#221 Replay (2-13)

avg: 358.97  •  sd: 54.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
154 Moontower Loss 2-11 194.6 Jul 15th Boston Invite 2023
178 Eat Lightning Loss 7-8 524.39 Jul 15th Boston Invite 2023
78 Deadweight** Loss 3-10 561.64 Ignored Jul 15th Boston Invite 2023
132 Harfang ultimate Loss 9-12 538.52 Jul 15th Boston Invite 2023
206 Quahogs Win 14-11 794.14 Aug 5th Vacationland
163 Sunken Circus Loss 8-15 206.93 Aug 5th Vacationland
78 Deadweight** Loss 5-15 561.64 Ignored Aug 5th Vacationland
158 Lobrid Loss 5-8 332.35 Aug 5th Vacationland
190 Rainbow Loss 9-12 219.89 Aug 6th Vacationland
44 The Buoy Association** Loss 4-15 874.01 Ignored Aug 6th Vacationland
206 Quahogs Loss 11-13 251.96 Sep 9th 2023 Mixed East New England Sectional Championship
44 The Buoy Association** Loss 2-13 874.01 Ignored Sep 9th 2023 Mixed East New England Sectional Championship
158 Lobrid Loss 7-15 185.95 Sep 9th 2023 Mixed East New England Sectional Championship
206 Quahogs Loss 8-10 218.13 Sep 10th 2023 Mixed East New England Sectional Championship
232 Boston Practice Squad Win 14-11 567.97 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)