#196 Great Minnesota Get Together (7-12)

avg: 558.75  •  sd: 71.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
164 Pandamonium Loss 7-11 307.41 Jul 15th Cheep Thrills 2023
188 Melt Loss 11-12 485.23 Jul 15th Cheep Thrills 2023
245 Underdogs Win 10-7 463.87 Jul 15th Cheep Thrills 2023
111 Bird Loss 3-13 422.53 Jul 16th Cheep Thrills 2023
237 Ca$h Cow$ Win 13-5 824.38 Jul 16th Cheep Thrills 2023
215 Lake Superior Disc Loss 10-13 99.36 Jul 16th Cheep Thrills 2023
144 Point of No Return Loss 7-13 289.37 Aug 19th Cooler Classic 34
181 Frostbite Win 10-5 1215.05 Aug 19th Cooler Classic 34
121 Jabba Loss 6-10 488.71 Aug 19th Cooler Classic 34
182 The Force Loss 10-13 311.08 Aug 19th Cooler Classic 34
164 Pandamonium Win 15-11 1155.47 Aug 20th Cooler Classic 34
161 Prion Loss 6-10 287.45 Aug 20th Cooler Classic 34
205 Locomotion Win 14-7 1100.06 Aug 20th Cooler Classic 34
111 Bird Loss 6-13 422.53 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
235 Mad City Vibes Win 13-10 557.94 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
155 Madison United Mixed Ultimate Loss 10-13 489 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
92 Mad Udderburn Loss 5-13 500.16 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
181 Frostbite Loss 8-11 275.54 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
235 Mad City Vibes Win 13-6 829.8 Sep 10th 2023 Mixed Northwest Plains 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)