# Success Metric

Basically, the question here is, is it possible to create a statistic to measure the probable success of a robot/alliance?
Details: Theoretically, given a field of any number of robots (around the size of a regional) assign those robots a number indicating the percentage of robots receiving a lower number. Be able to use this measurement to predict which alliance will win a match with reliable accuracy.

Is this possible, how accurate is it, and what is the formula to calculate it?

Would that also factor in FMS issues? What you are asking for, most teams would call scouting. While numbers are useful, intangibles also count for a lot.

At Einstein, & the division matches this year, things where pretty even. Run the whole thing over again, I not sure I would bet on who would be the winner. The quality of competition was pretty amazing.

Is it possible? Sure, you can create a function to rank teams.

How accurate? Depends on your function and what accuracy you consider important. Is your goal to say who wins each match or to say by how much? Who wins can be pretty decent accuracy (well, higher than flipping a coin).

Formula? There is the kicker. Is it based on scoring? On strategy? Past performance?

People have been trying to do this with the OPR system (search how to solve for this) but it is not terribly accurate this year. (Co-Op Bridge is the most notable reason but the limited number of game pieces also factors in)

Ultimately though, humans are driving these machines so there is that factor as well. You also have to understand that an alliance is NOT the sum of its parts but is, in fact, greater or less than that.

favorite piece of wisdom (forget who first said it): “everything is possible, there are simply many things nobody has done yet”

as for accuracy: it depends. by the same logic as used above, it is possible to make a program that perfectly predicts the results down to when each basket is scored, it is just highly unlikely.

The formula would likely be so long and complex it would require a live feed of data to fuel in order to run making it not very practical for FRC use.

While this is technically possible, it is far from accurate to calculate it with a number because some factors are intangible and unable to put a number to. One of these is the strategical factor, and it is really difficult to put a number to that, even as a ranking of the teams, because different types of strategies may be more effective then others at different points.

How would you choose to rank a team that is amazing at strategizing, but has an average to poor level robot, versus a team that has an amazing robot but a very low level strategy system?

The wiimote scouting system 842 uses is very good at predicting the outcome of matches with enough data. Last year at championships our system predicted the correct outcome of every qualifying match on Saturday. This year there were a few wrong ones mostly due to bridges but overall its got insane accuracy. Eliminations though is a different story. This of course also doesn’t account for breakage or field issues.