Inside Edge Fielding

Inside Edge Fielding is a set of statistics that measures how often a player has made a defense play of a particular difficulty. There are six categories of plays based on how often a fielder at that position would make the play in question.

Defensive value can be difficult to measure, but Inside Edge Fielding stats offer an additional way to evaluate what is happening on the field.

Calculation:

Inside Edge Fielding stats are, like the name indicates, tracked by Inside Edge and their particular methodology is not publicly information. In a basic sense, each play is rated based on how often a player at that position has made a very similar play. Those plays are grouped into six bins:

  • Impossible (0%)
  • Remote (1-10%)
  • Unlikely (10-40%)
  • About Even (40-60%)
  • Likely (60-90%)
  • Almost Certain / Certain (90-100%)

At FanGraphs, we track the number of balls hit to a player at each difficulty and the percentage of those plays they made in the given season. So for example, if a player has been hit 40 “Likely” balls and has made 35 plays on those 40, they would have a “Likely” rate of 87.5%. This data is available dating back to 2012. These are also the stats that populate the Spray Charts for fielders, so you can view them visually as well.

Why Inside Edge Fielding:

The modern way we measure defense is based on the difficulty of the play and the run value of the batted ball. DRS and UZR are two stats that measure defensive value based on those variables. Inside Edge Fielding stats break out the data based on the difficulty of the play, but do not consider the run values. As a result, the stats provide added value to you in two ways. First, Inside Edge operates separately from Baseball Info Solutions (the company that tracks the other two metrics), which allows you to perform a sanity test of either stat because they are not collected by the same people. Second, Inside Edge gives you an idea of what type of plays a given player succeeds on or struggles with.

If a fielder makes a bunch of 1-10% plays, it’s probably a safe bet that he has some exceptional raw ability. If a player misses a bunch of Almost Certain plays, he may be a bad defender, but he might also have been unlucky. The key is that all defensive misplays are not the same, when some stats liked defensive efficiency and RZR treat them all alike. Inside Edge Fielding stats allow you to parse some of this out.

How To Use Inside Edge Fielding: 

Inside Edge Fielding stats are useful in the sense that they allow you to get a sense of the type of fielder you’re dealing with. Some fielders are good at making the easy plays but don’t have range. Some players have tremendous range but fail to execute easy plays. Inside Edge lets you track that more easily than the high level stats which only show you run values.

However, you have to be careful because except for the 90-100% category, the sample sizes are very small. Making four 10-40% plays in four changes might look impressive if you see the 100% value, but it’s likely that four plays is not a large enough sample to indicate true talent. Succeeding on a few tough plays is possible for bad fielders and missing a few easy plays is possible for good fielders. Also, there is measurement error involved in these stats, and you can’t be sure that a 10-40% play is truly a 10-40% play with 100% certainty. As a result, there are margins of error around all of these numbers.

Use these numbers as a guide to understand the players’ seasons, but be careful when you are making inferences about their abilities because the data is imperfect and the samples are small. Making a 1-10% play doesn’t make you a good fielder, it means you made at least one difficult play.

Things To Remember:

● Batted balls are tracked based on how often they are turned into outs.

● Sample sizes are usually pretty small for the difficult plays.

● These stats are better at telling you about the past than the future, but can be informative about true talent if used properly.

Links To Further Reading:

Inside Edge Fielding Data – FanGraphs