#22 Siege (14-9)

avg: 1541.48  •  sd: 68.95  •  top 16/20: 0.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
37 Agency Win 14-3 1749.08 Jul 15th TCT Pro Elite Challenge East 2023
29 Pop Win 15-10 1855.73 Jul 15th TCT Pro Elite Challenge East 2023
1 Scandal** Loss 4-15 2006.7 Ignored Jul 15th TCT Pro Elite Challenge East 2023
2 Phoenix** Loss 6-15 1879.74 Ignored Jul 16th TCT Pro Elite Challenge East 2023
50 Drift** Win 15-2 1514.94 Ignored Jul 29th TCT Select Flight East 2023
41 Heist Win 13-4 1641.1 Jul 29th TCT Select Flight East 2023
34 Indy Rogue Win 14-9 1660.21 Jul 30th TCT Select Flight East 2023
18 Starling Ultimate Loss 12-14 1453.54 Jul 30th TCT Select Flight East 2023
54 Stellar** Win 15-6 1476.94 Ignored Jul 30th TCT Select Flight East 2023
15 Iris Win 11-10 1872.14 Aug 12th Log Jam
15 Iris Loss 8-13 1250.98 Aug 12th Log Jam
18 Starling Ultimate Loss 9-10 1549.5 Aug 12th Log Jam
86 Versa** Win 13-2 763.15 Ignored Sep 9th 2023 Womens East New England Sectional Championship
47 Vice Win 13-7 1505.78 Sep 9th 2023 Womens East New England Sectional Championship
74 Frolic** Win 13-1 1058.73 Ignored Sep 9th 2023 Womens East New England Sectional Championship
57 Salty Win 13-6 1438.69 Sep 9th 2023 Womens East New England Sectional Championship
7 BENT Loss 6-13 1531.43 Sep 23rd 2023 Northeast Womens Regional Championship
95 Ignite** Win 15-2 624.41 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
15 Iris Loss 9-15 1231.66 Sep 23rd 2023 Northeast Womens Regional Championship
57 Salty** Win 13-4 1438.69 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
8 6ixers Loss 9-15 1590.45 Sep 24th 2023 Northeast Womens Regional Championship
39 Brooklyn Book Club Win 15-9 1598.68 Sep 24th 2023 Northeast Womens Regional Championship
18 Starling Ultimate Loss 12-14 1453.54 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)