Digging Deep Into Inside Edge’s Fielding Data

The deck has been stacked against the defensively-challenged Eric Hosmer. (via Keith Allison)

The deck has been stacked against the defensively-challenged Eric Hosmer. (via Keith Allison)

Inside Edge Scouting Services embodies the never-ending pursuit to quantify every aspect of baseball. With the introduction of Statcast data into the public sphere of baseball analysis this year, Inside Edge finds itself with MLB-sponsored competition. The two can work in harmony, though, to help expand our understanding of how to put a price on defensive aptitude.

For every single ball put into play, the wizards at Inside Edge spatially code it, like plotting a landmark on a map. FanGraphs then uses these data to populate both hitters’ spray charts and defensive fielding charts. As an example, ere are two Mike Trout charts:

mike trout plays

FanGraphs adeptly explains the ins and outs of Inside Edge Fielding, but here’s a 30,000-foot overview. Each batted ball is assigned a percentage chance (or probability) it could have been fielded cleanly. Inside Edge established a series of bins into which batted balls of a particular probability are sorted: Impossible (0%), Remote (1-10%), Unlikely (10-40%), Even (40-60%), Likely (60-90%), and Routine (90-100%). I will henceforth refer to these bins as “levels of difficulty,” for ease of understanding.

This methodology differs from that of Baseball Info Solutions (BIS), another prominent provider of fielding data that is used in the calculation of Defensive Runs Saved (DRS) and Ultimate Zone Rating (UZR). While BIS’ metrics can be acutely mathematical — bordering on downright complicated — Inside Edge takes a simpler (but not elementary) and perhaps more intuitive approach to quantifying a player’s defensive aptitude.

It might be too much to ask Inside Edge to create 101 separate levels of difficulty — one for each percentage chance, from zero percent to 100 percent. It’s almost too granular and possibly too burdensome to feature comprehensively on a site such as FanGraphs. But the lack of granularity inherent to providing six levels of difficulty distills our collective comprehension of the exact chances a player might have to convert a particular play.

What we can do, instead–and, by no coincidence, what I’ve done here for you, because I love you–is:

  1. Take what is now four years’ worth of binned fielding data.
  2. Aggregate it.
  3. Examine major league-wide trends.
  4. Reduce them back down to the player level, identifying historical* outliers.
  5. Assess whether WAR (wins above replacement) adequately accounts for the difficulty level of each player’s defensive season.

*As historical as data can be when they only date back to 2012.

To reiterate, Inside Edge characterizes each ball in play as a particular level of difficulty. It also tracks the frequency with which a player converted that type of play, calculated as a percentage as well. Thus, we can actually verify if a player converted the appropriate number of balls in play within each level of difficulty. For example, a fielder should convert anywhere from 90 percent to 100 percent of the balls in play that Inside Edge classifies as “Routine.” A player who converts fewer than 90 percent of “Routine” balls in play should set off some mental alarm bells.

Inside Edge’s levels of difficulty and their lack of granularity make it difficult on an individual basis to know exactly how many batted balls a player should have converted. Let’s say our favorite center-fielding Kevins, of houses Kiermaier and Pillar, field 20 plays. All 20 of Kiermaier’s opportunities are characterized as having a 95 percent chance of being fielded cleanly. Meanwhile, eight of Pillar’s opportunities are characterized as being perfectly easy to convert (100 percent) while the other dozen are a bit harder, each assigned a 90 percent chance of being fielded cleanly. We can calculate the weighted averages, or expected values, for each:

  • Kevin Kiermaier: (20 * 95%) ÷ 20 = 95%
  • Kevin Pillar: ((8 * 100%) + (12 * 90%)) ÷ 20 = 94%

By Inside Edge’s numbers, the plays Pillar fielded were, on average, slightly more difficult to convert than those that Kiermaier fielded. But because of the aforementioned lack of granularity, all 40 of those batted balls will appear similarly and generally routine to the public.

There’s a way to circumvent these issues, though, and it (finally) gets to the heart of this post. I aggregated all Inside Edge fielding data for each season, from 2012 through 2015, and parsed them by defensive position. Accordingly, we can glean the following:

  1. We can observe not only the actual conversion rates of plays but also the frequencies that plays occur within each level of difficulty.
  2. We can observe fluctuations in the difficulty of fielding plays from year to year.
  3. We can quantify which defensive positions are most difficult. In other words, Inside Edge’s levels of difficulty do not treat each defensive position equally.
  4. And, perhaps most importantly, we can identify which players were subjected to uncharacteristically difficult (or easy) defensive seasons relative to their positional colleagues.

Inside Edge Fielding, by Year (2012-15)

Having addressed the de facto table of contents, let’s look at the frequency that plays occur within each level of difficulty:

FREQUENCY OF BATTED BALLS BY LEVEL OF DIFFICULTY AND YEAR
Year Impossible (0%) Remote (1-10%) Unlikely (10-40%) Even (40-60%) Likely (60-90%) Routine (90-100%)
2012 7.8% 3.2% 3.0% 3.7% 6.4% 74.2%
2013 9.9% 2.4% 3.6% 3.2% 5.8% 75.2%
2014 7.1% 5.6% 4.4% 3.2% 6.2% 73.4%
2015 7.3% 5.3% 3.7% 2.8% 5.6% 75.4%
Total 8.4% 4.1% 3.7% 3.2% 6.0% 74.6%
SOURCE: Inside Edge (via FanGraphs)

Generally, the frequencies at which we observe each level of difficulty remain fairly stable throughout the four-year sample. There’s a peculiar spike in impossible plays accompanied by a dip in remote plays in 2013, but other than that, everything is pretty consistent. Most plays are routine, which makes sense, given offense in baseball is predicated mostly on failure.

Next, the average conversion rate of plays by difficulty:

How an Ace Performance Impacts Reliever Workloads
Bullpenning has its advantages, but it's great when an elite starter eats up a bunch of innings, too.
ACTUAL SUCCESS RATES BY (1) LEVEL OF DIFFICULTY AND (2) YEAR
Year Impossible (0%) Remote (1-10%) Unlikely (10-40%) Even (40-60%) Likely (60-90%) Routine (90-100%)
2012 0.0% 8.0% 27.7% 56.3% 79.5% 97.9%
2013 0.0% 8.2% 30.5% 56.2% 81.7% 98.0%
2014 0.0% 4.6% 27.5% 59.1% 79.7% 97.8%
2015 0.0% 4.5% 29.1% 50.1% 77.9% 97.9%
Total 0.0% 5.8% 28.7% 55.6% 79.7% 97.9%
SOURCE: Inside Edge (via FanGraphs)

It’s good to see that all impossible plays are actually impossible. And it’s good to see elsewhere that the major league-average play-conversion rates within each level of difficulty actually fall within the range of probabilities stated by each bin.

But it’s important to note that the conversion rates above are not centrally located within each range. For example, the average probability of converting a play classified as “Even “40-60%” is actually closer to 55 percent, not the 50/50 coin flip we might assume would develop via a normal distribution. In fact, the frequencies in the previous table indicate that the distribution of play difficulty skews strongly left (with a slight bump in the left tail). Accordingly, the mean will exceed the expected median.

We can have some fun with these data as they pertains to error, as formally ruled by official scorekeepers:

FIELDING SUCCESS RATES BY YEAR
Year Plays Errors Fld% x-Success Sub-60% Errors
2012 108,936 3,002 .972 80.9% -0.6%
2013 109,267 2,745 .975 81.5% -0.1%
2014 110,028 2,912 .974 80.2% -1.0%
2015 108,208 2,821 .974 80.9% -1.1%
Total 436,439 11,480 .974 80.9% -0.7%
SOURCE: Inside Edge (via FanGraphs)

Plays, errors, and fielding percentage (Fld%) are intuitive, but in case you need a refresher: 1 – (Errors ÷ Plays) = Fld%

Expected success rate, or “x-Success,” calculates the overall success rate as a weighted average of each conversion rate by level of difficulty, according to their respective frequencies. It’s the expected value, in nerdy math speak. For the mathematically disinclined, expected value can be calculated as:

EV(x) = Σ [p(xi)*xi]
…where xi is the outcome and p(xi) is the probability that x will occur.

For the sake of this post, p(xi) (probability) represents frequency and xi (outcome) represents difficulty. Subscript i denotes a series of outcomes — in this case, the various difficulty bins previously described. Sigma (Σ) denotes summation. Ultimately, the long-form version of x-Success would be written as:

x-Success = [frequency of Impossible * difficulty of Impossible] + [frequency of Remote * difficulty of Remote] + … + [frequency of Routine * difficulty of Routine]

x-Success is not perfectly precise, given the inherent inexactitude of Inside Edge’s categorization process, but it’s close. It’s comforting to see the numbers so consistent from year to year, but a critical look might undo the numbers all together. If x-Success represents the percentage of total plays converted into outs, then the inverse should equal the league’s batting average on balls in play. Take the difference and, well, you know as well as I do that the major leagues didn’t hit below the Mendoza Line last year.

I asked FanGraphs’ resident data guru Jeff Zimmerman to investigate the discrepancies. Fortunately, he noted that deep within FanGraphs’ database lies a handful of batted balls that were classified as impossible and were not assigned a fielder to be held accountable for it, likely due to the sheer nature of each play’s impossibility. Thus, the total number of plays that turned up for me in my dig through FanGraphs’ leaderboards fell short of the number of plays that actually occurred. Given the major league batting average has hovered around .250, we can assume with non-zero comfort that roughly five percent of all plays are both (1) impossible and (2) not assigned a fielder.

The final column in the table above, “Errors < 60%,” requires a little bit of imagination. Players commit errors–thousands of them every year–but we don’t know the level of difficulty for each error. We can assume, though, that scorekeepers assign errors only to the easiest plays, sequentially. For example, all the routine plays; and when all the routine plays are accounted for, the likely plays; and so on. “Errors < 60%” therefore tries to guess how many errors are credited (debited?) to fielders on plays considered “Even (40-60%)” or harder, as a percentage of plays remaining:

((# of errors) – (# of plays: Routine, Likely)) ÷ (# of plays: Even, Unlikely, Remote, Impossible)

For example, an “Errors < 60%” of 10 percent means 10 percent of all Even, Unlikely, Remote, and Impossible plays, whether for the league or the individual player, are ruled as errors. By this methodology, the zeroes that fill the last column indicate that, generally, no relatively difficult plays are ruled as errors. In other words, the number of easy plays (Routine, Likely) exceed the number of errors committed. It’s a pleasant finding, a confirmation of reasonable expectations about scorers and/or Inside Edge’s standards by which plays are classified. But we’ll see soon that the finding breaks down when the data are further disaggregated.

Inside Edge Fielding, by Position

The following tables depict the play frequency and conversion, respectively, by difficulty and defensive position:

FREQUENCIES BY (1) LEVEL OF DIFFICULTY AND (2) POSITION
Position Impossible (0%) Remote (1-10%) Unlikely (10-40%) Even (40-60%) Likely (60-90%) Routine (90-100%)
P 2.2% 6.0% 4.5% 6.4% 12.7% 68.2%
C 7.9% 16.1% 31.1% 8.0% 5.7% 31.2%
1B 1.2% 3.0% 2.4% 3.4% 7.3% 82.7%
2B 1.8% 3.6% 2.5% 3.0% 5.7% 83.4%
SS 2.2% 4.5% 2.9% 3.1% 6.2% 81.1%
3B 2.1% 4.9% 3.6% 4.6% 9.1% 75.7%
LF 21.2% 2.7% 2.1% 2.1% 4.0% 67.9%
CF 15.7% 2.6% 1.7% 1.5% 3.2% 75.2%
RF 20.5% 2.6% 2.1% 2.0% 3.7% 69.2%
Total 8.4% 4.1% 3.7% 3.2% 6.0% 74.6%
SOURCE: Inside Edge (via FanGraphs)

I highlighted some of the interesting outliers. Outfielders are subject to far more impossible plays than infielders; I imagine these are typically deep shots to the gap and line drives/Texas Leaguers that fall in over the heads of corner infielders down the baselines.

Also — and, perhaps, intuitively — catchers play probably the most difficult position on the field (although that point can be debated endlessly). What caught my attention, though, is that center fielders are subject to more “Routine” and “Likely” plays than the corner outfield spots. Part of that might be selection bias; center fielders are typically highly athletic and, thus, liable to convert more plays than a different type of player (say, the prototypical bat-first left fielders). Maybe the selection bias is enough to account for five percentage points of batted balls, but maybe there also exists reason to believe we are overvaluing the need for defensive prowess in center fielders. Don’t shoot the messenger, it’s just what Inside Edge’s data show. Regardless, it’s a peculiar development.

SUCCESS RATES BY LEVEL OF DIFFICULTY AND POSITION
Position Impossible (0%) Remote (1-10%) Unlikely (10-40%) Even (40-60%) Likely (60-90%) Routine (90-100%)
P 0.0% 10.5% 31.5% 65.2% 83.9% 96.2%
C 0.0% 8.7% 30.1% 57.2% 79.6% 96.6%
1B 0.0% 3.9% 21.9% 55.1% 79.4% 97.5%
2B 0.0% 4.5% 28.1% 57.1% 80.3% 98.1%
SS 0.0% 3.9% 26.4% 48.6% 76.9% 97.2%
3B 0.0% 3.8% 26.4% 54.9% 75.8% 96.2%
LF 0.0% 6.3% 29.8% 55.3% 80.9% 99.1%
CF 0.0% 7.0% 32.1% 55.8% 83.7% 99.4%
RF 0.0% 5.3% 30.2% 53.9% 83.7% 99.1%
Total 0.0% 5.8% 28.7% 55.6% 79.7% 97.9%
SOURCE: Inside Edge (via FanGraphs)

Again, I highlighted any unusual details. Pitchers convert remote plays more than 10 percent of the time and even plays more than 65 percent of the time–conversion rates higher than what’s expected of each difficulty level by definition. I don’t know if there’s a takeaway here, other than some plays that pitchers field are, in theory, classified incorrectly. (For the sake of this exercise, it’s a non-issue.)

FIELDING SUCCESS RATES BY POSITION
Position Fld% x-Success Errors < 60%
P .940 82.5% 7%
C .930 50.0% 8%
1B .970 88.9% 0%
2B .978 88.9% 0%
SS .969 86.0% 0%
3B .959 83.3% 0%
LF .989 72.5% 0%
CF .991 79.1% 0%
RF .988 73.5% 0%
Total .974 80.9% 0%
SOURCE: Inside Edge (via FanGraphs)

Despite the fact that easy plays outnumber errors as a whole for the league, the same does not hold true specifically for pitchers and catchers, who incur a high ratio of errors on plays classified as “Even” or harder. It seems that, per Inside Edge’s standards, pitchers and catchers are not held to the same standards as the other defensive positions. While catchers are subject to difficult plays more frequently than other positions (they should be expected to convert only half of their plays!), their fielding percentages may unfairly punish them to an extent.

The Easiest and Most Difficult Defensive Player-Seasons, by Position

Using the information above, we can determine the most difficult defensive seasons from 2012 through 2015 by comparing each player’s expected success rate to the league average in a given year at a given position. To compare, I calculated Z-scores of expected success rates by player, year and position.

First, the hardest defensive seasons, minimum 100 plays:

MOST DIFFICULT DEFENSIVE SEASONS (2012-15)
Position Player Season Errors “Deserved” Errors Difficulty (Z-Score) Def
C Jarrod Saltalamacchia 2014 15 10 0.6 4.6
1B Yonder Alonso 2014 2 2 0.6 -1.4
2B Skip Schumaker 2013 5 4 0.8 -14.2
SS Tyler Pastornicky 2012 7 7 1.2 -10.3
3B Nick Castellanos 2014 15 18 0.5 -16.3
LF Carlos Gonzalez 2012 4 6 0.7 -12.6
CF Yasiel Puig 2014 2 0 1.1 -5.7
RF George Springer 2014 7 2 0.6 -7.9
SOURCE: Inside Edge (via FanGraphs)
Minimum 100 plays
Def: defensive value, per FanGraphs
“Deserved” errors according to “Errors < 60%” method
Pitchers excluded; none exceeded minimum play threshold

It’s no coincidence that the players above who were subjected to some of the most difficult defensive seasons in recent memory also generated the worst yearly defensive values of their careers. Defensive value is not a rate statistic, but even as a ratio of playing time, all of Schumaker, Pastornicky, Castellanos, Gonzalez, Puig and Springer experienced the worst defensive seasons of their careers. Puig and Springer, two of the game’s young phenoms, have markedly improved their defense since 2014. But such improvements may not be entirely self-manifested — their overall defensive difficulty has eased up, too. Ironically, even though Castellanos’ horrid defense in 2014 is partly vindicated by the sheer difficulty of the plays he encountered, he still butchered a lot of easy plays. Let’s not give him too much credit.

EASIEST DEFENSIVE SEASONS (2012-15)
Position Player Season Errors “Deserved” Errors Difficulty (Z-Score) Def
C Matt Wieters 2013 3 5 -0.7 14.8
1B Steve Pearce 2014 1 0 -0.5 5.4
2B Brian Roberts 2013 1 5 -0.6 1.3
SS Zack Cozart 2015 3 7 -0.8 4.4
3B Eric Chavez 2012 5 8 -0.6 -2.3
LF Brandon Guyer 2015 0 2 -0.9 0.2
CF Chris Young 2013 0 2 -0.6 -1.5
RF Hunter Pence 2015 3 2 -0.6 2.5
SOURCE: Inside Edge (via FanGraphs)
Minimum 100 plays
Def: defensive value, per FanGraphs
“Deserved” errors according to “Errors < 60%” method
Pitchers excluded; none exceeded minimum play threshold

The defensive seasons above aren’t especially impressive, but even Pearce, Roberts, Cozart, Guyer and Pence were subjected to their easiest defensive seasons since 2012.

There’s enough evidence here to inspire further research. I ran a regression that measured the correlation between each player’s difficulty Z-score and his defensive value (Def) relative to playing time (in other words, converted into a rate statistic). I limited the sample to players who fielded at least 100 plays at a particular position. For players who fielded at least 100 plays at more than one position, I kept only the most frequently-played position and omitted the rest. This left me with a sample of roughly 1,100 player-seasons from 2012 through 2015.

The regression yielded a slightly negative correlation coefficient (r = -0.180), indicating the difficulty of a player’s defensive season moves negatively with his defensive value, a major component of his WAR. When controlling for player fixed effects (in which difficulty is specified as the independent variable and defensive value the dependent variable), the regression produces an adjusted r-squared of 0.5616. The coefficient estimate for Z-Score conveys that holding all else constant, a player’s defensive value decreases (or increases) by one run per 100 plate appearances for every standard deviation above (below) the mean that the difficulty of his defensive season ranks.

One could argue, then, that Castellanos was docked eight to nine runs in defensive value simply because of how difficult his defensive season was relative to the rest of the league’s third basemen. Granted, he still had himself an atrocious season at the hot corner. But the eight-or-so runs you give back to him puts him very close to the defensive value he generated in 2015 in roughly the same amount of time–still bad, but not as bad.

Bonus Content! 2016 Defense So Far

Here are 2016’s easiest and most difficult defensive seasons thus far:

MOST DIFFICULT DEFENSIVE SEASONS OF 2016
Position Player Difficulty (Z-Score) Def
 C Dioner Navarro 1.2  -0.1
1B Eric Hosmer 0.9 -12.7
2B Brian Dozier 2.4  -1.6
SS Asdrubal Cabrera 2.3   0.4
3B Matt Duffy 0.9   8.2
LF Melky Cabrera 0.9  -4.8
CF Jackie Bradley Jr. 0.6  -0.3
RF Matt Kemp 1.6 -10.1
SOURCE: Inside Edge (via FanGraphs)
As of June 21, 2016
Minimum 500 innings played at position, except catcher (300 innings)

Note that Navarro, Hosmer and Dozier are all on pace to post the worst defensive seasons of their careers–and that’s saying a lot for the defensively-challenged Hosmer. Meanwhile, Bradley is on pace to post the only negative defensive value of his career, aside from his very brief debut in 2013.

EASIEST DEFENSIVE SEASONS OF 2016
Position Player Difficulty (Z-Score) Def
 C Carlos Perez -1.6  5.5
1B Freddie Freeman -1.0 -4.4
2B Ben Zobrist -1.4  2.9
SS Corey Seager -1.7  7.6
3B Adrian Beltre -1.8  8.9
LF Brett Gardner -0.7 -1.8
CF Mike Trout -1.2  1.4
RF Mookie Betts -0.7  1.7
As of June 21, 2016
Minimum 500 innings played at position, except catcher (300 innings)

Meanwhile, Freeman is roughly on pace to post his second-best defensive season, Betts hasn’t even reached the halfway point of what already is his best season, and Beltre is turning the clock all the way back to 2004.

That’s not to say all outliers behave similarly. Despite the difficulty of their defensive seasons, Duffy is on pace to generate the most defensive value of his career. But he has also played excellently, converting far more of his difficult plays than would normally be expected of him.

Epilogue

I originally sought to better understand Inside Edge’s defensive data on my accord and articulate the context of the data here. But my exploration evolved when anecdotal evidence seemed to turn into something more. En route, I offered here limited but still quantitative evidence that WAR, as we now calculate it, fails to properly account for the difficulty of defensive seasons, at least in the tail ends of the distribution of difficulties. Further analysis may further illuminate my findings or invalidate them completely. Such is life. But I am curious to know how much farther we can take this research.

Into the picture steps Statcast. Baseball fans now have so many new tools at their disposal. Major League Baseball uses cameras and all sorts of fancy technology to measure every player’s reaction time, acceleration, peak velocity, route efficiency, and an endless number of other variables for every single play. Work is already being done to control for a player’s starting position as well as the velocity and trajectory of the batted ball.

The possibilities are endless. It’s almost certain we’ll reach a point where we’ll scoff at how primitive our attempts to quantify fielding used to be. There will be no more guesswork, no more eye test. I don’t know if this is something Inside Edge will be able to publicly offer us. I don’t know if it’s in its best interest to try to compete directly with MLB in this sense.

Regardless, we’re not there yet. We have Daren Willman and Mike Petriello and now Tom Tango to man the helm of the U.S.S. Statcast, but their research, no matter how impressive it is and will be, is still in its fledgling stages. We may not have any combination of reliable, consistent, and public defensive metrics for years, let alone in 2016. Meanwhile, Inside Edge provides plenty of quality data; for a privately owned company collecting and generating its own proprietary data, the fact that we get to see any of it is a treat. And there’s no saying yet, definitively, whether it is better or worse than Statcast.

Until then, let us appreciate the data we currently have at our disposal before marveling at the possibilities the future holds for us. Old dogs can still learn new tricks, after all.

References & Resources


Print This Post
Currently investigating the relationship between pitcher effectiveness and beard density. Biased for a nicely rolled baseball pant. 2015 FSWA award winner (Rookie of the Year) and finalist (Baseball Writer of the Year).
Sort by:   newest | oldest | most voted
jim
Guest

Great stuff, since fielding data is in its early stages. But how do we know the folks at Inside Edge are “wizards?”

The lack of granularity is indeed a big issue.

David Gassko
Guest
David Gassko

Nice article but I’m not sure I agree with the conclusion. What your data may just as well be showing is the bias of IE’s scouts – plays that don’t get made get labeled as being more difficult than those that do (i.e. what looks ‘impossible’ for Asdrubal Cabrera may look ‘even’ for Andrelton Simmons). This is also the issue with BIS data that is used to calculate UZR / DRS but it could be that it is to a smaller degree. Agree that Statcast will be the ultimate arbiter here.

MGL
Guest

I thought the same thing. I think it is probably partially this and partially what Alex is concluding – that DRS and UZR are in fact biased by the difficulty of a player’s chances (and not properly accounted for). In what proportion, I have no idea.

Loren Visser
Guest
Loren Visser

I’m guessing the “high” percentage of Remote plays made by pitchers were on liners through the box that just happen to hit the glove and stick.

Anthony
Guest
Anthony

That’s likely causing some of the difference in the pitching fielding classification. I’d also add that it’s likely that many of the pitcher’s tougher missed plays are being fielded by other players, so there’s a bias against difficult pitcher’s plays being included in the sample, unless they successfully field the ball.

Peter Jensen
Guest
Peter Jensen
Thus, the total number of plays that turned up for me in my dig through FanGraphs’ leaderboards fell short of the number of plays that actually occurred. Given the major league batting average has hovered around .250, we can assume with non-zero comfort that roughly five percent of all plays are both (1) impossible and (2) not assigned a fielder. I am not sure what league batting average is representing in the above statement. A quick query of Retrosheet shows 126000 or 127000 balls in play for each of the years from 2012 to 2014. That would mean 16000 to… Read more »
jdbolick
Guest
jdbolick

I really enjoyed this piece. Kudos on all the work and the insight it revealed.

forestation
Guest
forestation

Thanks for this. I’m still poring through the article, but I see at least one correction:

In a left skewed distribution, the mean is generally less than the median, not greater.

Scott
Guest
Scott

This research screams for a “xDef” stat not unlike xFIP that one could use to normalize (especially in-season) WAR figures.

I really like blended FIP-WAR/RA9-WAR to evaluate pitching seasons. I think a similar treatment to Def folding in the findings from this article about expected impact of hard/easy plays could be useful. It would at least be a step forward from “beware small sample defensive metrics” where we currently reside.

forestation
Guest
forestation
I’m having trouble understanding a couple of variables in your calculation.%) 1. I can’t reproduce the “Sub-60% Errors ” and “Errors < 60%" in your tables, based on the definition you gave in your formula: ((# of errors) – (# of plays: Routine, Likely)) ÷ (# of plays: Even, Unlikely, Remote, Impossible) You show a value of 7% for pitchers, and note that for pitchers, easy plays do NOT outnumber errors. But unless I'm missing something, the data actually shows routine plays at 68.2%, which far exceeds the error % of 6% (based on a fielding % of 94%). Intuitively,… Read more »
Ben Karp
Guest
Ben Karp

I was actually looking to work with IE Fielding Data for my own purposes, and you’ve done some of it for me already with your “SUCCESS RATES BY LEVEL OF DIFFICULTY AND POSITION” table.

THANKS

John W
Guest
This is the type of fielding analysis article I have been hoping to read on FG/THT since the introduction of Inside Edge data. Have there been any other attempts to separate out “degree of difficulty” or fielding “opportunity” from UZR or DRS? This looks like a blind spot in determining true fielding talent with the available metrics, one that seems largely unacknowledged to this point. Maybe we’re all waiting for Statcast metrics to obliterate our current understanding of fielding—I don’t know. Outfield defense is the place where the effect of opportunity seems to make the biggest difference. If you look… Read more »
MGL
Guest
MGL

Alex, good article.

This:

….but maybe there also exists reason to believe we are overvaluing the need for defensive prowess in center fielders.

The reason we have the best fielder at CF has nothing (or almost nothing) to do with the difficulty of the plays, relative to RF and CF. It is simply that there are many more plays (after removing the impossible and routine) in CF.

Patrick Jeter
Guest
Patrick Jeter

If I’m reading the leaderboards correctly… Billy Hamilton has made 6 plays (60% of 10 chances) on balls categorized as remote (1-10%).

All other center fielders in MLB have made 5 such plays combined. One each for Cespedes, Jones, Jay, Pillar, and Cain.

MGL
Guest
MGL

I can say from experience, it is exceedingly difficult to watch a play and then categorize it.

Kyle
Guest
Kyle

So theoretically you could create an adjusted WAR based on the “expected” defensive WAR rather than their actual production? Akin to how pitching WAR uses FIP to say “this is what we expect to happen, given average defense”, our new adjWAR should say “this is what we expected our fielder to do, given an average distribution of balls in play at him.”

That makes sense to me, but someone please correct me if I’m wrong.

wpDiscuz