He explains how to get to that equation, but in my opinion doesn’t really explain any of the whys about the intricacies of the equation. Still an interesting read though. I also don’t have the answer, but I won’t let that stop me from speculating based entirely on irrelevant details.

MOVM = LN(ABS(PD)+1) * (2.2/((ELOW-ELOL)*.001+2.2))

I suspect that log_e was chosen over log_n because it doesn’t make too much of a difference. The curve can be scaled by the second portion of the equation to get the appropriate response.

The form of the second portion allows for essentially two different responses, one for underdog victory and one for favorite victory. It’s not a linear function, so underdog victories result in a larger MOVM than favorite victories of the same point differential. This is most significant for abs(ELOW-ELOL) > 500. For -500>ELOW-ELOL>500, it’s mostly linear.

The 0.001 and 2.2 aren’t *really* two different constants, they’re interdependent (let’s call them a and b, a=0.001 and b=2.2). If you take a=0.01 and b=22 you get the same result. The ratio of the two, a/b, determines the non-linearity of the second portion of the MOVM equation, as discussed previously. If you increase a and keep b constant, the function becomes more non-linear, increasing the difference between the reward for underdog victories and favorite victories.

The two also limit the scope of the function. In this case, for (ELOW-ELOL) <= -2200, it breaks. The more non-linear you’d like the function, the smaller of an elo difference it can allow (only for underdog victories of course, Rocky can lose all he wants but as soon as he beats Apollo you have issues).

So my post doesn’t answer any of your questions directly, but hopefully it helps with deciding what values would be acceptable for FRC. I suspect log_e is appropriate for pretty much any competition, and the a/b constant is what would actually be tuned. When tuning a/b it’s probably necessary to pay attention to the highest conceivable elo difference for an underdog win. Since you sum individual ELOs to achieve an alliance ELO, I suspect it’s not unreasonable that you could break this equation given a large upset, but you can probably look at historical data to make sure.

If a and b aren’t guess+check, I would think that they depend on the largest conceivable underdog win ELO difference and the prevalence of underdog victories. If underdog victories are common I would expect a more linear fit, but if they are uncommon I would expect a more non-linear fit. As to how to translate that into a number, , good luck!