#65 Notorious C.L.E. (9-10)

avg: 680.98  •  sd: 67.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
68 Eliza Furnace Loss 8-10 399.12 Jun 22nd SCINNY 2019
82 Autonomous Win 13-9 739.93 Jun 22nd SCINNY 2019
99 Belle** Win 15-6 445.4 Ignored Jun 22nd SCINNY 2019
41 Indy Rogue Loss 7-15 529.03 Jun 23rd SCINNY 2019
90 Sureshot Win 15-4 816.64 Jun 23rd SCINNY 2019
29 Virginia Rebellion** Loss 2-13 786.08 Ignored Aug 10th Chesapeake Open 2019
62 Agency Loss 4-13 190.78 Aug 10th Chesapeake Open 2019
48 Brooklyn Book Club Loss 10-12 795.72 Aug 10th Chesapeake Open 2019
45 Vice Loss 12-14 852.15 Aug 11th Chesapeake Open 2019
62 Agency Win 14-11 1104.11 Aug 11th Chesapeake Open 2019
70 Broad City Win 11-10 767.29 Aug 11th Chesapeake Open 2019
82 Autonomous Win 15-5 921.37 Sep 7th East Plains Womens Club Sectional Championship 2019
90 Sureshot Win 15-11 597.81 Sep 7th East Plains Womens Club Sectional Championship 2019
14 Nemesis** Loss 2-13 1174.52 Ignored Sep 20th Great Lakes Womens Club Regional Championship 2019
54 Dish Loss 11-12 776.33 Sep 21st Great Lakes Womens Club Regional Championship 2019
90 Sureshot Win 13-11 445.48 Sep 21st Great Lakes Womens Club Regional Championship 2019
67 Helix Loss 11-12 537.47 Sep 22nd Great Lakes Womens Club Regional Championship 2019
82 Autonomous Win 11-9 570.58 Sep 22nd Great Lakes Womens Club Regional Championship 2019
41 Indy Rogue Loss 12-15 828.54 Sep 22nd Great Lakes Womens Club 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)