Some factors, although this approach would not be able to separate modeled parameters that have, say, linear relationships with each other. For example, rolling resistance is usually expressed as a friction coefficient times the normal force, which is physically analogous to sliding friction at the hub. Separating different coefficients of friction would not be easy because the various sources of friction affect the physical observations in identical ways. (However, that is not to say that one might be able to estimate a single combined coefficient of friction.) On the other hand, air drag affects the difference in timer measurements differently than wheel friction, so it should be possible to separate those effects. And so on.Stan Pope wrote:...the method could measure factors that are difficult to measure otherwise...
Also, increasing the number of parameters would require more and more observations, probably over multiple runs. Another complication is that some parameters might change measurably before all the observational runs are collected. Least-squares is best at estimating values that are assumed not to change, so a detailed model might need to include additional nuisance terms (such as parameter rates) to account for any time-varying effects, creating even more parameters and significantly complicating the model...