You Can Count on It
“The most important thing we want our guys to understand is we need to get into better hitting counts. When you get into better hitting counts, you become better hitters. If you look at the numbers in the NL the past few years, guys in 2-1 counts were hitting .340, when the count was 3-1 they were hitting .330. What we’ve tried to accomplish is to be more patient so we can get ourselves into better hitting counts.”
—ex-Pirates manager Lloyd McClendon, Tribune-Review April 8, 2005
Unfortunately for McClendon, this philosophy didn’t translate into more runs for the Pirates, who were 28th in the majors (behind even the Royals) when he was fired this week. But that doesn’t mean that McClendon wasn’t right.
Earlier this week I talked about how pitch data is often used to evaluate players by looking at pitches per plate appearances and things like the percentage of swings and misses, first pitch swings, fouls and taking balls. And while all of those measures are interesting, what I think gets to the heart of the matter is how well a player controls the strike zone and is therefore able to select a pitch to hit rather than having to react to a pitcher’s pitch. So this week I’ll take a look at how we might measure that skill using pitch data.
Getting in the Zone
The Retrosheet data that I used for the previous article does not include pitch location, so a direct analysis that looks at how often a hitter swings at pitches out of the strike zone is not possible. In the strictest sense then, measuring the ability to control the strike zone is out of our reach. However, the counts at which a hitter’s plate appearances end are recorded. As a result, we can group the counts into hitter’s counts, pitcher’s counts and neutral counts based on McClendon’s reasoning that hitters who end up in favorable counts do so because they’re more selective and avoid swinging at pitches out of the strike zone early in their at-bats.
For this article I’ve grouped the counts as follows:
Count Hitter Pitcher Neutral 0-0 X 1-0 X 2-0 X 3-0 X 0-1 X 0-2 X 1-1 X 1-2 X 2-2 X 2-1 X 3-1 X 3-2 X
The categories in which the counts fall are pretty self explanatory, although some might argue that a 3-2 count is a hitter’s rather than a neutral count. Although the on-base percentage (OBP) recorded at 3-2 is high, the batting average is typically not, so it seems to lend itself to a neutral count. And although hitters do hit fairly well in 0-0 counts, I placed it in the neutral group since neither the pitcher nor the hitter has an inherent advantage on the first pitch of the at-bat, with the pitcher not pressured to throw a strike and the hitter not pressured to swing.
To then see which players were more proficient in getting themselves into advantagous situations, I took a look at the 473 hitters who amassed 500 or more plate appearances (not including intentional walks and hit-by-pitches) in the years 2000 through 2004 and counted the number of plate appearances for each hitter that fell into the three categories. I then calculated the percentage of plate appearances in each category, along with their on-base plus slugging (OPS) normalized for league, year and park (NOPS).
The top 20 hitters in getting into hitter’s counts were:
NOPS PA HCount PCount NCount HPct PPct NPct Barry Bonds 179 2704 973 683 1048 0.360 0.253 0.388 Mark Grace 104 1640 547 498 595 0.334 0.304 0.363 Brian Giles 129 3227 1043 1009 1175 0.323 0.313 0.364 Chipper Jones 125 3163 1018 959 1186 0.322 0.303 0.375 Luis Gonzalez 123 3111 995 1142 974 0.320 0.367 0.313 Carlos Baerga 98 510 161 198 151 0.316 0.388 0.296 Bernie Williams 114 3059 954 1011 1094 0.312 0.331 0.358 Lenny Harris 85 860 264 314 282 0.307 0.365 0.328 Rafael Palmeiro 114 3268 985 1140 1143 0.301 0.349 0.350 Albert Belle 106 607 181 182 244 0.298 0.300 0.402 John Olerud 111 3089 919 1132 1038 0.298 0.366 0.336 Mark McLemore 95 2108 626 721 761 0.297 0.342 0.361 Scott Hatteberg 99 2382 707 940 735 0.297 0.395 0.309 Barry Larkin 99 1830 543 719 568 0.297 0.393 0.310 Kenny Lofton 100 2730 803 982 945 0.294 0.360 0.346
On average these hitters are putting the ball into play around 30% of the time when ahead in the count. The interesting thing about this list is that it doesn’t just include good hitters like Chipper Jones, Luis Gonzalez and Bernie Williams, but it also includes players with high walk rates such as Lenny Harris, Kenny Lofton and Mark McClemore. As you might expect, this indicates that getting into hitter’s counts, while good for the batting average and slugging percentage, is also a big boon to eventually reaching base via walks.
The “top” 20 hitters in getting into pitcher’s counts were:
NOPS PA HCount PCount NCount HPct PPct NPct Alex Gonzalez 91 2259 367 1231 661 0.162 0.545 0.293 Joe McEwing 87 1144 239 620 285 0.209 0.542 0.249 Rod Barajas 80 906 163 479 264 0.180 0.529 0.291 Shawon Dunston 96 571 101 301 169 0.177 0.527 0.296 Gerald Williams 85 1179 180 621 378 0.153 0.527 0.321 Angel Berroa 93 1293 237 678 378 0.183 0.524 0.292 Gerald Williams 85 1171 180 614 377 0.154 0.524 0.322 Homer Bush 75 750 141 390 219 0.188 0.520 0.292 Alfonso Soriano 107 2749 545 1425 779 0.198 0.518 0.283 Vance Wilson 93 691 146 358 187 0.211 0.518 0.271 Dee Brown 79 832 149 426 257 0.179 0.512 0.309 Doug Glanville 85 2240 419 1146 675 0.187 0.512 0.301 Mark Ellis 94 1010 191 513 306 0.189 0.508 0.303 Tomas Perez 93 1025 197 517 311 0.192 0.504 0.303 Jared Sandberg 92 698 109 352 237 0.156 0.504 0.340 Tony Womack 87 2773 570 1397 806 0.206 0.504 0.291 Bill Hall 90 606 120 305 181 0.198 0.503 0.299 Pat Meares 83 779 173 392 214 0.222 0.503 0.275 Adrian Beltre 111 2952 592 1483 877 0.201 0.502 0.297 Mike Difelice 81 828 143 415 270 0.173 0.501 0.326
This is a group you don’t want to be in unless you have the uncanny ability to hit waste pitches out of the strike zone like Alfonso Soriano and, to a lesser extent, Adrian Beltre. The Alex Gonzalez in the list is the one from Florida, not Tampa Bay. You’ll also notice that many of these players did not garner a large number of at-bats due to their low NOPS. And those low NOPS numbers come home to roost when comparing the two lists; the average NOPS of the first was 112 while that of the latter was just 89.
I also found that when looking at seasonal data the correlation between a high percentage of plate appearances in pitcher’s counts and NOPS got stronger (more negative) as the plate appearance threshold I used went up. In other words, as hitters garner more plate appearances in a season, it is more important for them to avoid getting into pitcher’s counts. This is just what we would expect if there were a true correlation here and not simply randomness. In a smaller sample size a hitter is more apt to get lucky in a few at-bats, which raises his NOPS and masks the relationship between getting into hitter’s counts and being successful at the plate.
Controlling the Zone
But controlling the strike zone is really a combination of getting into favorable hitter’s counts and avoiding favorable pitcher’s counts. To attempt to measure this I then divided the HPct by the PPct so that this ability could be represented by a single number. The leaders in the resulting CZoneRate were:
NOPS PA HCount PCount NCount HPct PPct NPct CZoneRate Barry Bonds 179 2704 973 683 1048 0.360 0.253 0.388 1.425 Mark Grace 104 1640 547 498 595 0.334 0.304 0.363 1.098 Chipper Jones 125 3163 1018 959 1186 0.322 0.303 0.375 1.062 Brian Giles 129 3227 1043 1009 1175 0.323 0.313 0.364 1.034 Albert Belle 106 607 181 182 244 0.298 0.300 0.402 0.995 Bernie Williams 114 3059 954 1011 1094 0.312 0.331 0.358 0.944 Luis Gonzalez 123 3111 995 1142 974 0.320 0.367 0.313 0.871 Mark McLemore 95 2108 626 721 761 0.297 0.342 0.361 0.868 Rafael Palmeiro 114 3268 985 1140 1143 0.301 0.349 0.350 0.864 Matt Lawton 103 2853 835 982 1036 0.293 0.344 0.363 0.850 Moises Alou 116 2902 801 950 1151 0.276 0.327 0.397 0.843 Lenny Harris 85 860 264 314 282 0.307 0.365 0.328 0.841 Frank Thomas 121 2328 682 833 813 0.293 0.358 0.349 0.819 Kenny Lofton 100 2730 803 982 945 0.294 0.360 0.346 0.818 Lance Berkman 129 3052 839 1030 1183 0.275 0.337 0.388 0.815
When looking at all 473 players as a whole there is a connection between controlling the strike zone and recording a high NOPS. Below is the scatter plot of all 473 players and their NOPS plotted against the CZoneRate. The yellow line is the linear regression line that shows a pretty healthy positive (correlation coefficient of .456) correlation between the two measures.
Although I’m guessing it won’t offer much consolation at this point, tell your friends that Lloyd McClendon was right.
