#109 Green River Swordfish (9-14)

avg: 1012.08  •  sd: 72.15  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
13 Furious George Loss 7-13 1279.03 Jun 22nd Eugene Summer Solstice 2019
48 Red Dawn Loss 9-13 969.01 Jun 22nd Eugene Summer Solstice 2019
74 Dark Star Win 12-9 1527.98 Jun 22nd Eugene Summer Solstice 2019
194 Komori Win 13-7 1053.04 Jun 22nd Eugene Summer Solstice 2019
158 Cojones Win 15-7 1342.54 Jun 23rd Eugene Summer Solstice 2019
68 Sawtooth Loss 11-15 855.25 Jun 23rd Eugene Summer Solstice 2019
74 Dark Star Win 7-5 1510.76 Jun 23rd Eugene Summer Solstice 2019
56 Ghost Train Loss 6-15 720.48 Aug 3rd CBR 2019
158 Cojones Win 15-13 956.72 Aug 3rd CBR 2019
100 Seattle Blacklist Loss 13-15 822.47 Aug 3rd CBR 2019
74 Dark Star Loss 14-15 1057.62 Aug 4th CBR 2019
162 Rip City Loss 13-15 500.04 Aug 4th CBR 2019
145 Little Teapots Win 15-12 1125.4 Sep 7th Nor Cal Mens Club Sectional Championship 2019
60 Guerrilla Loss 8-15 717.22 Sep 7th Nor Cal Mens Club Sectional Championship 2019
153 The Berkeley Bobcats Loss 12-14 536.8 Sep 7th Nor Cal Mens Club Sectional Championship 2019
145 Little Teapots Win 15-9 1340.39 Sep 8th Nor Cal Mens Club Sectional Championship 2019
61 Battery Loss 8-15 714.49 Sep 8th Nor Cal Mens Club Sectional Championship 2019
61 Battery Win 11-9 1528.5 Sep 21st Southwest Club Mens Regional Championship 2019
6 Revolver** Loss 4-11 1438.57 Ignored Sep 21st Southwest Club Mens Regional Championship 2019
119 DOGGPOUND Win 11-7 1404.78 Sep 21st Southwest Club Mens Regional Championship 2019
70 Sundowners Loss 7-11 761.21 Sep 21st Southwest Club Mens Regional Championship 2019
60 Guerrilla Loss 11-13 1053.19 Sep 22nd Southwest Club Mens Regional Championship 2019
69 Streetgang Loss 10-13 906.34 Sep 22nd Southwest Club Mens Regional 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)