#158 OutKast (14-13)

avg: 839.42  •  sd: 56.1  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
251 Big Bend Win 11-6 910.06 Jun 15th ATL Classic 2019
75 Auburn HeyDay Win 11-10 1352.49 Jun 15th ATL Classic 2019
130 m'kay Ultimate Loss 4-10 385.41 Jun 15th ATL Classic 2019
252 Mixchief Win 13-5 956.35 Jun 15th ATL Classic 2019
272 Bold City Win 10-3 839.59 Jun 16th ATL Classic 2019
75 Auburn HeyDay Loss 2-13 627.49 Jun 16th ATL Classic 2019
80 Trash Pandas Loss 5-13 598.09 Jun 16th ATL Classic 2019
35 'Shine Loss 11-12 1400.9 Jul 6th Huntsville Huckfest 2019
231 Mississippi Blues Win 13-8 951.5 Jul 6th Huntsville Huckfest 2019
222 Monster Win 13-6 1141.18 Jul 6th Huntsville Huckfest 2019
228 The Umbrella Win 13-7 1027.57 Jul 6th Huntsville Huckfest 2019
213 Memphis Hustle & Flow Loss 10-11 455.86 Jul 7th Huntsville Huckfest 2019
84 sKNO cone Loss 5-15 565.41 Jul 7th Huntsville Huckfest 2019
80 Trash Pandas Loss 10-12 959.97 Jul 7th Huntsville Huckfest 2019
254 Derby City Thunder Win 13-7 886.87 Aug 17th Mudbowl 2019
231 Mississippi Blues Win 9-7 734.67 Aug 17th Mudbowl 2019
222 Monster Loss 10-11 416.18 Aug 17th Mudbowl 2019
189 Hairy Otter Loss 10-13 337.83 Aug 18th Mudbowl 2019
170 Magic City Mayhem Win 10-7 1152.81 Aug 18th Mudbowl 2019
298 The Leftovers** Win 13-3 600 Ignored Aug 18th Mudbowl 2019
162 BATL Cows Win 10-8 1081.92 Sep 7th East Coast Mixed Club Sectional Championship 2019
21 Bucket** Loss 2-13 1077.2 Ignored Sep 7th East Coast Mixed Club Sectional Championship 2019
35 'Shine** Loss 5-13 925.9 Ignored Sep 7th East Coast Mixed Club Sectional Championship 2019
266 Orbit Win 11-4 860.34 Sep 7th East Coast Mixed Club Sectional Championship 2019
160 APEX Loss 12-13 703.62 Sep 8th East Coast Mixed Club Sectional Championship 2019
130 m'kay Ultimate Win 11-9 1234.62 Sep 8th East Coast Mixed Club Sectional Championship 2019
80 Trash Pandas Loss 6-13 598.09 Sep 8th East Coast 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)