Here is another metric I am thinking of pursuing this year in addition to the metric described above. While the above metrics looks at the number of "scored" gears for each match, I would like to attempt to track the number of gears placed, regardless of whether they are scored or not. I really want to find the metric that has the most predictive power for future matches, even if it is not defined as clearly as the above metric.
My plan is to use the same general structure as the above methods, but to use an alternative number of gears for each possibility. To do this, I will wait until week 1 events are completed, and then try a bunch of different combinations of gear values for each combination of auto rotors and engaged rotors, and use the mapping which has the most predictive power. For example, I might find that the following mapping has the most predictive power for future matches:
Code:
function matchTotalGears(match):
if match.rotor4Engaged: return 13.6
if match.rotor3Engaged: return 8.1
if match.rotor2Auto: return 4.4
if match.rotor2Engaged: return 3.3
if match.rotor1Auto: return 0.8
if match.rotor1Engaged: return 0.2
else: return -1.5
function matchAutoGears(match):
if match.rotor2Auto: return 2.8
if match.rotor1Auto: return 1.7
else: return -0.1
function matchTeleopGears(match):
return matchTotalGears(match) - matchAutoGears(match)
I anticipate that I would primarily use this metric for any predictive models I make. I am also planning to ask teams for week 1 scouting data so that I can see if this metric correlates better with scouted gear performance than the "scored gear contribution." I imagine it will.
I am thinking of calling this the "estimated placed gears."