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Catch Probability and the Mariners Outfield

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Let’s play with the new Statcast defensive metric.

Texas Rangers v Seattle Mariners Photo by Stephen Brashear/Getty Images

With all the new Statcast data publically available, most of the focus has been on new ways to analyze pitching and hitting. Stats like exit velocity, launch angle, and spin rate—measurements that had been hidden or estimated before—get all the headlines. But perhaps the most exciting application of the Statcast data is in player defense. The current advanced defensive metrics in use are useful but flawed in many ways. We have two years of Statcast information and we’re just now scratching the surface of how it can inform our defensive analysis.

Earlier this offseason, MLB.com introduced catch probability. Quite simply, this metric tells us the odds that any given batted ball to the outfield will be caught by a fielder. Using hang time and the required distance to make the play, each opportunity is dropped into five buckets based on difficulty.

  • 0%–25% Catch Probability – 5 Star play – 8% league average catch rate
  • 26%–50% – 4 Star play – 42% caught
  • 51%–75% – 3 Star play – 68% caught
  • 76%–90% – 2 Star play – 84% caught
  • 91%–95% – 1 Star play – 93% caught

Plays with a catch probability higher than 95% are not included in the sample—though it might be interesting to see which outfielders might have missed the most routine catches. There are some limitations to this new metric that should be noted. First, it’s still in its infancy. It doesn’t yet account for direction of an outfielder’s route or the extra difficulty of plays made near the wall. It also treats opportunities within a bucket equally—an opportunity with a 1% catch probability is equal to a 24% opportunity. So when we’re comparing players, know that the data is just an estimate and not every player’s opportunity buckets are the same.

There have been a flurry of articles posted this week digging into this new data. FanGraphs has a few and USS Mariner had one. Those three articles will give you a great overview of this new metric and its potential applications.

Let’s take a look at some Mariner specific data. I’ve compiled the data from the last two years and calculated the number of plays made above or below expectation (based on league average catch rates). In the tables below, I’ve also included the range component of UZR (RngR) and DRS (PM) to compare.

2015:

Player OF Innings +/- Plays Made UZR - RngR DRS - PM
Player OF Innings +/- Plays Made UZR - RngR DRS - PM
Austin Jackson 899 11 8 0
Seth Smith 797 0 7 2
Nelson Cruz 704 -7 -3 -3
Dustin Ackley 499 -2 -4 -1
Mark Trumbo 325 -10 -3 -4

2016:

Player OF Innings +/- Plays Made UZR - RngR DRS - PM
Player OF Innings +/- Plays Made UZR - RngR DRS - PM
Leonys Martin 1275 10 -3 -9
Nori Aoki 891 -5 -4 -1
Seth Smith 730 -5 -5 -5
Franklin Gutierrez 475 0 -4 -4

The biggest thing that stands out is how the advanced defensive metrics have underrated Leonys Martin. This isn’t a surprise if you read any of the articles I linked above. On the other side of the coin, Seth Smith’s positive defensive contributions in 2015 look overrated and help explain why they fell so harshly in 2016.

As it stands, the data is pretty limited. We can get a feel for which outfielders are making more plays than we’d expect, and in real time, we can more accurately assess the difficulty of a catch. But let’s take the available data a step further and try and come up with a fielding profile for a particular player.

Like I mentioned above, directionality isn’t integrated into the calculated catch probabilities yet. But we do have spray charts from Baseball Savant that show us where these opportunities occurred on the field. Below is a chart for Leonys Martin that shows his most difficult catches (<75% catch rate) and the hits he probably should have caught (>25% catch rate):

A brief glance at the chart shows us that Martin excels at making catches in front of him but can struggle with balls hit over his head. Now let’s compare Martin’s chart to one of his peers—say Kevin Kiermaier.

While Martin’s catch radius juts out towards the infield, Kiermaier has incredible lateral range—probably stemming from his legendary pre-pitch positioning. Kiermaier is also converting a higher percentage of his opportunities, leading to fewer hits in his zone. Again, when comparing players like this, it’s important to remember that not every player’s opportunities are equal. But the data does give us a generalized look at what a fielder’s strengths or weaknesses might be.

Next week, I’ll start looking forward to this season by taking a deeper look at Jarrod Dyson and Mitch Haniger with a discussion of the ideal outfield arrangement.