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Thursday, June 07, 2007

Quantifying Plate Discipline

In my column this morning on Baseball Prospectus (subscription required but well worth it) among other things I take another crack at the PITCHf/x Gameday data. In part, inspired by this fascinating article, I created a couple of metrics to quantify plate discipline. They are:

  • Swing (S) defined as the percentage of pitches the batter swung at and also available in many other places. Obviously high values here are indicative of aggressive hitters or hitters who see a greater percentage of pitches out of the strike zone.


  • Fish (F) defined as the percentage of pitches out of the strike zone that the hitter swung at. A higher percentage here indicates that the hitter may have trouble recognizing pitches since he is offering at pitches that would likely otherwise be called balls. It should be noted that the strike zone as defined for this analysis is 17 inches wide (the standard) and uses the actual height customized for the player. No buffer room was added as was done in the previous articles since here we're not concerned with giving the umpire the benefit of the doubt.


  • Bad Ball (BB) defined as the percentage of pitches out of the strike zone that were swung at and made contact with (including foul balls although there is an argument to be made that a foul ball is not the intended outcome and so should be discounted in some way). A higher value in this category indicates that the hitter, when swinging at bad pitches, is at least able to get the bat on the ball.


  • Eye (E) defined as the percentage of pitches in the strike zone on non-three and zero counts that were taken for strikes. A smaller value in this metric indicates a player who recognizes strikes and aggressively offers at them. Non three and zero counts were excluded since obviously a hitter is much more likely to let a strike go by in this situation and so we don’t want to penalize them for that behavior. Some readers will see, however, where this idea could be extended to each of the eight possible counts and a system devised where less penalty is credited to hitters who take at 3-1 than those that do so at 0-2.


  • Without further ado here's the list (with the big caveat that only 24% of all pitches in 2007 have been tracked in the system and there is a heavy bias to the AL West because of the parks that the system is installed in) of all players who have 200 or more pitches tracked this season sorted by "Fish".


    Name Stand Pitches Swing Fish BadBall Eye
    A.J. Pierzynski L 357 0.602 0.469 0.813 0.117
    Garret Anderson L 260 0.515 0.467 0.750 0.350
    Delmon Young R 200 0.550 0.454 0.630 0.228
    Rob Mackowiak L 297 0.525 0.438 0.738 0.240
    Hank Blalock L 332 0.521 0.430 0.632 0.183
    Erick Aybar L 235 0.532 0.430 0.869 0.217
    Carl Crawford L 216 0.519 0.427 0.738 0.171
    Jeffrey Francoeur R 337 0.546 0.425 0.775 0.094
    Vladimir Guerrero R 403 0.524 0.421 0.771 0.142
    Adrian Beltre R 430 0.512 0.403 0.694 0.234
    Michael Young R 585 0.523 0.403 0.766 0.284
    Eric Chavez L 519 0.503 0.394 0.736 0.225
    Kenji Jojima R 270 0.522 0.389 0.878 0.217
    Nomar Garciaparra R 436 0.557 0.388 0.800 0.132
    Juan Pierre L 431 0.480 0.385 0.916 0.302
    Yuniesky BetancourR 345 0.496 0.383 0.806 0.279
    Joe Crede R 376 0.503 0.381 0.729 0.238
    Jason Phillips R 203 0.493 0.381 0.721 0.303
    Vernon Wells R 417 0.501 0.380 0.713 0.216
    Jose Lopez R 347 0.478 0.375 0.853 0.288
    Ichiro Suzuki L 515 0.435 0.369 0.862 0.326
    Khalil Greene R 407 0.514 0.367 0.595 0.247
    Orlando Cabrera R 517 0.468 0.362 0.838 0.279
    Brian Mccann L 267 0.434 0.358 0.783 0.282
    Kevin Kouzmanoff R 363 0.499 0.350 0.724 0.167
    Lyle Overbay L 381 0.465 0.349 0.704 0.230
    Sammy Sosa R 532 0.492 0.349 0.625 0.205
    Royce Clayton R 227 0.515 0.348 0.457 0.194
    Adam Lind L 336 0.482 0.348 0.719 0.253
    Andruw Jones R 389 0.478 0.344 0.686 0.232
    Jose Guillen R 394 0.475 0.344 0.636 0.232
    Alexis Rios R 413 0.426 0.343 0.735 0.351
    Marcus Giles R 452 0.511 0.341 0.674 0.211
    Bobby Crosby R 474 0.451 0.338 0.652 0.327
    Casey Kotchman L 463 0.434 0.338 0.814 0.285
    Gary Matthews Jr. L 449 0.441 0.334 0.781 0.223
    David Dejesus L 209 0.445 0.333 0.816 0.340
    Nelson Cruz R 374 0.457 0.330 0.690 0.268
    Andre Ethier L 376 0.500 0.329 0.803 0.200
    Jose Vidro L 336 0.461 0.329 0.870 0.200
    Juan Uribe R 334 0.476 0.328 0.651 0.232
    Raul Ibanez L 474 0.456 0.328 0.760 0.229
    Gerald Laird R 484 0.459 0.326 0.698 0.290
    Jason Kendall R 405 0.432 0.325 0.870 0.373
    Jermaine Dye R 452 0.442 0.324 0.753 0.267
    Mark Teahen L 213 0.437 0.324 0.636 0.307
    Adrian Gonzalez L 590 0.453 0.322 0.767 0.225
    Darin Erstad L 372 0.398 0.318 0.797 0.413
    Shea Hillenbrand R 330 0.445 0.308 0.821 0.297
    Brad Wilkerson L 263 0.414 0.306 0.755 0.303
    Paul Konerko R 457 0.420 0.303 0.765 0.290
    Richie Sexson R 448 0.446 0.303 0.732 0.211
    Mark Ellis R 440 0.409 0.302 0.851 0.380
    Mark Teixeira L 501 0.399 0.302 0.657 0.276
    Kenny Lofton L 514 0.434 0.300 0.818 0.239
    Travis Buck L 377 0.416 0.300 0.662 0.255
    Michael Napoli R 346 0.419 0.299 0.683 0.322
    Kelly Johnson L 384 0.393 0.292 0.787 0.315
    Josh Bard L 283 0.470 0.292 0.878 0.177
    Edgar Renteria R 346 0.405 0.290 0.688 0.275
    Jeff Kent R 420 0.476 0.285 0.611 0.176
    Ian Kinsler R 591 0.423 0.280 0.784 0.268
    Terrmel Sledge L 285 0.446 0.272 0.609 0.217
    Rafael Furcal L 371 0.391 0.270 0.857 0.353
    Shannon Stewart R 437 0.410 0.269 0.810 0.327
    B.J. Upton R 225 0.431 0.269 0.528 0.227
    Frank Thomas R 478 0.377 0.267 0.838 0.335
    Troy Glaus R 306 0.376 0.264 0.706 0.330
    Jose Cruz Jr. L 285 0.428 0.262 0.591 0.216
    Aaron Hill R 417 0.415 0.253 0.729 0.302
    Willie Harris L 205 0.400 0.248 0.765 0.239
    Russell Martin R 506 0.401 0.241 0.757 0.290
    Luis Gonzalez L 428 0.395 0.236 0.889 0.256
    Nick Swisher L 384 0.365 0.232 0.559 0.307
    Tadahito Iguchi R 462 0.422 0.230 0.678 0.243
    Frank Catalanotto L 309 0.372 0.228 0.821 0.319
    Mike Cameron R 539 0.399 0.224 0.569 0.237
    Reggie Willits L 274 0.328 0.219 0.818 0.455
    Brian Giles L 381 0.396 0.218 0.804 0.269
    Jim Thome L 330 0.358 0.203 0.545 0.239
    Bobby Abreu L 203 0.374 0.185 0.545 0.274
    Jack Cust L 294 0.310 0.175 0.563 0.342
    Wilson Betemit L 208 0.327 0.155 0.700 0.282
    Dan Johnson L 387 0.282 0.148 0.737 0.346


    Of course the interesting thing is that you can then plot the Fish and Eye metrics on a graph and then split the graph into four quadrants. Each quadrant creates a little profile that can be used to characterize a hitter's plate discipline. Note that the bottom left is the sweet spot.

    7 comments:

    Guy said...

    Great stuff, Dan. Could you post the averages for the 4 metrics so far?

    I wonder if you might not be better served in the long-run by inverting either Fish or Eye, so that a high score is either good or bad on both. It's a little confusing to have them reversed.

    Will the combined metric be called "fisheye"?

    When we have more data, you might consider breaking this down by home and away. A big part of home-field advantage is often a K and BB differential. It would be interesting to see if pitchers actually throw more strikes at home, or if visiting hitters have more trouble reading the strike zone, or some combination.

    Pizza Cutter said...

    Dan, thanks so much for the hat tip. I sent you an e-mail with some comments. Good work!

    Dan Agonistes said...

    Guy, the averages (for all players with 100 or more pitches seen) are

    Swing: 44.8%
    Fish: 32.1%
    BadB: 73.2%
    Eye: 26.7%

    I also created another metric I didn't publish called Square which is the percentage of pitches in the zone that were swung at and made contact with. The average is 87.1% and there are several players at 100% including Chone Figgins, Placido Polanco, Derek Jeter, and Esteban German. On the flip side Russell Branyan is 61.2%, Rob Bowen 65.4% and Jack Cust 69.7%.

    I totally agree with you on inverting one of them. I think Fish is the one to invert.

    Great idea on breaking it down by home and road. Thanks as usual.

    Dan Agonistes said...

    Pizza Cutter, you're welcome.

    I'll be publishing the "Square" metric shortly which you referenced in your email as "Holes". Great idea.

    Guy said...

    Dan: another part of the home/away story might be umpires giving more calls to the home team. You may have enough data already to look at that at an aggregate level. According to your earlier article, umpires call a strike on 87.4% of actual strikes, and call a ball on 90.7% of actual balls (allowing for the 1-inch error margin). If there's a home-team bias, we'd see a lower called-strike rate and/or a higher called-ball rate when home team is at bat.

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