Project ORB: A superb predictive scouting system!

[kinganu123]

How is the data represented for the neural network? I was thinking about this the other day, and wasn't too sure how you'd account for individual teams without essentially combining the teams' stats into one "super team" (for a lack of a better phrase).

[TomAwezome]

Howdy!! I'm Tom, Wired Wizards' Alpha Team leader (Robot Code), and I as well have been doing the vast majority of the back end for ORB.

Originally Posted by kinganu123

How is the data represented for the neural network? I was thinking about this the other day, and wasn't too sure how you'd account for individual teams without essentially combining the teams' stats into one "super team" (for a lack of a better phrase).

The data from the neural networks is saved into a database into tables for goals, defenses, challenge, and scale; goals has 4 values (autolow, autohigh, teleoplow, teleophigh), defenses has 9 values (0-8, 0:lowbar, 1ortcullis, 2:cdf, 3:moat, 4:ramparts, 5:drawbridge, 6:sallyport, 7:rockwall, 8:roughterrain), scale and challenge each have 1 value for their respective percentage. The goals values are representative of the quantity of goals, defenses representative of 0-2 crossings (as that is all TBA has per match), scale and challenge as mentioned are stored as a value between 0-1, a decimal percent. As each match is a combination of 3 teams per alliance, pulling the data gives you an idea of how they perform, but of course it is just a showing of how the alliance performs! The magic happens as the networks for each team analyze all of their different combinations, and finds the trends inside the dataset that would indicate the team it's training for. Good question!

[kinganu123]

Originally Posted by TomAwezome

Howdy!! I'm Tom, Wired Wizards' Alpha Team leader (Robot Code), and I as well have been doing the vast majority of the back end for ORB.



The data from the neural networks is saved into a database into tables for goals, defenses, challenge, and scale; goals has 4 values (autolow, autohigh, teleoplow, teleophigh), defenses has 9 values (0-8, 0:lowbar, 1ortcullis, 2:cdf, 3:moat, 4:ramparts, 5:drawbridge, 6:sallyport, 7:rockwall, 8:roughterrain), scale and challenge each have 1 value for their respective percentage. The goals values are representative of the quantity of goals, defenses representative of 0-2 crossings (as that is all TBA has per match), scale and challenge as mentioned are stored as a value between 0-1, a decimal percent. As each match is a combination of 3 teams per alliance, pulling the data gives you an idea of how they perform, but of course it is just a showing of how the alliance performs! The magic happens as the networks for each team analyze all of their different combinations, and finds the trends inside the dataset that would indicate the team it's training for. Good question!

Ok, so my followup question is how are you combining the teams into an alliance? Do you use something like CCWM to weight each team into a single alliance?

[TomAwezome]

Originally Posted by kinganu123

Ok, so my followup question is how are you combining the teams into an alliance? Do you use something like CCWM to weight each team into a single alliance?

The only data it uses is data it has pulled then trained. To compute how a combination of teams adds up as an alliance, it, for each defense, finds the max of the three teams' defense scores for that defense, then multiplies that by either 5 for lowbar and 2.5 for the other defenses. The same for goals, but with the proper point values for auto/teleop high/goal. And again, the same for scale and challenge, with the proper score multipliers. It then adds these all together and the result is that alliance's score. To determine the winner, it sees who is larger.