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March 4, 2003 Breaking BallsGetting Defensive: The Basics
The basic problem with trying to evaluate fielding performance is that it's too complicated. The greatest change in baseball thought over the past 20 years has been the shift of focus from one offensive statistic (number of hits / number of times to plate that did not result in a walk) to a better one (number of times reached base / number of times at the plate). Granted, I realize that I'm omitting sacrifice flies and catcher interferences there, but that's the essence of batting average and onbase percentage. If you only knew onbase percentage, you'd do pretty well comparing players. Unfortunately, there is no easy way to do this with fielding statisticsa fact that results in a disagreement between our eyes, instincts, and what we read. I've been trying to educate myself on fielding statistics for the last couple years, and I want to admit up front that I have not been able to reconcile them with my own evaluation. When I see Mike Cameron rated as a slightly aboveaverage center fielder last year, I roll my eyes, because I have in my head a mental image of how far he can go to get a balla massive expanse few visiting outfielders can cover. The issue, though, is that it's not an accurate picture or particularly useful in evaluation. The first unit of mainstream fielding evaluation is the error. A player gets an error when he makes an obvious gaffe that results in a runner advancing. With this knowledge, raw errors can be compared: Orlando Cabrera led the major league shortstops last year with 29 errors, while Mike Bordick only made one. That said, there are a few obvious problems with errors. First, they are the subjective judgment of the official scorer of that game. For an error to awarded, the scorer must believe that an ordinary effort would have gotten the runner out (or not allowed the runner the extra base). Hitters want to be credited for hits, while fielders don't want to be marked with errors. This has led to direct lobbying of scorers by players and teams on decisions, and because the scorer is teamemployed rather than a member of the umpiring crew, there's a bias, conscious or not. It also requires the scorer make an evaluation on what an average player at that position would have done. If you're a hometown scorer for a bad shortstop, half the plays by a shortstop you see are going to be that bad shortstop; he's going to change the average you're scoring against, no matter how you resistit's the nature of perception. Second, it's a counting statistic, like home runs. Boston's Lou Merloni played an inning in left and made no errors. Does that mean he was a better left fielder than Jacque Jones, who made five errors while playing regularly all season. Hell no. The error is also a little teamdependent: a shortstop with a slick gloveman at first base will have fewer errors because the ball will get past the first baseman less often on errant throws, which advance the runner and result in an error for the shortstop. To turn errors into a rate statistic, we have fielding percentage, as (assists + putouts) / (assists + putouts + errors). This introduces a couple of new counting stats. First, though, a plea for indulgence: as I talk about the different statshere and elsewhere, for the sake of ease of reading and writingI'm going to gloss over much of the messy details. I'm going to talk about putouts without interference, for instance, and assists without the wording in the rules about deflection or slowing the ball mentioned. Putouts are outs the fielder is directly involved in: they catch the fly ball, they tag a runner, or catch a ball while touching a base for a forceout. Catchers are credited for a putout on a strikeout as well. Assists are awarded when a throw on a ball is made that results in a putout. For instance, a shortstop who fields a ground ball and throws the runner out at first is awarded with an assist, and the first baseman gets a putout. Sixfourthree double plays get the second baseman both a putout (forcing the runner to second) and an assist (throwing the batter out at first). This sounds pretty good, but again, it depends entirely on the error to determine the effectiveness of a shortstop. Oneerror Mike Bordick's fielding percentage for 2002 was .998, while 29error Orlando Cabrera's fielding percentage was .962. In a way, it helps to imagine errors as if they applied to plate appearances. Say Alex Rodriguez bats against Mike Mussina 10 times, and:
That's a .400 onbase percentage, right? Four hits, six outs, in ten chances. Not under the fielding percentage system, though: he'd be scored as having an error on that popup, and the extra hit would have been no more than another successful chance. ARod's "batting percentage" would be (4 hits) / (4 hits + 1 error) = .800. Meanwhile, Rey Ordonezwho goes 210 on the same day, with no great hits and no mistakeswill post a (2 hits) / (2 hits + 0 errors) = 1.000 "batting percentage." And you can't build a house on a bad foundation. Where batting average depends a little on errors, fielding percentage depends entirely on errors, and errors aren't a good statistic. Enter Range Factor. It's a STATS, Inc.invented statistic, designed to measure the number of plays a player successfully makes. There are many ways I've seen this defined, but the official STATS, Inc. method off their glossary page is that Range Factor is calculated as ((assists + putouts)*9) / (defensive innings played). Innings played is the number of outs seen by a player while at that position, divided by three. Multiplying the assists and putouts by nine approximates the number of plays that the player would make at a position if he played there for an entire game. For example, if I play shortstop for an inning and I catch a popup and field a groundball I throw to first for a forceout, my Range Factor is (1 + 1)*9 divided by 1 = 18. I would be the best shortstop of all time if I kept that up. An average shortstop's range factor's about 4.6. Range Factor offers a sometimes dramatically different picture than fielding percentage when looking at players. Mike Bordick remains near the topsecond at 5.07but Colorado's Juan Uribe jumps to 5th with 4.89. Derek Jeter, who made a modest 14 errors and ranks in the middle of the pack in fielding percentage, drops to tie Tony Womack for deadlast, both with an RnF of just 3.81. Think about that: Juan Uribe makes an extra out a game over Derek Jeter. Range factor is flawed too, though in different ways than we've seen before. Because it's a measure of the number of outs a player creates in a measure of time, it is dependent on opportunity. The number of balls a player sees can vary tremendously. Hypotheticallly, if I was the shortstop on a team with a staff of flyballers, while my identical (and totally illegal) clone Kered played behind a rotation of groundballers, my clone would make many more plays and have a Range Factor much higher than mine. Which brings us back to our subjects. Mike Bordick played behind a staff that has a ground ball to fly ball ratio of 1.31 in 2002allowing 2,119 ground balls to 1,620 fly balls. Meanwhile, Juan Uribe's team had a similar G/F ratio of 1.32allowing 2,108 grounders to 1,603 fly balls. But Jeter. The Yankee staff got far fewer groundballs either the O's or Rockies did; their ratio was 1.15, as they gave up 1,907 ground balls and 1,655 fly balls in 2002. With an extra 200 ground balls over a season, it's not hard for a shortstop to pick up one a game and see their Range Factors climb. Even if you figure that those grounders are evenly distributed among the infielders, 50 extra grounders fielded would be an extra 0.3 on your fielding percentage, and when the difference from besttoworst is 1.4 points, that's significant. Can you adjust for the difference in opportunity? As a quick diversion, here's how often a couple of shortstops made a play (PO + A) as a percentage of all the team ground balls:
Back to Range Factor. It's also affected to some extent by park effects, too, since a hitter's park with no foul territory will result in fewer putouts for corner infielders, catchers, and to a lesser extent, corner outfielders. To put Range Factor to work, it's best to compare a player to his onteam comparables. Unfortunately, this is hard to do because sample size enters into it: if ARod plays 160 games at shortstop, there's almost no innings there by other players to compare his numbers against. Still, let's say you're interested in whether a particular player has deteriorated over a period of a couple years while in the same park. You can compare his numbers at the same position against his backups, and maybe start to draw some conclusions. Range Factor's not really too complicated, but on a gamebygame basis, I don't look at my handscored game and say, "Well, looks like Guillen had a good day in the field todayhe had four putouts and two assists, for a Range Factor of six." Now this partly because it's unlikely Carlos Guillen would have a game that good, but also because the scoresheet is offenseoriented (which is an entirely different issue). Still, if MLB keeps packing commercials into the halfinnings, it's something for me to do between hitting the can, buying more beer, and getting into trouble. Of course, there's the other problem, in that it's still not a perchance rate stat like batting average or OBP. It's a purely aesthetic criteria, but it's weird to explain to a newcomer that Barry Bonds gets onbase a spectacular 50% of the time, but in the field Derek Jeter makes under four plays per game. Defensive Average (DA) and Zone Rating (ZR) are intended to try and resolve some of these issues. Defensive Average grew out of Project Scoresheet and the Baseball Workshop, when widespread availability of the location of every ball hit allowed people to attempt to assign the responsibility for fielding each ball to a player. Then it becomes possible to measure that against whether they fielded the ball successfully. Zone Ratings allow for areas of the field where balls hit to some areas aren't assigned to a playerareas not considered "fieldable," essentially, where it's no one's fault if a ball drops for a hit. Both offer what should be the magic bullet: a percentage number we can use like batting average. Looking at STATS, Inc.'s Zone Rating, for instance, we can say that Alex Rodriguez fields 92% of balls hit into his zone, Mike Bordick 90%, with Juan Uribe taking a huge hit to 84%. And Jeter? Secondtolast with 80%, ahead only of Tony Womack at 76%. Defensive Average and Zone Rating both have deficiencies, though. For one, there's the issue of where a ball landed. It's done by humans, marking it down on a sheet from some terrible seat in the stands. Plus, the areas of responsibilities are not defined differently for each park, and that opens up a whole box of other problems. They're defined radially from home plate, and each park is divided into slices in the same way. This means that while the infield covers zones of the same size in every stadium, a park with a deep center field will have a much greater area in those zones than a shallow one, and a center fielder assigned to patrol that area has a much harder task ahead of him. There's also the issue of what happens to balls that can be taken by more than one fielder. Think of easy popups. If the same outfielder gets all of them, his ratings are going to go up while his buddies will have theirs depressed. It's a complementary system. After all, unlike the number of runs you can score, or times yu can get on base, the number of outs your team records on defense is finitesomewhere in the ballpark of 4,500 per season. ZR may also face some problems with positioning. If a team has really good data on where balls fall against their teamor even better, each pitcher and batterthey may adjust their positioning dramtically (think of the Bonds shift, for instance) while these zoneresponsibility measures assume they stand around in standard formation before the ball is hit. That wellpositioned outfield might make many more plays in what is usually considered "unplayable" territory, while letting fewer hits go into zones that woiuld normally be covered. (Zone Rating counts outs made outside the area of responsibility, though, so shifting shouldn't hurt a player's ZR unless the shifts are counterproductive). It also means a player in a defensive position that doesn't see many balls could (for example) get more credit for playing shifts if they mean that he makes more outs. Now, you're not going to see that happen in games, but there it is. Fielding percentage, Range Factor, and Zone Rating are the three mostcommonly available fielding measures. They all have problems, but together we can use them to draw rough conclusions. You can't hide a truly awful or outstanding defender. Alex Rodriguez is the fourthbest regular shortstop in fielding percentage, ninthbest in Range Factor, and first in Zone Rating. You can rest assured he's pretty good. Similarly, Tony Womack is thirdworst in fielding percentage, tied for last in Range Factor, dead last in Zone Rating. Survey says: the man's pretty awful with the glove. And for the most part, that's where the normal fan's interest stops. People understand batting average, onbase percentage, and (for the most part) slugging. Does he get hits? Does he get on base? Does he hit for power? Those three fielding stats are similar: Does he make errors? Does he make many plays in a game? Does he make an out on a ball he should get to? Except that they don't really tell us those things, not as well as we'd like. I took last year's pitching lines for every team along with their overall fielding ratings. It's a small sample in one sense (it's 30 teams), but it's not awful considering it's also a fullseason of defensive chances and all. I looked for correlation between raw runs scored and the different measures. Raw runs scored isn't particularly good: you're going to see a team like Arizona, with two dominating strikeout pitchers, throw off your lines, but overall it's worth looking at. And correlation, of course, is not causation. Quick note on correlation: Let's say you've got two series that match perfectly. You'll get a correlation of 1. Or, let's say you've got two series, and one counts down to zero from 100 as the other ascends (say, drinks bought versus cash in wallet on a Friday). You'll get a correlation of 1. Two lists of random numbers gets you a 0. Correlation between batting average and runs scored runs about .800, OBP and runs scored about .900. Fielding percentage proves okay. I mean, sure, it sucks, but in raw form it's got a .398 correlation efficient, which is decent enoughif your team gets tagged with more errors, it allows more runs. It's a modest correlation. It makes me wonder, too, if suggestions for improving the scoring of errors (adding a man to umpiring crews, and the fifth man is the official scorer for that game, or having the league employ the scorers, and do some training and peer review) would make errors respectable enough to be useful. Zone rating came out at .250, which is sort of weakly modest: as range factor drops, runs allowed increased, sort of. Range factor had little correlation at all, at .060. Based on my sketchy sketching, even the mostmocked of individual offensive statistics, batting average, has a correlation with runscoring twice as high as the best fielding stat offers us in the prevention of run scoring. In the course of writing this, I did a little further work trying to remove pitching and baserunningrelated events from run totals, and while fielding percentage remained about the same, the correlation for zone rating went through the roof (to a modest .590, which is a good negative correlation). While offensive stats don't have much to close to explain that last slice of run scoring, there's an alphabet soup of additional offensive metrics: RC, RC/27, ISO, SEC, OPS, along with parkadjusted versions of all these, all offering slightly different pictures of offensive contributions. In the same way, researchers trying to close the much larger gap between fielding and run prevention have started to separate pitching from fielding, and in so doing they have created evermore complicated formulas, which we'll get to later this week. Next Time: Advanced concepts. 0 comments have been left for this article.
 