I assigned a score to each target-team pairing based on average profit gained/lost by that team type attempting similar items. Specifically, I summed a site score, a classification score and nine flag scores where each sub-score is the average profit of attempts by that team type for targets sharing that attribute. I excluded team-attribute pairings that are rare in the dataset (legal teams targeting humanoids, infiltration teams targeting locations), which I guess would predictably fail to acquire for obvious reasons. I also noted that paramilitary teams are extraordinarily bad at acquiring virtual targets even though they attempt fairly often. I would have excluded these if it was relevant.
I want to find the overall team assignment that maximizes/minimizes the sum of the scores for each target, but there are 24.8 trillion possible team assignments. To make the problem tractable, I first reduced the target set to “viable” targets, which are the nine top scorers for each team type. Some of these overlap, so this reduced the target list from 60 to just 13 minimal targets and 17 maximal targets (1.2 million and 70.8 million possible assignments respectively). I iterated over these reduced target lists to find the best/worst overall assignments.
My method:
I assigned a score to each target-team pairing based on average profit gained/lost by that team type attempting similar items. Specifically, I summed a site score, a classification score and nine flag scores where each sub-score is the average profit of attempts by that team type for targets sharing that attribute. I excluded team-attribute pairings that are rare in the dataset (legal teams targeting humanoids, infiltration teams targeting locations), which I guess would predictably fail to acquire for obvious reasons. I also noted that paramilitary teams are extraordinarily bad at acquiring virtual targets even though they attempt fairly often. I would have excluded these if it was relevant.
I want to find the overall team assignment that maximizes/minimizes the sum of the scores for each target, but there are 24.8 trillion possible team assignments. To make the problem tractable, I first reduced the target set to “viable” targets, which are the nine top scorers for each team type. Some of these overlap, so this reduced the target list from 60 to just 13 minimal targets and 17 maximal targets (1.2 million and 70.8 million possible assignments respectively). I iterated over these reduced target lists to find the best/worst overall assignments.
Maximal assignment:
Infiltration: (SCP-1466, SCP-3339, SCP-5117)
Legal: (SCP-1282, SCP-3850, SCP-5136)
Paramilitary: (SCP-3212, SCP-3936, SCP-4834)
Minimal assignment:
Infiltration: (SCP-2116, SCP-2178, SCP-3279)
Legal: (SCP-2626, SCP-3781, SCP-4036)
Paramilitary: (SCP-1838, SCP-3577, SCP-4654)