#86 Green River Swordfish (9-17)

avg: 922.11  •  sd: 91.33  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
93 Battery Win 11-6 1430.58 Jul 7th 2018 San Diego Slammer
- Whiskeyjacks Loss 2-11 522.87 Jul 7th 2018 San Diego Slammer
81 Sundowners Loss 7-11 480.83 Jul 7th 2018 San Diego Slammer
24 Inception Loss 7-11 954.44 Jul 7th 2018 San Diego Slammer
144 Gridlock Win 11-7 935.15 Jul 7th 2018 San Diego Slammer
91 Sprawl Loss 6-13 297.37 Jul 8th 2018 San Diego Slammer
88 PowderHogs Loss 14-15 783.48 Jul 8th 2018 San Diego Slammer
12 Rhino Slam** Loss 4-13 1179.67 Ignored Aug 25th CBR Memorial 2018
63 Sawtooth Loss 8-12 615.41 Aug 25th CBR Memorial 2018
91 Sprawl Win 11-10 1022.37 Aug 25th CBR Memorial 2018
46 Ghost Train Loss 9-13 807.26 Aug 25th CBR Memorial 2018
78 Rip City Ultimate Win 13-8 1471.95 Aug 26th CBR Memorial 2018
38 Dark Star Loss 7-13 722.39 Aug 26th CBR Memorial 2018
40 Streetgang Loss 6-13 675.1 Aug 26th CBR Memorial 2018
- Anchor Win 12-8 690.4 Sep 8th Nor Cal Mens Sectional Championship 2018
93 Battery Win 11-7 1350.78 Sep 8th Nor Cal Mens Sectional Championship 2018
19 Guerrilla Loss 6-13 947.89 Sep 8th Nor Cal Mens Sectional Championship 2018
- Journeymen Win 13-4 1064.22 Sep 9th Nor Cal Mens Sectional Championship 2018
99 Red Dawn Loss 8-9 718.71 Sep 9th Nor Cal Mens Sectional Championship 2018
19 Guerrilla** Loss 2-13 947.89 Ignored Sep 9th Nor Cal Mens Sectional Championship 2018
74 DOGGPOUND Loss 13-15 783.12 Sep 22nd Southwest Mens Regional Championship 2018
17 SoCal Condors** Loss 5-15 1081.02 Ignored Sep 22nd Southwest Mens Regional Championship 2018
40 Streetgang Loss 9-15 759.62 Sep 22nd Southwest Mens Regional Championship 2018
1 Revolver Loss 7-15 1479.65 Sep 23rd Southwest Mens Regional Championship 2018
74 DOGGPOUND Win 16-15 1122.3 Sep 23rd Southwest Mens Regional Championship 2018
81 Sundowners Win 13-12 1072.72 Sep 23rd Southwest Mens Regional 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)