#1 Scandal (14-2)

avg: 2606.7  •  sd: 71.91  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
37 Agency** Win 15-1 1749.08 Ignored Jul 15th TCT Pro Elite Challenge East 2023
29 Pop** Win 15-1 2002.13 Ignored Jul 15th TCT Pro Elite Challenge East 2023
22 Siege** Win 15-4 2141.48 Ignored Jul 15th TCT Pro Elite Challenge East 2023
12 Nemesis** Win 15-6 2531.46 Ignored Jul 16th TCT Pro Elite Challenge East 2023
14 Parcha** Win 15-4 2425.67 Ignored Jul 16th TCT Pro Elite Challenge East 2023
2 Phoenix Win 11-6 3026.44 Jul 16th TCT Pro Elite Challenge East 2023
3 Fury Win 14-13 2579.65 Aug 4th 2023 US Open Club Championships ICC
10 Traffic Win 15-8 2665.87 Aug 4th 2023 US Open Club Championships ICC
3 Fury Win 15-13 2668.83 Aug 6th 2023 US Open Club Championships ICC
5 Brute Squad Win 15-7 2902.83 Sep 2nd TCT Pro Championships 2023
2 Phoenix Loss 11-12 2354.74 Sep 2nd TCT Pro Championships 2023
10 Traffic Win 15-11 2482.22 Sep 2nd TCT Pro Championships 2023
8 6ixers Win 15-6 2705.93 Sep 3rd TCT Pro Championships 2023
3 Fury Win 15-14 2579.65 Sep 3rd TCT Pro Championships 2023
14 Parcha** Win 15-5 2425.67 Ignored Sep 3rd TCT Pro Championships 2023
2 Phoenix Loss 13-15 2265.56 Sep 4th TCT Pro Championships 2023
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)