#210 VU (3-17)

avg: 526.65  •  sd: 91.29  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
- Brewcade** Loss 1-15 965.82 Ignored Jun 9th Bay Area Ultimate Classic 2018
85 Platypi** Loss 3-12 590.67 Ignored Jun 9th Bay Area Ultimate Classic 2018
190 DR Loss 5-15 35.15 Jun 9th Bay Area Ultimate Classic 2018
170 Spoiler Alert Loss 4-11 158.52 Jun 10th Bay Area Ultimate Classic 2018
245 Hot Stix Win 15-3 564.35 Jun 10th Bay Area Ultimate Classic 2018
190 DR Loss 4-13 35.15 Jun 10th Bay Area Ultimate Classic 2018
131 Absolute Zero Loss 6-12 386.75 Jul 21st Revolution 2018
120 Mimosas Loss 4-13 422.95 Jul 21st Revolution 2018
- POWERLINE Loss 6-10 837.79 Jul 21st Revolution 2018
161 AC Bandits Win 10-9 925.29 Jul 22nd Revolution 2018
193 Feral Cows Loss 7-10 226.1 Jul 22nd Revolution 2018
190 DR Loss 9-10 510.15 Jul 22nd Revolution 2018
136 Lawn Patrol Loss 6-8 648.23 Sep 8th Nor Cal Mixed Sectional Championship 2018
12 Mischief** Loss 3-11 1184.35 Ignored Sep 8th Nor Cal Mixed Sectional Championship 2018
74 Alchemy Loss 6-11 718.5 Sep 8th Nor Cal Mixed Sectional Championship 2018
190 DR Loss 9-10 510.15 Sep 8th Nor Cal Mixed Sectional Championship 2018
85 Platypi Loss 6-11 643.98 Sep 8th Nor Cal Mixed Sectional Championship 2018
182 Sebastopol Orchard Loss 10-11 547.27 Sep 9th Nor Cal Mixed Sectional Championship 2018
190 DR Loss 9-11 385.94 Sep 9th Nor Cal Mixed Sectional Championship 2018
236 Delta Breeze Win 13-6 809.22 Sep 9th Nor Cal Mixed Sectional Championship 2018
**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)