#292 Mixfits (2-14)

avg: -82.66  •  sd: 93.42  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
256 Balloon Loss 4-15 -222.14 Jul 13th Riverside Classic 2019
104 Moontower** Loss 0-15 559.12 Ignored Jul 13th Riverside Classic 2019
211 Mud Turtles** Loss 1-15 50.27 Ignored Jul 13th Riverside Classic 2019
75 Bexar** Loss 0-15 673.51 Ignored Jul 14th Riverside Classic 2019
276 Alpha Loss 3-15 -359.43 Jul 14th Riverside Classic 2019
211 Mud Turtles** Loss 3-15 50.27 Ignored Jul 14th Riverside Classic 2019
297 NWA White Tails Win 13-2 194.97 Aug 17th Hootie on the Hill 2019
186 Be Reasonable Loss 6-13 145.44 Aug 17th Hootie on the Hill 2019
243 rubber duck ultimate. Loss 1-13 -137.36 Aug 17th Hootie on the Hill 2019
149 Tex Mix** Loss 1-13 326.68 Ignored Aug 17th Hootie on the Hill 2019
297 NWA White Tails Win 13-8 91.13 Aug 18th Hootie on the Hill 2019
243 rubber duck ultimate. Loss 9-15 -52.84 Aug 18th Hootie on the Hill 2019
75 Bexar** Loss 1-13 673.51 Ignored Sep 7th Texas Mixed Club Sectional Championship 2019
256 Balloon Loss 4-10 -222.14 Sep 7th Texas Mixed Club Sectional Championship 2019
149 Tex Mix** Loss 0-13 326.68 Ignored Sep 7th Texas Mixed Club Sectional Championship 2019
261 Boomshakalaka Loss 2-12 -253.87 Sep 8th Texas 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)