#206 Quahogs (7-10)

avg: 480.8  •  sd: 74.88  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
111 Lampshade Loss 7-12 484.91 Jul 8th AntlerLock
163 Sunken Circus Loss 7-14 188.85 Jul 8th AntlerLock
233 Equinox Loss 8-9 121.42 Jul 8th AntlerLock
190 Rainbow Win 12-6 1144.56 Jul 9th AntlerLock
168 Default Loss 8-13 245.2 Jul 9th AntlerLock
233 Equinox Win 7-3 846.42 Jul 9th AntlerLock
78 Deadweight Loss 8-14 625.61 Aug 5th Vacationland
221 Replay Loss 11-14 45.63 Aug 5th Vacationland
163 Sunken Circus Win 11-10 896.74 Aug 5th Vacationland
190 Rainbow Win 5-3 983.82 Aug 5th Vacationland
158 Lobrid Loss 8-12 344.79 Aug 6th Vacationland
44 The Buoy Association** Loss 5-15 874.01 Ignored Aug 6th Vacationland
65 League of Shadows** Loss 5-13 648.69 Ignored Sep 9th 2023 Mixed East New England Sectional Championship
221 Replay Win 13-11 587.81 Sep 9th 2023 Mixed East New England Sectional Championship
190 Rainbow Loss 7-14 -17.63 Sep 9th 2023 Mixed East New England Sectional Championship
223 Electric Eyebrows Win 10-9 463.47 Sep 10th 2023 Mixed East New England Sectional Championship
221 Replay Win 10-8 621.64 Sep 10th 2023 Mixed East New England 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)