Evaluating Defense: Web Gems or Advanced Metrics

Clip art illustration of a Cartoon Tiger with a Missing Tooth

I’m a huge believer in the value of defense in baseball and I’m also someone who believes in advanced statistics in baseball. You might already know that if you’re a regular reader. Some of the typical advanced stats regarding defense are Defensive Runs Saved (DRS), Ultimate Zone Rating (UZR), and UZR’s close cousin UZR/150 which scales that number based on a full season of games.

Critics and proponents alike will tell you these numbers aren’t perfect and do not always predict true skill in small samples, but they are reasonably good compared to any other defensive statistic we have and they are created by people watching baseball, not a computer algorithm. So they’re the best measure of defense we have even if there are flaws.

But another measure of defense is the number of spectacular, eye-popping plays. This measure is called the Web Gem and is brought to you by the people at Baseball Tonight.

Mark Simon, an ESPN Stats and Info researcher, often posts Web Gem data on Twitter and I’ve been wondering about Web Gems and advanced stats for a while. Today I stopped wondering and started doing. Here’s Simon’s most recent tweet regarding team level Web Gems:

Now if you’re a real scientist who knows about probability and stuff, you know there are a couple flaws in what I am about to do. Let me get them out of the way quickly:

  1. Web Gems are conditional what happens on a given day, the 6th best play (not a gem) on Monday might have been 1st on Tuesday (a gem) but due to the random distribution of gems, it doesn’t qualify even though it should.
  2. Terrible defensive plays don’t count against you in Gems, but do in DRS/UZR
  3. Team numbers aren’t best, but it’s all I have. A team’s defensive quality can vary, so if one play accumulates all of your gems they can still only account for a fraction of your DRS/UZR

So recognize that these are issues, but also ignore them for now because this is supposed to be fun and merely to satisfy my curiosity.

How well do Web Gems predict defense? Does a small number of great plays predict overall defensive value? The short answer, no. Not at all. Here is Web Gems plotted against DRS, UZR, and UZR/150.

drs uzr uzr150

You may notice the line slops upward in each graph, meaning that as Web Gems increase, so too do the various metrics, but a positive slop doesn’t mean it’s a real effect. That’s just the line of best fit. In reality, these lines are not statistically significant. In fact, they are almost as insignificant as something can be (I know that’s bad statistical theory, it’s poetic license).

Here are the slope coefficients, standard errors, and adjusted r squares:

WEB GEMS DRS UZR UZR/150
Parameter Estimate 1.70 0.86 0.43
Standard Error 1.30 0.80 0.27
Adjusted R squared 0.03 0.00 0.05

As you can see, the adjusted r squares for each of these are remarkable tiny. In layman’s terms, what you are seeing here is this. More Web Gems, on average, mean higher DRS/UZR, but this is almost surely due to random chance. Web Gems also explain less than 10% of the overall variation in the defensive stats.

Basically, the takeaway here is that overall team defense is not related to a team’s overall number of Web Gems. That might not interest you, but I was curious. I’d like to do it with every player in the league, but I don’t have complete individual Web Gem data and I think the very high number of zeroes would probably make it a giant mess. I’m not sure.

But my curiosity has been satisfied and I feel better knowing that the ability to make ridiculous plays is not strongly related to the overall ability prevent runs.

Advertisement

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: