First: You can tinker with them, and other things have been developed, linear weights. Then again, EqR can be broken down into linear weights if you want to. Linear weights basically represent that each event has a specific run value. Off the top of my head it's something like this: R = .5*1B + .8*2B + 3B + 1.4*HR +.33*BB -.25 OUT. Multiplying these three by 4 results in: 4RC = 1.3 BB + 2 1B + 3.2 2B + 4 3B + 5.6 HR - Out....which is very very close to Raw without the outs. Second: Basically something like OBP + SLG. If you ignore the denominator and add just the top half of the fractions of OPS you get BB + HBP + 2*1B + 3*2B + 4*3B + 5*HR. OPS is actually fit better by 1.6*OBP+SLG, but they just put all of the 1.6ish term into BB and HBP. They then added more bases to the top. So it's not all that different than the weights for OPS. Clay Davenport came up with them by himself, I believe. Thanks. Getting back to the linear weights you mentioned, I can understand the logic of choosing the weights applied to each event, but I don't understand why these weights have not been further statistically analyzed to see how accurate they really correlate to runs scored. What I mean is, is it God's honest truth that a HR is 1.4 times as valuable as a 3B? Could a study prove its closer to 1.47? They seem like a pretty logical point to start out at, but judging by the values it seems they haven't been studied in depth to find the optimum weights.