#279 Identity Theft (4-13)

avg: 155.32  •  sd: 55.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
294 MUTT Win 13-11 -79.17 Jul 20th Minnesota Ultimate Disc Invitational
219 Great Minnesota Get Together Loss 5-11 -43.59 Jul 21st Minnesota Ultimate Disc Invitational
241 Madison United Mixed Ultimate Loss 8-12 -22.22 Jul 21st Minnesota Ultimate Disc Invitational
141 Point of No Return** Loss 4-13 298.63 Ignored Jul 21st Minnesota Ultimate Disc Invitational
287 Ope! Win 10-7 413.23 Jul 21st Minnesota Ultimate Disc Invitational
168 ELevate** Loss 4-13 180.15 Ignored Aug 3rd Heavyweights 2019
183 Wildstyle Loss 5-13 112.34 Aug 3rd Heavyweights 2019
290 Taco Cat Win 13-8 407.98 Aug 3rd Heavyweights 2019
199 Jabba Loss 7-13 79.19 Aug 4th Heavyweights 2019
176 Mousetrap Loss 4-13 132.43 Aug 4th Heavyweights 2019
250 Mishigami Loss 8-13 -132.74 Aug 4th Heavyweights 2019
241 Madison United Mixed Ultimate Loss 12-13 293.94 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
38 Minnesota Star Power** Loss 2-13 911.76 Ignored Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
141 Point of No Return Loss 8-13 402.47 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
176 Mousetrap Loss 4-13 132.43 Sep 7th Northwest Plains Mixed Club Sectional Championship 2019
245 Dinosaur Fancy Loss 10-13 59.08 Sep 8th Northwest Plains Mixed Club Sectional Championship 2019
295 Fox Valley Forge Win 13-4 255.54 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)