#8 6ixers (10-8)

avg: 2105.93  •  sd: 88.57  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Flipside Loss 9-13 1849.06 Aug 4th 2023 US Open Club Championships ICC
13 Nightlock Win 15-11 2212.69 Aug 4th 2023 US Open Club Championships ICC
3 Fury Loss 12-13 2329.65 Aug 5th 2023 US Open Club Championships ICC
4 Molly Brown Loss 12-15 2033.54 Aug 5th 2023 US Open Club Championships ICC
16 Grit Win 15-10 2143.74 Sep 2nd TCT Pro Championships 2023
3 Fury Loss 4-15 1854.65 Sep 2nd TCT Pro Championships 2023
11 Seattle Riot Win 15-12 2285.11 Sep 2nd TCT Pro Championships 2023
4 Molly Brown Loss 11-13 2105.19 Sep 3rd TCT Pro Championships 2023
1 Scandal Loss 6-15 2006.7 Sep 3rd TCT Pro Championships 2023
10 Traffic Win 14-6 2701.06 Sep 3rd TCT Pro Championships 2023
5 Brute Squad Loss 11-15 1921.66 Sep 4th TCT Pro Championships 2023
86 Versa** Win 13-0 763.15 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
47 Vice** Win 15-1 1548.25 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
39 Brooklyn Book Club** Win 13-4 1683.2 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
15 Iris Win 15-8 2311.95 Sep 23rd 2023 Northeast Womens Regional Championship
7 BENT Loss 8-12 1690.28 Sep 24th 2023 Northeast Womens Regional Championship
22 Siege Win 15-9 2056.96 Sep 24th 2023 Northeast Womens Regional Championship
15 Iris Win 15-11 2128.3 Sep 24th 2023 Northeast Womens Regional Championship
**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)