#7 BENT (16-4)

avg: 2131.43  •  sd: 105.57  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
26 Vengeance Win 15-8 1998.76 Jul 15th TCT Pro Elite Challenge East 2023
30 Tabby Rosa Win 14-10 1782.94 Jul 15th TCT Pro Elite Challenge East 2023
16 Grit Win 12-11 1815.14 Jul 15th TCT Pro Elite Challenge East 2023
17 Ozone Win 13-7 2243.93 Jul 16th TCT Pro Elite Challenge East 2023
2 Phoenix Loss 9-11 2230.54 Jul 16th TCT Pro Elite Challenge East 2023
30 Tabby Rosa Win 14-9 1858.1 Aug 19th TCT Elite Select Challenge 2023
38 FAB** Win 15-2 1719.26 Ignored Aug 19th TCT Elite Select Challenge 2023
14 Parcha Win 12-6 2404.98 Aug 19th TCT Elite Select Challenge 2023
23 Flight Win 13-6 2139.4 Aug 20th TCT Elite Select Challenge 2023
11 Seattle Riot Loss 13-14 1859.62 Aug 20th TCT Elite Select Challenge 2023
12 Nemesis Win 13-9 2350.03 Aug 20th TCT Elite Select Challenge 2023
95 Ignite** Win 15-2 624.41 Ignored Sep 9th 2023 Womens Metro NY Sectional Championship
- Remember When** Win 15-2 1156.3 Ignored Sep 9th 2023 Womens Metro NY Sectional Championship
39 Brooklyn Book Club** Win 15-5 1683.2 Ignored Sep 9th 2023 Womens Metro NY Sectional Championship
57 Salty** Win 13-3 1438.69 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
22 Siege Win 13-6 2141.48 Sep 23rd 2023 Northeast Womens Regional Championship
5 Brute Squad Loss 13-15 2088.65 Sep 23rd 2023 Northeast Womens Regional Championship
18 Starling Ultimate Win 15-7 2274.5 Sep 23rd 2023 Northeast Womens Regional Championship
8 6ixers Win 12-8 2547.08 Sep 24th 2023 Northeast Womens Regional Championship
5 Brute Squad Loss 11-14 1989.49 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)