I used the Lahman database for all teams 1961-2002, except 1981 and 1994 for obvious reasons. I tested 10 different
RC methods, with the restricition that they use only AB, H, D, T, HR, W, SB, and CS, or stats that can be derived from those.
This was for three reasons: one, I personally am not particularly interested in including SH, SF, DP, etc. in RC methods if
I am not going to use them on a team; two, I am lazy and that data is not available and I didn't feel like compiling it; three,
some of the methods don't have published versions that include all of the categories. As it is, each method is on a
fair playing field, as all of them include all of the categories allowed in this test. Here are the formulas I tested:

**RC: **Bill James, (H+W-CS)*(TB+.55SB)/(AB+W)

**BR: **Pete Palmer, .47S+.78D+1.09T+1.4HR+.33W+.3SB-.6CS-.090(AB-H)

.090 was the proper absolute out value for the teams tested

**ERP:** originally Paul Johnson, version used in "Linear Weights" article on this site

**XR: **Jim Furtado, .5S+.72D+1.04T+1.44HR+.34W+.18SB-.32CS-.096(AB-H)

**EQR: **Clay Davenport, as explained in "Equivalent Runs" article on this site

**EQRme**: my modification of EQR, using 1.9 and -.9, explained in same article

For both EQR, the LgRAW for the sample was .732 and the LgR/PA was .117--these were held constant

**BsR: **David Smyth, version used published in "Base Runs" article on this site

**UW: **Phil Birnbaum, .46S+.8D+1.02T+1.4HR+.33W+.3SB-.5CS-(.687BA-1.188BA^2+.152ISO^2-1.288(WAB)(BA)-.049(BA)(ISO)+.271(BA)(ISO)(WAB)+.459WAB-.552WAB^2-.018)*(AB-H)

where WAB = W/AB

**AR: **based on Mike Gimbel concept, explained in "Appraised Runs" article on this site

**Reg: **multiple regression equation for the teams in the sample, .509S+.674D+1.167T+1.487HR+.335W+.211SB-.262CS-.0993(AB-H)

Earlier I said that all methods were on a level playing field. This is not exactly true. EQR and BR both take
into account the actual runs scored data for the sample, but only to establish constants. BSR's B component should have
this advantage too, but I chose not to so that the scales would not be tipped in favor of BsR, since the whole point is to
demonstrate BsR's accuracy. Also remember that the BsR equation I used is probably not the most accurate that you could
design, it is one that I have used for a couple years now and am familiar with. Obviously the Regression equation
has a gigantic advantage.

Anyway, what are the RMSEs for each method?

**Reg-------22.56**

**XR--------22.77**

**BsR-------22.93**

**AR--------23.08**

**EQRme--23.12**

**ERP-------23.15**

**BR--------23.29**

**UW-------23.34**

**EQR------23.74**

**RC--------25.44**

Again, you should not use these figures as the absolute truth, because there are many other important factors to consider
when choosing a run estimator. But the important things to recognize IMO are:

- all of the legitamite published formulas have very similar accuracy with real major league teams' seasonal data
- if accuracy on team seasonal data is your only concern, throw everything away and run a regression(the reluctence of people
who claim to be totally concerned about seasonal accuracy to do this IMO displays that they aren't really as stuck on seasonal
team accuracy as they claim to be)
- RC is way behind the other methods, although I think if it included W in the B factor as the Tech versions do it would
be right in the midst of the pack
- BsR is just as accurate with actual team seasonal data as the other run estimators

Anyway, the spreadsheet is available below, and you can plug in other methods and see how they do. But here is the
evidence; let the myths die.