#87 Garbage (12-10)

avg: 1205.37  •  sd: 54.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
82 Pegasus Loss 8-10 982.93 Jun 15th Northwest Mixer 2019
43 Birdfruit Loss 10-12 1279.67 Jun 15th Northwest Mixer 2019
28 Lights Out Loss 6-11 1096.91 Jun 15th Northwest Mixer 2019
11 Lochsa Loss 6-10 1387.59 Jun 15th Northwest Mixer 2019
3 Seattle Mixtape Loss 5-11 1483.72 Jun 15th Northwest Mixer 2019
138 Choco Ghost House Win 15-10 1448.36 Jun 29th Rose City Rumble 2019
64 The Administrators Loss 11-14 1031.62 Jun 29th Rose City Rumble 2019
77 Happy Hour Loss 11-13 1040.52 Jun 29th Rose City Rumble 2019
232 Natural Twenties** Win 15-3 1091.54 Ignored Jun 29th Rose City Rumble 2019
82 Pegasus Loss 6-15 645.6 Aug 3rd Kleinman Eruption 2019
98 Buckwild Win 12-9 1519.69 Aug 3rd Kleinman Eruption 2019
245 Mola Mola Win 15-7 1053.96 Aug 3rd Kleinman Eruption 2019
272 SkyLab** Win 15-4 875.16 Ignored Aug 3rd Kleinman Eruption 2019
138 Choco Ghost House Win 14-9 1468.62 Aug 4th Kleinman Eruption 2019
232 Natural Twenties Win 15-9 1007.02 Aug 4th Kleinman Eruption 2019
150 Igneous Ultimate Win 13-8 1421.21 Aug 4th Kleinman Eruption 2019
28 Lights Out Loss 7-13 1086.08 Sep 7th Washington Mixed Club Sectional Championship 2019
152 Fable Win 13-9 1340.58 Sep 7th Washington Mixed Club Sectional Championship 2019
250 Friendzone Win 12-7 937.36 Sep 7th Washington Mixed Club Sectional Championship 2019
144 T.T Win 13-10 1275.57 Sep 7th Washington Mixed Club Sectional Championship 2019
43 Birdfruit Loss 7-14 934.91 Sep 8th Washington Mixed Club Sectional Championship 2019
114 Surge Win 15-6 1708.9 Sep 8th Washington Mixed Club Sectional 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)