Quick UZR/PMR Comparison

by Dan Turkenkopf on Dec 8, 2008 8:00 AM EST in Positional Reviews

Now that FanGraphs is publishing MGL's UZR, I wanted to see how the 2008 results compared to David Pinto's PMR (which I converted to runs here).

I ran a quick correlation for each position containing the players qualified according to PMR - those who were on the field for at least 1000 balls in play.

Position	Correlation
Pitcher	NA
Catcher	NA
First Baseman	0.71501645
Second Baseman	0.58129407
Third Baseman	0.62672569
Shortstop	0.68798239
Left Fielder	0.74839581
Center Fielder	0.52849967
Right Fielder	0.80255003

There were definitely some issues in center field, and to a lesser extent 2B. The two systems had a high level of agreement in the corner OF positions.

The 10 biggest disagreements were:

Player	Position	PMR	UZR	Diff
Akinori Iwamura	2B	-17.25	0.6	17.85
Pedro Feliz	3B	-10.19	7.2	17.39
Troy Glaus	3B	-13.85	3.3	17.15
Edgar Renteria	SS	-16.14	0.9	17.04
Jose Reyes	SS	-18.21	-1.5	16.71
Brad Hawpe	RF	-21.64	-37.2	15.56
Melvin Mora	3B	-17.81	-3	14.81
Torii Hunter	CF	4.81	-9.8	14.61
Dan Uggla	2B	14.85	0.3	14.55
Bobby Abreu	RF	-11.45	-25.2	13.75

Both PMR and UZR were calculated using the Baseball Info Solutions (BIS) data set this season. I wonder if David or MGL might be able to give some ideas as to where the differences might come from.

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Comments

At least we know Abreu and Hawpe really suck.

by R.J. Anderson on Dec 8, 2008 10:37 AM EST reply actions

When I saw Abreu at -25 runs by combined ZR, I couldn't wrap my ahead around how that was even possible.

But now we see -37 runs from Hawpe. Really? That’s like -45 plays, or one every 3.5 games. Given that a corner outfielder might see, what, three batted balls per game, that’s like 1/10 opportunities that Hawpe screws up that an average fielder doesn’t. And considering, what, at least 2/3 of those opportunities are “routine”…

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on Dec 8, 2008 10:43 AM EST reply actions

UZR and PMR

I don’t remember all of DP’s methodology or if he has changed it much since its inception, but I vaguely recall there being quite a few differences. Any time you have two different engines processing the same data, you are bound to have some large differences, especially in small (say, one year) samples.

Sky, above, that does not mean that Hawpe is screwing up 1/10 of his opps, or some such thing. A large number, plus or minus, is extremely likely to be a fluke. If you see someone with -37, it likely means that he is a bad fielder (maybe -18 if that -37 is in 150 games or so) and that the other -19 runs are “flukey.” “Flukey” could be hard balls to catch that “appeared” easy according to the data, 5 or 6 balls (or 10 or whatever) that 50% of all fielders would have gotten to, but Hawpe happened to have just missed, balls that an adjacent fielder would have or should have gotten to but didn’t (in UZR when any player catches a ball, no other players get docked, and when no one catches a ball every player whose position EVER caught a similar ball gets docked some amount). Etc.

When you see a number for a fielder, NEVER think in terms of that number and “how it could be.” Always think in terms of the proper regressed number and how THAT could be. That goes for all sample metrics – offensive, defensive, etc.

by mgl on Dec 10, 2008 3:18 AM EST reply actions

I'm with you, MGL, don't worry.

Wondering how -37 “could be” could be useful just to show how unlikely it actually is “to be”. You know, in case someone actually thinks Hawpe was THAT bad.

Beyond the Boxscore // Calling BJ Upton lazy is lazy.

by Sky Kalkman on Dec 10, 2008 5:20 PM EST up reply actions