#73 m'kay Ultimate (13-5)

avg: 1233.75  •  sd: 70.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
86 Crown Peach Loss 8-10 884.31 Jul 8th Club Terminus 2023
204 New Orleans Boil** Win 12-3 1123.57 Ignored Jul 8th Club Terminus 2023
238 Victrix** Win 13-2 793.58 Ignored Jul 8th Club Terminus 2023
53 Roma Ultima Win 12-10 1587 Jul 9th Club Terminus 2023
81 B-Unit Win 13-11 1405.22 Jul 9th Club Terminus 2023
126 Barefoot Win 13-3 1567.13 Jul 9th Club Terminus 2023
248 Pickles** Win 15-4 650.83 Ignored Aug 12th HoDown Showdown 2023
150 Memphis STAX Win 12-6 1406.74 Aug 12th HoDown Showdown 2023
122 Magnanimouse Win 13-8 1477.44 Aug 12th HoDown Showdown 2023
53 Roma Ultima Loss 10-15 895.27 Aug 13th HoDown Showdown 2023
96 Bear Jordan Win 15-11 1457.17 Aug 13th HoDown Showdown 2023
66 Malice in Wonderland Loss 6-11 722 Aug 13th HoDown Showdown 2023
34 Dirty Bird Loss 10-13 1254.97 Sep 9th 2023 Mixed East Coast Sectional Championship
217 Flood Zone** Win 13-3 1009.59 Ignored Sep 9th 2023 Mixed East Coast Sectional Championship
86 Crown Peach Loss 9-10 1021.98 Sep 9th 2023 Mixed East Coast Sectional Championship
195 MoonPi Win 13-6 1167.93 Sep 9th 2023 Mixed East Coast Sectional Championship
218 Hairy Otter** Win 13-4 989.72 Ignored Sep 10th 2023 Mixed East Coast Sectional Championship
152 Verdant Win 13-8 1320.66 Sep 10th 2023 Mixed East Coast 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)