesn’t appear to make a heck of a lot of difference what type of GB/FB pitcher you throw up there; there’s a very small variance in performance based on GB/FB matchups. For every other type of hitter, there is a significant dropoff in performance when facing extreme GB pitchers, significantly reducing the frequency of extra-base hits while not increasing on-base percentage.

Let’s look at the data from 1978-2000, with the same PA cutoff:

Actual,            Hitter       Hitter       Hitter      Hitter       Aggregate:
1978-2000   Quartile 1   Quartile 2   Quartile 3  Quartile 4   Pitchers
Quartile 1  0.259         0.267              0.271          0.269               0.266
            0.338         0.335              0.335          0.331               0.335
            0.462         0.442              0.426          0.384               0.430
Quartile 2  0.267         0.270              0.272          0.271               0.270
            0.343         0.337              0.335          0.330               0.336
            0.459         0.429              0.415          0.382               0.426
Quartile 3  0.272         0.274              0.271          0.275               0.273
            0.346         0.340              0.335          0.333               0.339
            0.454         0.427              0.401          0.378               0.415
Quartile 4  0.279         0.276              0.273          0.268               0.274
            0.351         0.340              0.336          0.326               0.338
            0.447         0.416              0.391          0.358               0.403
Hitters            0.269         0.272              0.272          0.271
            0.344         0.338        0.335       0.330
            0.456        0.429        0.408       0.376

The effects are muted a bit, but the basics are still there–significance is considerably easier with a mammoth sample size. (And thanks to Keith Woolner for sacrificing his CPUs and hard drive time when I was unable to. Keith’s the guy we put extra guards on when the Terror-O-Meter hits orange.)

There are a number of biases inherent in this kind of analysis and presentation. I haven’t checked, but I expect that if you further break down the data to include K rate, flyball pitchers would bifurcate quite nicely into Jim Acker impersonators and Roger-Clemens-In-His-Prime-Death-Machines. (Perhaps not quite that starkly.) But even with all the noise, there appear to be tactical advantages to be gained here. How much of that effect is due to the GB/FB ratios of the pitchers and hitters, and how much of it is due to simply matching up pitchers and hitters of disparate expected performance levels, regardless of proclivity for the groundball of flyball?

Bill James developed a system he called log5 to determine what you should expect when a hitter of a particular skill level faces a pitcher of a particular skill level. It’s basic linear algebra–nothing too involved. Let’s take a look at the data from 1978-2000 through that lens. Based on the aggregate performances for the requisite quartiles, here’s the expected outcomes, using log5:

Predicted,   Hitter     Hitter     Hitter     Hitter      Aggregate:
1978-2000    Quartile 1        Quartile 2 Quartile 3 Quartile 4  Pitchers
Quartile 1   0.264        0.267           0.267      0.266          0.266
             0.342        0.336           0.333      0.328          0.335
             0.468        0.441           0.419      0.387          0.430
Quartile 2   0.268        0.271           0.271      0.270          0.270
             0.343        0.337           0.334      0.329          0.336
             0.464        0.437           0.415      0.383          0.426
Quartile 3   0.271        0.274           0.274      0.273          0.273
             0.346        0.340           0.337      0.332          0.339
             0.452        0.425           0.405      0.373          0.415
Quartile 4   0.272        0.275           0.275      0.274          0.274
             0.345        0.339           0.336      0.331          0.338
             0.440        0.413           0.393      0.361          0.403
Hitters             0.269        0.272           0.272      0.271
             0.344      0.338      0.335      0.330
             0.456      0.429      0.408      0.376

And, the difference between predicted and actual?

Difference, Hitter      Hitter      Hitter      Hitter      Aggregate:
1978-2000   Quartile 1        Quartile 2  Quartile 3        Quartile 4  Pitchers
Quartile 1  0.005        0            -0.004        -0.003            0.266
            0.004        0.001            -0.002        -0.003            0.335
            0.006        -0.001            -0.007         0.003            0.430
Quartile 2  0.001        0.001            -0.001        -0.001            0.270
            0                0            -0.001        -0.001            0.336
            0.005        0.008             0                 0.001            0.426
Quartile 3 -0.001        0             0.003        -0.002            0.273
            0                0             0.002        -0.001            0.339
           -0.002        -0.002       0.004        -0.005            0.415
Quartile 4 -0.007        -0.001             0.002        0.006            0.274
           -0.006        -0.001             0                0.005            0.338
           -0.007        -0.003             0.002        0.003            0.403
Hitters            0.269        0.272             0.272        0.271
            0.344       0.338        0.335      0.330
            0.456        0.429        0.408      0.376

Intuitively, managers have been using these matchups for a long time, bringing in the groundballer to get a double play, etc., but to a far more limited extent than lefty/righty matchups. An innovative team that’s willing to take some risks may do well to further explore the possibilities of structuring their roster taking GB/FB tendencies into account the same way (but not to the same extent) they consider lefty/righty matchups. They may find themselves with more tactical options during games, as well as more talent and less risk overall on the roster. Teams have had a tendency in the past to look at specific skills when filling out the back of the roster–a groundball pitcher to try to get the double play, a contact hitter to avoid the strikeout in a key pinch-hitting situation. A careful review of the GB/FB specifics of the candidates for the 24th and 25th spots on the roster could well result in a better bench or bullpen, and more favorable tactical options available to managers in key situations.

Of course, the very basic lesson here is that groundball hitters are a bad thing, and that may seem obvious, but it’s actionable information that hasn’t yet made its way into front offices. Specific individuals may well have GB/FB breakdowns that are dramatically different than the average, and this area is one more place where progressive, diligent organizations will eventually be looking for, and probably finding, significant advantages.