#78 Deadweight (17-8)

avg: 1161.64  •  sd: 47.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
221 Replay** Win 10-3 958.97 Ignored Jul 15th Boston Invite 2023
178 Eat Lightning Win 13-1 1249.39 Jul 15th Boston Invite 2023
132 Harfang ultimate Win 13-5 1483.89 Jul 15th Boston Invite 2023
154 Moontower Win 11-7 1261.49 Jul 15th Boston Invite 2023
206 Quahogs Win 14-8 1016.83 Aug 5th Vacationland
221 Replay** Win 15-5 958.97 Ignored Aug 5th Vacationland
163 Sunken Circus Win 15-7 1371.74 Aug 5th Vacationland
44 The Buoy Association Loss 6-8 1173.52 Aug 5th Vacationland
190 Rainbow Win 15-4 1165.25 Aug 6th Vacationland
158 Lobrid Win 13-8 1282.11 Aug 6th Vacationland
66 HVAC Win 12-10 1482.58 Aug 19th Philly Invite 2023
175 Philly Twist Win 11-8 1021.15 Aug 19th Philly Invite 2023
106 Ant Madness Win 13-11 1256.74 Aug 19th Philly Invite 2023
68 Heat Wave Loss 7-12 715.66 Aug 20th Philly Invite 2023
59 Greater Baltimore Anthem Loss 10-11 1184.03 Aug 20th Philly Invite 2023
65 League of Shadows Win 13-12 1373.69 Aug 20th Philly Invite 2023
178 Eat Lightning Win 13-5 1249.39 Sep 9th 2023 Mixed Metro New York Sectional Championship
155 NY Swipes Win 13-3 1390.42 Sep 9th 2023 Mixed Metro New York Sectional Championship
162 Room Temperature Win 13-9 1193.44 Sep 9th 2023 Mixed Metro New York Sectional Championship
71 Grand Army Win 12-11 1329.31 Sep 9th 2023 Mixed Metro New York Sectional Championship
62 Funk Loss 12-13 1136.89 Sep 10th 2023 Mixed Metro New York Sectional Championship
68 Heat Wave Loss 6-12 656.86 Sep 10th 2023 Mixed Metro New York Sectional Championship
44 The Buoy Association Loss 7-13 916.48 Sep 23rd 2023 Northeast Mixed Regional Championship
71 Grand Army Loss 10-11 1079.31 Sep 23rd 2023 Northeast Mixed Regional Championship
84 Buffalo Lake Effect Loss 9-10 1005.22 Sep 23rd 2023 Northeast Mixed 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)