#1 Sockeye (20-1)

avg: 2258.77  •  sd: 70.75  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
10 GOAT Win 13-11 2109.84 Jul 13th TCT Pro Elite Challenge 2019
7 Sub Zero Loss 14-15 1860.71 Jul 13th TCT Pro Elite Challenge 2019
32 Prairie Fire** Win 13-5 2083.73 Ignored Jul 13th TCT Pro Elite Challenge 2019
2 Truck Stop Win 13-12 2306.02 Jul 14th TCT Pro Elite Challenge 2019
15 Rhino Slam! Win 12-8 2258.74 Jul 14th TCT Pro Elite Challenge 2019
12 Doublewide Win 13-5 2455.51 Jul 14th TCT Pro Elite Challenge 2019
5 Chicago Machine Win 14-13 2212.11 Aug 2nd 2019 US Open Club Championship
3 Ring of Fire Win 14-11 2482.33 Aug 3rd 2019 US Open Club Championship
4 PoNY Win 15-12 2408.91 Aug 4th 2019 US Open Club Championship
74 Dark Star Win 13-7 1740.15 Sep 21st Northwest Club Mens Regional Championship 2019
19 Voodoo Win 13-8 2187.35 Sep 21st Northwest Club Mens Regional Championship 2019
90 PowderHogs** Win 13-5 1692.8 Ignored Sep 21st Northwest Club Mens Regional Championship 2019
68 Sawtooth** Win 13-3 1836.41 Ignored Sep 21st Northwest Club Mens Regional Championship 2019
13 Furious George Win 15-9 2352.04 Sep 22nd Northwest Club Mens Regional Championship 2019
15 Rhino Slam! Win 15-9 2333.07 Sep 22nd Northwest Club Mens Regional Championship 2019
13 Furious George Win 15-8 2401.37 Oct 24th USA Ultimate National Championships 2019
9 SoCal Condors Win 15-10 2347.54 Oct 24th USA Ultimate National Championships 2019
8 DiG Win 13-12 2076.74 Oct 24th USA Ultimate National Championships 2019
7 Sub Zero Win 15-9 2501.19 Oct 25th USA Ultimate National Championships 2019
3 Ring of Fire Win 14-13 2294 Oct 26th USA Ultimate National Championships 2019
5 Chicago Machine Win 13-12 2212.11 Oct 27th USA Ultimate National Championships 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)