#111 Cryptic (12-11)

avg: 766.47  •  sd: 73.65  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
154 Black Market II Win 12-6 886.14 Jun 30th Spirit of the Plains 2018
120 KC SmokeStack Loss 10-12 473.62 Jun 30th Spirit of the Plains 2018
117 THE BODY Loss 7-13 173.41 Jun 30th Spirit of the Plains 2018
60 DeMo Loss 0-13 496.58 Jun 30th Spirit of the Plains 2018
154 Black Market II Win 9-7 586.17 Jul 1st Spirit of the Plains 2018
117 THE BODY Win 12-7 1251.45 Jul 1st Spirit of the Plains 2018
- Baemaker Win 13-8 791.01 Aug 4th Heavyweights 2018
112 Enigma Win 13-11 993.28 Aug 4th Heavyweights 2018
161 Ironside Win 13-6 807.16 Aug 4th Heavyweights 2018
56 Haymaker Loss 7-13 572.74 Aug 5th Heavyweights 2018
129 Prime Loss 8-9 519.69 Aug 5th Heavyweights 2018
95 Scythe Win 15-14 1004.07 Aug 18th Cooler Classic 30
73 Greater Gary Goblins Y Win 13-11 1244.64 Aug 18th Cooler Classic 30
56 Haymaker Loss 6-13 530.27 Aug 18th Cooler Classic 30
124 Wisconsin Hops Win 15-11 1077.51 Aug 19th Cooler Classic 30
60 DeMo Loss 11-15 715.41 Aug 19th Cooler Classic 30
49 CaSTLe Loss 6-13 614.15 Sep 15th West Plains Mens Sectional Championship 2018
120 KC SmokeStack Win 12-10 949.87 Sep 15th West Plains Mens Sectional Championship 2018
60 DeMo Loss 9-13 678.01 Sep 15th West Plains Mens Sectional Championship 2018
95 Scythe Loss 8-13 382.91 Sep 16th West Plains Mens Sectional Championship 2018
164 Fifty-Fifty** Win 13-5 737.74 Ignored Sep 16th West Plains Mens Sectional Championship 2018
53 Illusion Loss 10-13 845.94 Sep 16th West Plains Mens Sectional Championship 2018
- Miner Magic Win 13-6 1028.88 Sep 16th West Plains Mens Sectional 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)