#110 CITYWIDE Special (4-14)

avg: 1167.75  •  sd: 50.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
31 Garden State Ultimate Loss 6-13 1076.12 Jun 24th Phantom Invite 2023
39 Pittsburgh Temper Loss 8-13 1128.55 Jun 24th Phantom Invite 2023
38 Phantom Loss 3-13 1024.98 Jun 24th Phantom Invite 2023
2 PoNY** Loss 3-13 1733.94 Ignored Jun 25th Phantom Invite 2023
12 Raleigh-Durham United Loss 6-13 1406.62 Jun 25th Phantom Invite 2023
38 Phantom Loss 5-13 1024.98 Jun 25th Phantom Invite 2023
24 Blueprint Loss 7-11 1297.62 Jul 29th TCT Select Flight East 2023
29 Mallard Loss 4-15 1128.26 Jul 29th TCT Select Flight East 2023
46 DeMo Loss 11-13 1334.81 Jul 29th TCT Select Flight East 2023
83 Red Wolves Loss 10-11 1222.98 Jul 30th TCT Select Flight East 2023
55 Colonels Loss 3-15 903.69 Jul 30th TCT Select Flight East 2023
82 Lantern Loss 9-15 834.23 Jul 30th TCT Select Flight East 2023
215 EZ Win 7-4 1044.55 Sep 9th 2023 Mens Founders Sectional Championship
38 Phantom Loss 9-13 1206.41 Sep 9th 2023 Mens Founders Sectional Championship
212 Hardta Spelots Win 13-7 1124.96 Sep 9th 2023 Mens Founders Sectional Championship
190 Side Hustle Win 13-4 1346.84 Sep 9th 2023 Mens Founders Sectional Championship
80 Rumspringa Win 13-12 1490.61 Sep 16th 2023 Mens Founders Sectional Championship
38 Phantom Loss 8-15 1060.17 Sep 16th 2023 Mens Founders Sectional 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)