#47 Darkwing (11-10)

avg: 1419.5  •  sd: 64.66  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
71 Grand Army Win 10-9 1329.31 Jul 15th Boston Invite 2023
7 XIST Loss 8-10 1658.2 Jul 15th Boston Invite 2023
38 Pittsburgh Port Authority Loss 12-13 1376.11 Jul 15th Boston Invite 2023
62 Funk Win 13-4 1861.89 Jul 15th Boston Invite 2023
29 RAMP Loss 9-13 1204.18 Jul 29th TCT Select Flight East 2023
31 Kansas City United Loss 7-8 1470.15 Jul 29th TCT Select Flight East 2023
77 Bullet Train Win 12-6 1742 Jul 29th TCT Select Flight East 2023
46 Revival Loss 6-12 856.75 Jul 30th TCT Select Flight East 2023
65 League of Shadows Win 12-5 1848.69 Jul 30th TCT Select Flight East 2023
57 Steamboat Loss 12-13 1214.4 Jul 30th TCT Select Flight East 2023
196 Beached Whales** Win 13-5 1142.38 Ignored Sep 9th 2023 Mixed East New England Sectional Championship
95 Scarecrow Win 13-3 1668.5 Sep 9th 2023 Mixed East New England Sectional Championship
111 Lampshade Win 12-6 1584.73 Sep 9th 2023 Mixed East New England Sectional Championship
45 Wild Card Loss 9-15 942.05 Sep 9th 2023 Mixed East New England Sectional Championship
64 Obscure Win 15-11 1631.54 Sep 10th 2023 Mixed East New England Sectional Championship
44 The Buoy Association Loss 13-14 1349.01 Sep 10th 2023 Mixed East New England Sectional Championship
48 Townies Loss 10-12 1164.74 Sep 23rd 2023 Northeast Mixed Regional Championship
64 Obscure Win 13-12 1375.38 Sep 23rd 2023 Northeast Mixed Regional Championship
55 Garbage Plates Loss 11-13 1113.43 Sep 23rd 2023 Northeast Mixed Regional Championship
68 Heat Wave Win 12-9 1581.54 Sep 24th 2023 Northeast Mixed Regional Championship
62 Funk Win 12-9 1607.26 Sep 24th 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)