#251 Big Bend (6-14)

avg: 363.36  •  sd: 72.19  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
130 m'kay Ultimate Loss 6-13 385.41 Jun 15th ATL Classic 2019
80 Trash Pandas** Loss 4-13 598.09 Ignored Jun 15th ATL Classic 2019
158 OutKast Loss 6-11 292.72 Jun 15th ATL Classic 2019
252 Mixchief Win 12-5 956.35 Jun 15th ATL Classic 2019
278 Baywatch Win 13-10 493.04 Jun 16th ATL Classic 2019
75 Auburn HeyDay** Loss 3-13 627.49 Ignored Jun 16th ATL Classic 2019
266 Orbit Win 10-5 834.24 Jun 16th ATL Classic 2019
155 Jackpot Loss 3-13 256.9 Jul 12th Swan Boat 2019
222 Monster Loss 7-12 20.67 Jul 12th Swan Boat 2019
206 FIRE ULTIMATE CLUB MIAMI Loss 5-9 86.71 Jul 12th Swan Boat 2019
272 Bold City Loss 6-7 114.59 Jul 13th Swan Boat 2019
94 Mutiny** Loss 1-15 528.81 Ignored Jul 14th Swan Boat 2019
278 Baywatch Win 14-9 638.77 Jul 14th Swan Boat 2019
252 Mixchief Loss 7-12 -164.16 Jul 14th Swan Boat 2019
94 Mutiny** Loss 4-13 528.81 Ignored Sep 7th Florida Mixed Club Sectional Championship 2019
252 Mixchief Win 12-11 481.35 Sep 7th Florida Mixed Club Sectional Championship 2019
272 Bold City Win 9-6 658.16 Sep 7th Florida Mixed Club Sectional Championship 2019
20 Weird** Loss 2-13 1081.45 Ignored Sep 7th Florida Mixed Club Sectional Championship 2019
155 Jackpot Loss 5-12 256.9 Sep 8th Florida Mixed Club Sectional Championship 2019
206 FIRE ULTIMATE CLUB MIAMI Loss 9-12 270.41 Sep 8th Florida 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)