#4 Brute Squad (8-3)

avg: 2343.67  •  sd: 78.94  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
54 Venus** Win 15-2 1556.73 Ignored Jun 1st New York Warmup Womens Sanctioned Games 2019
16 Iris Win 15-3 2351.91 Jun 1st New York Warmup Womens Sanctioned Games 2019
39 Stella** Win 15-5 1811.9 Ignored Jun 2nd New York Warmup Womens Sanctioned Games 2019
5 Scandal Win 15-13 2468.81 Aug 2nd 2019 US Open Club Championship
5 Scandal Win 15-14 2379.63 Aug 3rd 2019 US Open Club Championship
6 6ixers Loss 9-12 1893.99 Aug 31st TCT Pro Championships 2019
1 Fury Loss 11-12 2377.24 Aug 31st TCT Pro Championships 2019
7 Phoenix Win 14-13 2272.19 Aug 31st TCT Pro Championships 2019
9 Nightlock Win 15-7 2548.3 Sep 1st TCT Pro Championships 2019
2 Seattle Riot Loss 10-13 2085.24 Sep 1st TCT Pro Championships 2019
5 Scandal Win 13-8 2750.79 Sep 1st TCT Pro Championships 2019
**Blowout Eligible

FAQ

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
  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
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)