

I'd say it is somewhat addicting. But I think that is a good thing. It proves that knowledge can be addicting. How cool is that? Now, if we could just get everyone addicted to knowledge, just think of what we can accomplish.:D  Molten [more] 



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#16




Re: Offense/Defense rankings for 1043 teams
Here is a link for some individual robot scoring at West Michigan and Chesapeake.
Individual Scoring Thread 
#17




Re: Offense/Defense rankings for 1043 teams
Here is the way I determine whether a team is carried or is carrying the alliance.
This should give you a pretty normal distribution of teams centered around 0. If the team is carried, they should get a negative score based on the other teams in the alliance average scores. The team in question's position on the normal curve shows how well they did.. the farther to the right the better. The standard deviation and sample density tell you how hard the event actually was. 
#18




Re: Offense/Defense rankings for 1043 teams
Here's the actual excel sheet. You'll have to disable macro security if you want to run the calculator. To only list teams going to atlanta, append an 'a' to whatever you enter into the input box at the beginning
http://www.student.cs.uwaterloo.ca/~...eamRanking.xls Quote:

#19




Re: Offense/Defense rankings for 1043 teams
Let's do a little linear algebra.
Start with the assumption that each robot, on average, will contribute a certain number of points to their alliance's total in the matches that they play. The goal is to try to find that number for each robot using just the data from match scores. Call the number of points on average a team contributes to their alliance's score their "offensive power rating". Let the vector of offensive power ratings be p. p is what we are trying to find. Let s be the vector of the total points for each team at the competiton (the sum of the points scored by their alliance in their matches). We can also create an nxn matrix (where n teams are competing in the regional) that represents the schedule for the regional. Call this matrix M. Define M as follows: M(ji)=M(ij)=the number of matches team i plays on an alliance with team j. Also let M(ii) be the number of matches that the ith team plays in total. Fixing i=k and summing over j, M(k1)p(1)+M(k2)p(2)+...+M(kn)p(n) should equal s(k) be the number of points the kth team's alliances score at the competition. In other words, Mp=s. Can we can solve for p here? Yes, because M will always be nonsingular (unless one team plays all of its matches with another team). I broke everything down regionalbyregional, because many teams who perform poorly at one regional do very well in the next. And the winners are: Team Offensive Power Rating Regional 1114 62.31 Waterloo 25 61.74 Las Vegas 469 51.31 Detroit 233 50.55 Boston 25 50.21 Trenton 1126 48.38 FingerLakes 1114 47.04 GLR 175 46.48 Annapolis 987 45.52 Arizona 111 45.22 Wisconsin Mean is 10.14, Median is 8.46. I did not include elimination rounds. Nor did I include GTR, SBPLI, UTC, or BAE (missing or no match data). I will share all the offensive ratings with anyone who asks. Defensive ratings can be done in a similar fashion but your results will not really tell you who the good defensive robots are (i.e. the defensive ratings will be useless). I would appreciate results for the regionals I am missing. Last edited by sw293 : 04062006 at 04:42 PM. 
#20




Re: Offense/Defense rankings for 1043 teams
Quote:

#21




Re: Offense/Defense rankings for 1043 teams
Sorry, but I am having a hard time understanding (maybe it's just because it's 2 Am here)
Our team is in first place in the "top defence finals only" Is this good or bad? what does it mean? and what is the for/against? David 
#22




Re: Offense/Defense rankings for 1043 teams
Quote:
for: avg. points your bot scored per match, against: points on average scored against your bot per match the finals: i think it means using alliance scores from elimination matches only its a good thing, 1577 is considered the best defensive bot 
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