#74 Dark Star (8-12)

avg: 1182.62  •  sd: 75.97  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
13 Furious George Loss 6-13 1236.56 Jun 22nd Eugene Summer Solstice 2019
109 Green River Swordfish Loss 9-12 666.72 Jun 22nd Eugene Summer Solstice 2019
48 Red Dawn Loss 5-13 787.58 Jun 22nd Eugene Summer Solstice 2019
194 Komori** Win 13-4 1095.51 Ignored Jun 22nd Eugene Summer Solstice 2019
56 Ghost Train Loss 14-15 1195.48 Jun 23rd Eugene Summer Solstice 2019
109 Green River Swordfish Loss 5-7 683.94 Jun 23rd Eugene Summer Solstice 2019
110 Oregon Eruption! Win 15-11 1390.34 Jun 23rd Eugene Summer Solstice 2019
39 Blackfish Loss 9-15 915.25 Aug 3rd CBR 2019
90 PowderHogs Win 15-10 1546.4 Aug 3rd CBR 2019
162 Rip City Win 15-5 1314.22 Aug 3rd CBR 2019
56 Ghost Train Loss 12-13 1195.48 Aug 4th CBR 2019
109 Green River Swordfish Win 15-14 1137.08 Aug 4th CBR 2019
162 Rip City Win 19-16 976.88 Sep 7th Oregon Mens Club Sectional Championship 2019
90 PowderHogs Win 13-10 1420.94 Sep 21st Northwest Club Mens Regional Championship 2019
1 Sockeye Loss 7-13 1701.23 Sep 21st Northwest Club Mens Regional Championship 2019
68 Sawtooth Loss 10-13 908.27 Sep 21st Northwest Club Mens Regional Championship 2019
19 Voodoo Loss 7-13 1133.65 Sep 21st Northwest Club Mens Regional Championship 2019
77 SOUF Win 12-9 1501.28 Sep 22nd Northwest Club Mens Regional Championship 2019
19 Voodoo Loss 9-14 1217.32 Sep 22nd Northwest Club Mens Regional Championship 2019
68 Sawtooth Loss 12-15 935.92 Sep 22nd Northwest 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)