#218 Great Minnesota Get Together (7-10)

avg: 612.32  •  sd: 59.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
209 Pushovers-B Loss 8-11 293.82 Jul 20th Minnesota Ultimate Disc Invitational
248 Dinosaur Fancy Win 10-9 557.61 Jul 21st Minnesota Ultimate Disc Invitational
154 Melt Loss 5-13 313.8 Jul 21st Minnesota Ultimate Disc Invitational
171 Mousetrap Loss 6-10 303.39 Jul 21st Minnesota Ultimate Disc Invitational
279 Identity Theft Win 11-5 809.93 Jul 21st Minnesota Ultimate Disc Invitational
156 ELevate Loss 8-12 471.83 Aug 17th Cooler Classic 31
171 Mousetrap Loss 9-12 454.19 Aug 17th Cooler Classic 31
214 Stackcats Loss 7-11 170.36 Aug 17th Cooler Classic 31
213 Mastodon Loss 7-9 366.19 Aug 18th Cooler Classic 31
241 EDM Win 9-6 889.11 Aug 18th Cooler Classic 31
228 Midwestern Mediocrity Win 8-7 650.94 Aug 18th Cooler Classic 31
248 Dinosaur Fancy Win 13-8 928.77 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
141 Mad Udderburn Loss 8-11 602.77 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
154 Melt Loss 10-13 585.66 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
32 NOISE** Loss 1-13 1011.8 Ignored Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
226 Boomtown Pandas Win 13-9 990.18 Sep 8th Northwest Plains Mixed Club Sectional Championship 2019
244 Madison United Mixed Ultimate Win 13-6 1061.78 Sep 8th Northwest Plains Mixed Club Sectional Championship 2019
**Blowout Eligible

FAQ

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
  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
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)