I know this subject was brought up previous in the other PMR discussions, but looking at the Difference column, most teams are positive. What I'd like to see is some numbers metric of above/below average. That's one of the reasons why numbers like Rate and OPS+ are so easy to use. Just looking at the number tells you how the player did compared to average.

With 25 of 30 teams in the basic model outperforming their predicted DER, and a 26th team being virtually identical to the model, it seems to me that there's either a problem in the model, or something in the BIP distribution changed between 2004 and 2005. The model should not be underpredicting DER for that many teams.

I've re-done the numbers for my own use with a fudge factor. If anyone wants them, drop me an e-mail.

David

Oh, and David, this is great stuff! Keep it coming.

Mike, the model is based on four years of data. My guess is if I ran all four team seasons, you'd see an even number above and below 0.

David, interesting data. One question and observation. How do you interpret the DER? If the Phillies have a DER of .703, does that mean there's a 70 percent chance that a ball put in play will turn into an out? And I noticed that there's not a lot of variation in the data. It seems like all the team are in the .68 to .70 range. Is there any concrete way to describe how a team with a DER of .700 differs from a team with a DER of .680?

Steve, your interpretation of DER is correct. These are fieldable balls in play, so out of the park home runs are removed from the equation.

It may help you get a handle on DER by thinking of it as 1.0-DER. That would be approximately batting average vs. the defense. So the difference between a .700 DER and a .680 DER is the difference between a .300 hitter and a .320 hitter.

David, you rock! Love this stuff.

Besides the Red Sox drop w/ no park variable, the Nationals rise is also quite drastic, from #12 to #27. That big stadium must let them catch many fly balls that would be HR's elsewhere.

Really great information.

Another way to think about what a .703 DER means is that the Phillies' fielders made 129 more outs than expected. That's about 100 runs (depending on distribution btwn IF and OF), or a reduction in team RA/G of 0.62. In fact, making the "difference" column in the table outs (or runs) rather than DER would probably be more helpful.

"Unlike 2004, this was a very good defensive year."
With about 130,000 BIP each year, I'm skeptical that there are really 'bad' or 'good' years. The THT site shows overall DER at .695 in 2004 and .695 in 2005. If that's right, it's hard to conclude this was a better fielding year. Or if DER was higher this year, I would look to factors like weather, park changes, strike zone changes, etc. before concluding the overall level of fielding changed.

David is taking the perspective, in his presentation and perhaps in his opinion, the following:

given that the "true mean" league-average is zero for the last 4 years, any deviation within those 4 years at the league-level is a true difference in skill.

There are a multitude of reasons why the "sample" mean is not zero for each of the last 4 years (within the 4-yr population). Change in personnel or quality of fielders is one, but that is probably way down the list. External factors, like the climate, or ball (or list your favorite) can be a cause.

It is much more beneficial, and probably more accurate, to list the annual "true mean" of each sample year as zero. It makes in-season comparisons much easier, and easier to grasp.

Year-to-year comparisons are probably more accurate as well (rather than making it seem like 80% of the fielders, improved, or whatever the number is).

That said, I love this!

There are two things being tracked: real DER and pred DER. Real DER changes very little -- approx .691 in 2004 and .694 in 2005 according to David's data. If 3 plays out of 1,000 makes '05 a "better fielding year," well OK (but the difference is w/in the MOE). But the predicted DER goes from .698 last year to .681 this year. That doesn't make sense to me. The distribution of 125,000 BIP can't possibly be that different.

Guy,

You raise an excellent point. I'll look into this. One problem is that if you look at the table from last year, there's a different underlying model, in that it's based on three years (2002-2004) of data. I don't know if that's enough to cause a difference, but I'm going to run 2004 against the four year model and see.