Towards an Inside Edge Runs Saved

by
September 25, 2015

There is a treasure trove of data sitting on FanGraphs which to my (limited) knowledge is little used. These data are the Inside Edge fielding stats. We have UZR and DRS, but no IERS (Inside Edge Runs Saved), despite the general availability of the data.

UZR essentially guesses, based on batted-ball profile, what the probability is that a play will be made, which given the lack of true batted-ball data will take time to stabilize. Inside Edge has the benefit of stacking each play in a probability bucket. Here is a list of the probabilities by POS, Probability Bucket and Year that we have IE data:

I then took each player’s stats and based on their position and year, computed the expected number of plays they should have made and compared that to the actual number of plays they had made. In other words, a RF in 2014 should make 86% of his plays in the 60-90% range, so if he had 100 plays there and made 92, he made 6 extra plays. Here’s what 2015 Top 30 looks like in that lens:

Note that IE seems to like Arenado and Longoria a lot more than DRS, however the list is pretty consistent with DRS, esp with Simmons and Hechavarria in the 2/3 spots. I didn’t control for team bias in the results, so it may be favouring certain teams (Jays players seem to be getting a large boost, see Martin, Revere, Pillar, Tulo and Donaldson all on the top 30). Go Jays Go! Yankees Suck!

The next step is a little less mathematical, in that I attempt to ascribe an average run saved based on position. Based on linear weights, a single is worth roughly .5, a double roughly .75 and a triple roughly 1. Thus, a catcher and pitcher can save at most .5 runs each play they make. Second basemen and shortstops will save .5 on most plays, but will get a bump when they convert a double play. A third baseman will prevent some doubles as well as convert some double plays. Outfielders will be preventing some mix of singles, doubles and triples (and the occasional home run). So, based just on my gut feelings on the matter, I ascribed the following run values to each position:

C/P: .5 Runs Saved

1B/2B/SS: .6 Runs Saved

3B: .65 Runs Saved

OF: .75 Runs Saved

Based on these values (estimated runs saved), these are the top fielders (catchers excluded) from 2012-2015 and 2015, respectively:

And the Worst (2012-2015 followed by 2015):

 





 

Eli Ben-Porat is a Senior Manager of Reporting & Analytics for Rogers Communications. The views and opinions expressed herein are his own. He builds data visualizations in Tableau, and builds baseball data in Rust. Follow him on Twitter @EliBenPorat, however you may be subjected to (polite) Canadian politics.

5 Comments
Oldest
Newest Most Voted
Inline Feedbacks
View all comments
Matt P
8 years ago

I did something like what you’re doing way back in 2014. As you can expect from your CF results, I found that Inside Edge data isn’t as good as UZR.

What you can do is use the Inside Edge data to determine a defensive runs metric for a given team using all of its players. Then, you can run a regression analysis trying to model team wins using IERS rather than UZR and seeing your results. They’ll be unfortunate.

0
Eli Ben-Poratmember
8 years ago
Reply to  Matt P

That’s a good idea actually. Use the team UZR to adjust the player’s IERS, which when blended should give a non-biased, quickly stabilizing defensive metric.

0
Kinshuk Rajanmember
8 years ago

Interesting, I hadn’t checked out the Inside Edge data before. Any thoughts as to why infielders seem to convert fewer of the 60-90% balls than outfielders?

0
Eli Ben-Poratmember
8 years ago
Reply to  Kinshuk Rajan

Odds are there is systemic bias towards certain positions. I.e. a human has to make a judgement call as to which bucket each play falls into, which is a difficult thing to do. Since shortstops are held to a higher standard, the probability buckets need to be shifted back a little to the harder side (hence the benchmarks being POS and Season). In other words a play that a SS will make 90% of the time a 2B will make 85% of the time, however this is difficult to measure precisely, hence the noise in the data.

0
KK-Swizzle
8 years ago

Do we know how much the raters take positioning into account? It seems like it would be inherently less than the more computer-dependent defensive metrics, in which case we could compare the two to get an idea how of much of a player’s defensive come from physical/athletic ability, and how much from mental/analytical ability (though the latter may have more to do with pitchers and teams than the defensive player himself). Really interesting concept and potential applications here, nice work!

0