The answer to that question is complicated. There is no easy yes or no answer and that is not so much because there are so many variables we don’t know the answer. It has to do with game theory. Oh, in case you didn’t watch the 6th game of the ALCS or you forgot, in the 8th inning with the Yankees up 3-2, Swisher bunted with a runner on first (and no out of course) and when he reached on an ROE, Melky bunted with runners on first and second.

Many people, including those who are sabermetrically inclined, typically decry the sacrifice bunt – why give away outs? The conventional (and lazy) sabermetric wisdom used to be that sac bunt attempts were almost always incorrect – at least ever since The Hidden Game of Baseball told us so and legions of sabermetric fans and even sabermetricians looked at the RE and WE tables and noticed that the game state after an out and base runner advance was worse than before – hence the sac bunt is wrong.

The problem of course is that that is a ridiculously simplistic way to answer the question on two fronts. One, the WE or RE before and after a “successful” sac bunt, using a standard table, is based on an average batter in an average lineup against an average pitcher and defense in an average stadium on an average Spring day. At least some analysts recognized that in different contexts, those numbers would have to be revised. However, most of them also noted that the gap was so large between the “before” and “after” state (in favor of the “before” state – which assumes hitting away most of the time), that it would take an enormously bad hitter -like a pitcher – to make it correct to bunt. They would basically be right.

Now, there is a more important and pertinent reason why looking at RE and WE charts and comparing the “before” and “after” numbers do not help you in answering the question as to whether a sac bunt (by a non-pitcher) is correct in any given situation. And that is because a sac bunt attempt obviously does not lead to an out and a base runner advance 100% of the time (or even close to 100%); in fact the average result from a sac bunt attempt is not even equivalent to an out and a base runner advance. Also, the average result varies a lot with the speed and bunting skill of the batter and whether and by how much the defense is anticipating the bunt or not (among other things).

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Double plays are called the pitcher’s best friend for a good reason. I think we’re all familiar with the huge swing in win expectancy that takes place when a pitcher wiggles out of a one-out, bases loaded jam with an inning ending double play. And it is a skill for infielders; some are clearly better at turning DP’s than others. It takes talent for a shortstop to field the ball quickly and cleanly, transfer to the second baseman, the second baseman must pivot, throw accurately and quickly to first base…you get the idea. Your routine 6-4-3 double play is probably a lot harder than it looks.

One of the components of UZR for infielders is DPR, or double play runs. It is simply (and I’m quoting word for word from the site glossary) “the number of runs above or below average a fielder is, based on the number double plays versus the number forces at second they get, as compared to an average fielder at that position, given the speed and location of the ball and the handedness of the batter.”

I definitely am the wrong person to get into the nitty gritty details of such things, but I can sort through leader boards with the best of them. I wanted to look at just some of the leaders and laggards of the keystone combos. One note before we jump in (and someone correct me if I’m mistaken) but it appears to me a typical shortstop or second baseman is usually about a maximum of plus or minus three runs in pivot or starting double plays, or in other words, the difference between a very good middle infielder and a very bad one is really only about ten double plays a year. So we can say that the ability to turn a double play can be pretty overrated. Range is much, much more important.

Your 2009 Top DP Combo thus far:

Jack Wilson and Freddy Sanchez. Hey, we talked about these two yesterday. Wilson has been worth 1.7 DP runs, Sanchez 1.4, for a total of 3.1 runs saved in turning the double play. Compare this to…

Your 2009 Worst DP Combo:

Hanley Ramirez and Dan Uggla: These two are a pitcher’s worst enemy. Hanley Ramirez has been a -1.4, Dan Uggla an ugly -2.2. That’s -3.6 runs for those of you scoring at home.

Getting back to Jack Wilson for a moment, FanGraphs has UZR data dating back from 2002. Wilson is by far the leader at double play runs with +15.6. Michael Young has been the worst at -7.8, and he wasn’t moved full time to shortstop until 2004.

A word about Dan Uggla — the man is in some sort of DP slump, as the three seasons prior (2006-2008) he led all second baseman with +6 runs. In fact, his ability to turn the DP is what salvaged his defensive value. DP’s aside, Uggla was a -6.7 UZR during those seasons. Brian Roberts was the worst second baseman at -6.1. Roberts was worth 7.7 UZR before factoring DPR, so his lack of ability to turn two offset what other defensive value he added. He’s the anti-Uggla.

Finally, the best keystone combo between 2006 until now was Yuniesky Betancourt and Jose Lopez of the Mariners, who were combined for +9.8 runs, or a full win. The fact that it took one DP combo to total a whole win over three and a half seasons drives home the fact that while that the ability to turn two is important, it is not nearly as important as we might have thought. Being that Yuniesky has been so brilliant at DP’s and yet so bad at everything else is also a reminder that range is waaaay more important.

One of the most difficult tasks a responsible baseball analyst must take on involves avoiding small samples of data to make definitive claims about a player. If Victor Martinez goes 4-10, it does not automatically make him a .400 hitter. We have enough information about Martinez from previous seasons to know that his actual abilities fall well short of that mark. Not everything, however, should merit a house call from the small sample size police because there are some stats that stabilize more quickly than others. Additionally, a lot of the small sample size criticisms stem from the actual usage of the information, not the information itself. If Pat Burrell struggled mightily after the all star break last season and started this season with similarly poor numbers, we can infer that his skills may be eroding. Isolating these two stretches can prove to be inaccurate, but taking them together offers some valuable information.

The question asked most often with regards to small sample sizes is essentially – When are the samples not small anymore? As in, at what juncture does the data become meaningful? Martinez at 4-10 is meaningless. Martinez at 66-165, like he is right now, tells us much, much more, but still is not enough playing time. What are the benchmarks for plate appearances where certain statistics become reliable? Before giving the actual numbers, let me point out that the results are from this article from a friend of mine, Pizza Cutter over at Statistically Speaking. Warning: that article is very research-heavy so you must put on your 3D-Nerd Goggles before journeying into the land of reliability and validity. Also, Cutter mentioned that he would be able to answer any methodological questions here, so ask away. Half of my statistics background is from school or independent study and the other half is from Pizza Cutter, so do not be shy.

Cutter basically searched for the point at which split-half reliability tests produced a 0.70 correlation or higher. A split-half reliability test involves finding the correlations between partitions of one dataset. For instance, taking all of Burrell’s evenly numbered plate appearances and separating them from the odd ones, and then running correlations on both. When both are very similar, the data becomes more reliable. Though a 1.0 correlation indicated a perfect relationship, 0.70 is usually the ultimate benchmark in statistical studies, especially relative to baseball, when DIPS theory was derived from correlations of lesser strength. Without further delay, here are the results of his article as far as when certain statistics stabilize for individual hitters:

 50 PA: Swing %
100 PA: Contact Rate
150 PA: Strikeout Rate, Line Drive Rate, Pitches/PA
200 PA: Walk Rate, Groundball Rate, GB/FB
250 PA: Flyball Rate
300 PA: Home Run Rate, HR/FB
500 PA: OBP, SLG, OPS, 1B Rate, Popup Rate
550 PA: ISO

Cutter went to 650 PA as his max, meaning that the exclusion of statistics like BA, BABIP, WPA, and context-neutral WPA indicates that they did not stabilize. So, here you go, I hope this assuages certain small sample misconceptions and provides some insight into when we can discuss a certain metric from a skills standpoint. There are certain red flags with an analysis like this, primarily that playing time is not assigned randomly and by using 650 PA, a chance exists that a selection bias may shine through in that the players given this many plate appearances are the more consistent players. Cutter avoids the brunt of this by comparing players to themselves. Even so, these benchmarks are tremendous estimates at the very least.

It’s almost opening day, and it seems like everyone is talking about projections.

When considering a projection, there are really two questions to be answered – what is the player’s “True Talent Level” right now, and how will he perform next year? Between now and the end of next year, his talent level very well might change, as he’s a year older and might recover from or succumb to injuries. Even then, there’s still the random variance of a single season performance. In this article I’d like to explore how some of the major projection systems work when predicting different subgroups of players.

I tested the following projections: PECOTA (2006-2009), ZiPS (2006-2009) CHONE (2007-2009) and my own Oliver (2006-2009).

By wOBA

The first test was to group the yearly projections to the nearest .010 of wOBA, and then see how that group of players actually performed. There were 468 players who had projections from all four systems, and had at least 350 plate appearances in the major leagues in the following season. As 2009 is yet to be played, and CHONE is not available for 2006, these projections to next year comparisons are for the 2007 and 2008 seasons. All four projections were tested on the same 468 players. The observed results were unadjusted major league stats, so that the results of the test would not be influenced by which park factors or MLE formulas I chose to normalize stats.

To read the results, CHONE of the players would have a wOBA between .375 and .385, averaging .380, 25 of them had 350 or more PAs in MLB in the following seasons, and those 25 players had an average wOBA of .363, so at that level CHONE was .017 high. Oliver was .008 high on 21 projections, PECOTA .027 high on 26, and ZiPS .014 on 26. The last line of the table shows the root mean square error (weighted by number of players). Oliver had the lowest mean error at .006, followed by CHONE .011 and PECOTA and ZiPS at .012 each.

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As promised, let’s split up the starters from the relievers. Rather than set an innings barrier, I instead opted to eliminate all of those with less than 50% of their appearances coming in the form of starts. Our friendly neighborhood Ryan Franklin no longer qualifies to be spoken about and the overall numbers drop like you would expect. Here’s the causality breakdown:

5 P – 7 dropped
4 P – 34 dropped
3 P – 64 dropped
2 P – 30 dropped

That leaves:
5 P – 21 pitchers
4 P – 85
3 P – 70
2 P – 9

How did they fare?

If you’re thinking to yourself that those numbers look a lot like those presented yesterday, then you have a good memory. In fact, here’s the differentials between yesterday and today:

Note that a negative value indicates a drop in FIP.

5 P: 0.02 runs
4 P: 0.03 runs
3 P: 0.08 runs
2 P: 0.66 runs

As expected, looking at mostly starters sees the run averages increase.

To address some concerns from this analysis:

I’m not looking at grips, arm slots, release points, etc. instead simply the classification of the pitch. Some of the classifications are erroneous or too simplistic for the pitch style. That’s understandable.

Also not looking at the quality of the pitch, that would take examination on a pitch-by-pitch basis.

Looking at individual pitchers before and after the addition of a new pitch is definitely something I’ll look into pursuing. No guarantees though.

I’ve been talking about pitches, pitching patterns, and pitch usage a lot lately. Whether it be through PitchFx charts, simply sharing observations, or talking about a pitcher who needs an additional pitch. Finally, I broke down and gathered the data needed to see whether having a surplus of pitches or only a couple mattered to performance.

Most people have the idea that quality matters more than quantity in mind. I know I did. In fact, while running the query (last three years, at least 5% usage of the pitch, at least 150 innings) only one pitcher recorded more than five pitches and that was Ryan Franklin with six. As you’ll see, Ryan Franklin is not a particularly good pitcher. Franklin is passable, but I think you would expect more from someone who has a constant advantage in game theory. Now, it is possible that Franklin falls into patterns, tips his pitches, or simply throws hittable garbage, I’ll leave that up to you to figure out, my only interest is the amount of pitches used modestly and whether it makes for better pitchers.

We begin today with that query I mentioned earlier. No restriction on amount of games started and only 150 innings over the last three years; meaning relievers like Joe Nathan, Mariano Rivera, and Jonathan Papelbon were eligible to make the cut. Let’s get to the data, shall we?

Franklin was the only pitcher with six pitches and 28 pitchers had five pitches qualify. Tradition has most starters throwing 3-4 pitches and most relievers having one or two. Tradition holds true here. 119 pitchers had four pitches qualify, 134 had three, and 39 had two. Franklin failed to make a start over the last three years meanwhile pitchers with 5 pitches saw 64% of their games come as starters, 60.2% for four pitch qualifiers, 32.2% for three pitches, and 11.7% for two pitches.

Let’s look at how they actually performed:

Are the relievers skewing the two and three pitch numbers? Tomorrow we’ll separate the starters from the pack and see if that’s the case.

Other than stolen bases, which I addressed a few weeks ago, very little has been published on catcher’s fielding numbers. Tom Tango first conceived his WOWY technique in studying catchers. Now I’ve extended my stolen base study back to the beginning of the current RetroSheet in 1953, and added the rates of wild pitches and passed balls allowed back to the same date. It should put a smile on Tango’s face that Gary Carter of his beloved Montreal Expos rates third in career SB_RAA behind Ivan Rodriguez and Jim Sundberg, and fourth in career WP_RAA behind Bill Freehan, Bruce Benedict and Brad Ausmus, and second overall behind only Pudge, along with the best single season of +28.2 in 1983…the worst, Dick Dietz, -18.6 in 1970.

I had earlier included groundballs to catchers when I ran my infield defense. There just aren’t that many grounders fielded by catchers – the most in any one season over the past sxi years was 74 by Jason Kendall in 2006. Single season RAA on grounders ranges from Jason Phillips’ +1.1 in 2004 to Mike Lieberthal’s -2.4 in 2003. Totals for the last six years range from Carlos Ruiz’s +2.4 to Lieberthal’s -3.4. (I don’t yet have a groundball table built for seasons before 2003).

The process is the same as I descrobed in the previous article on stolen bases. I queried RetroSheet’s events table, creating a new table of every combination of catcher and pitcher in each year, how many batters were faced with runners on base, and how many wild pitches and passed balls occured. A total was made of each catcher’s stats in each year (the “with” part) and also the stats of each pitcher he caught, while working with any other catcher (the “without”). These were weighted to the smaller of the sample sizes, and then summed into season and career totals.

The single best season for preventing wild pitches and passed balls, since 1953, was Bill Freehan of the Tigers in 1971. The pitchers he caught that year would have been expected to throw 62 wild pitches and 20 passed balls in Freehan’s playing time, but he only allowed 31 wild pitches and 7 passed balls to get by hum, saving an estimated 12.6 runs that season. His total allowed of 38 was 46% of the expected 82. Freehan had the highest career RAA of +52.0, while Jorge Posada had the lowest at -38.2.

On the other end is one of America’s favorites, who not only couldn’t hit, but apparently couldn’t catch either, Bob Uecker. In 1967, appropriately his last in the majors, in which Uecker split time between the Phillies and Braves, in only 80 games played he allowed 40 wild pitches and 25 passed balls, 222% above his expected totals of 18 and 12.

The major league average is .016 wild pitches and .004 passed balls per plate appearance with a runner on base. The best career normalized wild pitch rates go to Bruce Benedict, Yogi Berra and Mike Redmond at .010; Brian Downing, Del Crandall and Jason Varitek at .011; and Rod Barajas, Manny Sanguillen, Bill Freehan, Kirt Manwaring, Sherm Lollar and Steve Yeager at .012. The worst wild pitch rates are Earl Battey at .021; Junior Ortiz and Mike Macfarlane at .021; and Miguel Olivo, Johnny Roseboro, Tim Laudner, Jorge Posada, Pat Borders, Thurman Munson, Hal Smith, Darrell Porter and George Mitterwald at .020.

The lowest normalized passed ball rates were Brian Downing, Charlie O’Brien, Bruce Benedict, Dan Wilson, Yogi Berra, Brad Ausmus, Del Crandall, Sherm Lollar and Ron Karkovice at .002, with the worst being Miguel Olivo and Bob Brenly at .008; and Joe Azcue, Jorge Posada, Earl Battey and Lance Parrish at .007.

The top 5 ratios of reducing both are Bruce Benedict 56%, Yogi Berra 59, Brian Downing 60 and Mike Redmond and Del Crandall 64% each. The worst were Bob Brenly 142%, Earl Battey 140, Miguel Olivo 140, Jorge Posada 132, and Junior Ortiz and Mike Macfarlane 129% each.

In 2008, the best at runs saved blocking the plate were Kurt Suzuki +6.9, Kenji Johjima +5.8, Brian McCann +5.2, Ramon Hernandez +5.1 and Jason Varitek +4.1, while the worst were Miguel Montero -3.0, Miguel Olivo -2.8, Kevin Cash -2.7, Greg Zaun -2.7 and Jesus Flores -2.6. In case you were thinking that one year might be a small sample size for some of these backup catchers, Montero, Olivo and Flores are also among the five worst career rates for active catchers, along with Mike Rivera and Jorge Posada.

Career WP&PB Records
Yearly WP&PB Records

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One of the really cool things that Baseball Info Solutions keeps track of is when there is a shift and it effects the outcome of the play. If it doesn’t effect the outcome of the play, it’s not recorded as a shift, even if one was employed.

In 2008, the top 5 players that were most effected by shifts (positively or negatively) were:

Carlos Delgado
Ryan Howard
Jim Thome
David Ortiz
Adam Dunn

Delgado’s BABIP on shift effected plays was at the .191 mark, compared to his .284 BABIP on every play. This is entirely different from say, Ortiz’s BABIP on shift effected plays which was .299, compared to his overall .273 BABIP. Makes you wonder if shifting on Ortiz is a good idea, though it would definitely take a deeper dive into the numbers to know for sure.

Anyway, this was really just a quick preliminary look at the data, but with everyone talking about shifts and BABIP lately, I thought this might be of some interest.

After a leak of the results of MLB’s 2003 anonymous survey testing for performance enhancing drugs, Alex Rodriguez admitted in an interview with ESPN’s Peter Gammons using them from 2001 to 2003, the three years he played for Texas.

“When I arrived in Texas in 2001, I felt an enormous amount of pressure, felt all the weight of the world on top of me to perform and perform at a high level every day.”…When asked if his usage took place from 2001-2003, Rodriguez said, “That’s pretty accurate.”
Rangers owner Tom Hicks, who took over the team in 1998, was shocked by Rodriguez’s admission.
“I certainly don’t believe that if he’s now admitting that he started using when he came to the Texas Rangers, why should I believe that it didn’t start before he came to the Texas Rangers

If ARod started using PEDs in 2001, then they had no effect, as his three years in Texas are statistically indistinguishable from his previous two years in Seattle. His jump in performance was between the 1998 and 1999 seasons.

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Most of you are familiar with Ultimate Zone Rating (UZR), a metric for evaluating fielding using detailed play-by-play data. For first basemen, like all other fielders, it measures the number of ground balls that are fielded and turned into outs as compared to an average-fielding first basemen, given the same number, type, and location of ground balls, as well as the same number of outs, the base runner configuration, and batter handedness (plus an adjustment for the parks and the ground/fly tendency of the pitchers). The difference between these two values is a fielder’s UZR. It is usually expressed as a number of runs saved or cost compared to an average player at that position over a specified period of time (usually defensive games). You will often see it as a rate stat, generally per 150 games.

What UZR does not measure for first basemen, because the requisite data is not readily available, are the theoretical runs saved or cost by virtue of a first basemen’s skill at successfully catching errant throws or throws in the dirt. For lack of a better word, I call these “scoops,” even though they necessarily include poor throws (e.g., high or off-line) that are not in the dirt.

Fortunately, there is a way to estimate this skill, using a relatively simple method “invented” by Tom Tango, called “without and with you”, or WOWY. The WOWY methodology was explained by Tango in an excellent article in the 2008 Hardball Times as well as in various online forums, including his (and my) blog, www.insidethebook.com.

Basically, it goes like this: Figure out what happens when a particular player is on the field (generally something that explicitly involves that player, or at least is affected by that player, such as the number of ground balls a particular SS – say Derek Jeter – fields and turns into outs). Then figure out the same thing when that player is not on the field, but all other relevant variables (in this example, mostly the pitcher, park, and opponent batters) remain constant. The difference between the two rates (per whatever you want) should reflect the difference in skill, or at least in performance (skill is usually performance regressed toward some mean, the amount of regression being a function of the sample size of the performance) of whatever you are measuring, between the player in question and the average player when the player in question is not on the field (but in the data set).

In other words, to use the Jeter example, if Derek fields 4 balls per 9 innings and all other SS (let’s say that our sample of “other SS” is large and unbiased enough to consider them league average) field 4.5 balls per 9 innings, given the exact same pool of pitchers, batters, and parks, then we can safely say (more or less – within the bounds of sample error) that Jeter is .5 balls per 9 innings worse than the average SS. As I said, it is simple and brilliant. The results are extremely telling if we can get large enough samples of Jeter and lots of other SS that are “matched” with Jeter based upon parks, pitchers, batters, etc.

The methodology is a little more complicated than that, but hopefully you get the general idea. Anyway, the same thing can be done with first basemen in order to estimate their “scooping” performance and ability. What I did was look at every ground ball that was thrown from each infielder to each first baseman. I put that ground ball into one of two buckets: One, when there was no error on the throw, or two, when there was an error on the throw. The assumption is that when a throwing error is made, there is an errant throw (no duh) and the first baseman is not able to somehow coax that bad throw into an out by scooping it out of the dirt, jumping in the air, catching it while off the bag and still making the play, etc. Obviously most of the time when a throwing error occurs, there is nothing that any first baseman can do about it. However, in the long run, we can assume that a certain fixed percentage of bad throws from each infield position will always result in an error while another fixed percentage of those bad throws have a chance to be “saved” by a skilled (or tall) first baseman.

We can also assume that when an error is not made, that sometimes a bad throw occurs and the first baseman “saves the day.” Again, most outs and non-errors occur on easily catchable throws, but a certain fixed percentage of them will occur on bad throws that are skillfully and successfully handled by the first baseman.

Given these assumptions (which are true, by the way), if we look at all throws by a particular player at a particular position to a certain first baseman and then compare the results (error or no error) to when throws are made from the same infielder at the same position to all other first basemen, the difference can be attributed to the “scooping skill” of that particular first baseman.

An example:

Say that all infielders threw to Todd Helton 1000 times in 2007. And say that those exact same infielders threw to some other first baseman (and we are going to assume that all of these “other” first basemen are average, collectively) around 1000 times also (it does not really matter how many throws went to these other first basemen, although we would like it to be a lot). Now let’s say that 20 throwing errors were made on those throws to Helton and only 15 were made to all the other first basemen. We can safely say (with some uncertainty of course, due to sample error) that Helton was 5 plays per 1000 throws (about a full season actually) better than the other pool of first basemen, who we are assuming are average. So Todd is 5 plays, or around 4 runs, per season, above average at “scoops.”

In actuality what I did was to match up every player at every infield position (including pitcher and catcher) with every first basemen, and then for each, specific first baseman, I did a “with and without” and prorated or weighted that difference by the minimum of these two numbers – the throws made to the first baseman in question and the throws made to all other first basemen – by the same fielder.

For example, let’s say that over the sample time period, Edgar Renteria threw 300 balls to Albert Pujols (by the way, I used all data from 2000 to 2008) and he made 6 throwing errors. Now let’s say the Renteria also threw 800 balls to other first basemen (on the Cardinals or any other team he played for) and made20 errors. The difference in error rate is .5 errors per 100 throws in favor of Albert. Thus we give him credit so far for .5 errors per 100, weighted by 300 throws (the lesser of 300 and 500). We do that for every fielder who threw to Albert at least 20 times and 20 times to other first basemen, and then we add everything up to get a weighted average. This weighted average represents a first baseman’s “scooping” skill as compared to an average first baseman, or at least as compared to the average first baseman in that player’s “matched pool” of first basemen.

That last part of the last sentence is the primary flaw in the methodology. Since I am only comparing each first basemen to all other first baseman who had the same fielders throwing to them, it is possible, and in some cases, inevitable, for one first baseman to be compared to a pool of primarily good or bad first basemen, since each one will tend to be compared with the others on his own team. For example, Helton will tend to be compared with whomever else manned first base for the Rockies when he was not on the field. If those few backup first basemen happened to be particularly bad at “scooping” balls, then Helton will be overrated by this methodology. In fact, because of this flaw, and because regular first basemen tend to be better at “scooping” than backup first basemen, any regular will tend to be overrated (because they are often compared to a backup) and any backup will tend to be underrated (because they are often compared to a regular). Keep in mind, for example, that because some of Helton’s infielder teammates played on other teams, he is not always going to be compared to another Rockies’ first baseman. Anyway, for now, it looks like we are going to have to live with this flaw or weakness in the system. The correct way to handle the data, of course, is to adjust each first baseman’s rating by that of his “others” by doing an iterative process. In the future I may do this, but for now, we are stuck with version 1.0.

Here are the results:

Again, I used play-by-play data from 2000 to 2008.

Interestingly, and not unexpectedly, there were significant across-the-board differences in the scooping ability of the average tall and short, lefty and righty, and regular and backup first baseman.

Tall (>6’1”) RH: .6 runs per 1000 matched throws, or around a full season.
Tall LH: 1.2 runs

Short RH: -.8 runs
Short LH: .6 runs

Less than 300 matched throws: -1.5 runs
300 to 1000 throws: .4 runs
More than 1000 throws: .6 runs

Players with at least 1000 matched throws total where the minimum of the two pair for each fielder is added to the total:

Best per 1000 throws

Berkman +4 runs, 2928 matched throws
Choi +4, 1361
Conine +3, 3100
Connor Jackson +3, 1790
Loney +3, 1679
Mientkewicz +3, 4344
D Ward +3, 1119
Olerud +2.5, 4481
Sexson +2.5, 6471
Tony Clark +2.5, 2880
Dan Johnson +2.5, 1774
Overbay +2.5, 4834

Worst

Karros -4, 2986
Mo Vaughn -4, 1644
Galaraga -3, 1842
Stairs -3, 1122
Bagwell -2.5, 4931
Casey -2.5, 6444
Julio Franco -2.5, 2010
Swisher -2.5, 1134
Thome -2.5, 4476

When we regress these numbers in order to turn them into “true talent” scooping ability, we get slightly smaller values, depending on the number of matched throws (sample size) of course. In fact, it appears that the spread in talent between the best and worst “scoopers” at first base is on the order of 2-3 runs, plus or minus (a 4-6 run spread). So before you start opining about how your favorite first baseman is so great defensively because he “saves so many errors,” consider that scooping ability is probably worth less than a ¼ of total defensive ability or value at first base. Fielding grounders is at least 75% of the package and “scooping” is the rest. But every little bit helps.