What I have been doing recently is crude, but seems to be working for me. I am more interested in errors and plus/minus plays than range factor. I compare those to either the league average for that position or another player I am considering playing. I consider each error, and net plus play worth about 2/3rds of a run. So if Player A has 10 fewer errors and 10 more net plus plays over Player B, I assume he will prevent 14 more runs over Player B. So for me to consider Player B, he would have to project to 14 more RC's over the same PA's or something like an OPS of .100 - .120 points more than Player A.

I know this is crude and others can probably give me "constructive" suggestions to improve it, but I think it gets me to a better result than most of the teams I play against.

Nice to see someone else looking at this also dwoolery. I'm using +/- and errors vs the average (average comes from WIS averages of all worlds) to determine a Defensive Runs Above Average metric.

However I was using .92 runs per error (from Tango), but after a bit of research, it appears your 2/3rds number is correct.

One suggestion is to subtract the average fielding % from your player's fielding % and then multiply it against the total chances the average fielder would see in a season. Instead of going world-specific, I used this: http://www.whatifsports.com/hbd/Pages/Main/WorldSnapshot.aspx

Doing that will make sure nobody sees an advantage/disadvantage from more attempts.

Have you done any work on offensive WAR and the run values of each event by chance? I'm using what I've seen for the MLB there also and I'm wondering if there's an actual way to determine it in HBD.

Hey spud - thanks for the angle on fielding %, never would have considered that. What do you do then with the result? Or is that how you were getting to the same end I am? And why compare to average as opposed to replacement level?

I've looked at Tango's WAR formulas. I'm just dubious as to how well they would translate to our engine, especially given the position modifier. We may be closer after the last update but I've been waiting to get enough data to take a stab at reviewing it again. Have you found the results worth the trouble to do the inputs?

As for run values, I start by using the original Bill James pythag formula to set my targets at the team level. Then I assume an average of 625 PA's for starters and divvy up the remaining 700 among my bench. I then reduce the targets individually by defensive contribution and estimate OPS that will generally deliver the RC's I want. Obviously this leaves out SB's, but I assume any benefit I get there will help offset poor math or off years from a given player.

I then go over to the pitchers and look for a staff that can give me a composite ERA around .5 - .75 below desired team run average. (I've already factored superior defensive contributions above).

I then cross my fingers.

I've read there are better pythag formulas to use especially at the upper and lower ends of win projections but haven't found one simple enough for everyday/back of the envelope use. If you use something different I'd like to see it - either here or sitemail.

I use average figuring that gives me a proper baseline and the fact you can always find average defense from a position and where replacement level matters is on the offensive end. Since I don't know where to go to figure replacement anyway... it's the best I got.

I was considering just running FIP to figure WAR for pitchers, but was hoping I could put GB/FB rates through some formula out there.

Have you considered just using runs created that WIS provided in their Extended Batting stats?

As for the use, I've got a rating system that I'm trying to correlate to WAR and then be able to figure out how much I'm paying per win vs the rest of the league.

Create your own WAR. Run a regression between OPS or ERA and whatever players ratings are.

Thats what I have done, works well for hitters. Not so well for pitchers.

It's pretty easy for offense, but the trick is defense. You're not going to get anywhere near a good metric for defense using just RF and fielding percentage.

I'm only using RF to determine the average number of attempts each position should expect. If you're using each player's individual RF, that's a mistake.

Fielding percentage and +/- over a full season at a position does give a pretty good idea how much value you can get from each defensive position.

+/- might, especially at the extremes, but I'd argue fielding percentage doesn't give you much at all.

RF is a pretty flawed stat. While Mike is right that a good LF doesn't "steal" balls from an average CF, a BAD LF means more balls get put into play due to the extra hitters getting AB's. This has been mentioned by several above, but is worth clarifying.

I routinely notice that some really awful teams, with bad pitching staffs and bad defense, end up with their middle of the field players high in the RF leaders, not because they're particularly good, but because their pitchers and other fielders are so bad.