#61 Trainwreck (8-10)

avg: 809.84  •  sd: 94.04  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
4 Molly Brown** Loss 1-13 1680.55 Ignored Jun 22nd Fort Collins Summer Solstice 2019
66 Jackwagon Loss 8-9 554.19 Jun 22nd Fort Collins Summer Solstice 2019
27 Wicked Loss 7-11 934.43 Jun 23rd Fort Collins Summer Solstice 2019
54 Dish Loss 7-11 434.44 Jun 23rd Fort Collins Summer Solstice 2019
55 Crackle Loss 9-13 449.47 Aug 17th Cooler Classic 31
103 The Matriarchy** Win 13-1 258.68 Ignored Aug 17th Cooler Classic 31
84 Cold Cuts Win 13-8 796.92 Aug 17th Cooler Classic 31
91 MystiKuE Win 13-7 748.53 Aug 17th Cooler Classic 31
52 Stellar Loss 7-9 680.16 Aug 18th Cooler Classic 31
55 Crackle Win 6-5 993.04 Aug 18th Cooler Classic 31
28 Colorado Small Batch Loss 4-15 795.57 Sep 7th Rocky Mountain Womens Club Sectional Championship 2019
66 Jackwagon Win 11-4 1279.19 Sep 7th Rocky Mountain Womens Club Sectional Championship 2019
- COSMOS** Win 15-2 600 Ignored Sep 7th Rocky Mountain Womens Club Sectional Championship 2019
17 Showdown** Loss 0-13 1080.21 Ignored Sep 21st South Central Club Womens Regional Championship 2019
50 Crush City Win 13-10 1327.45 Sep 21st South Central Club Womens Regional Championship 2019
83 Inferno Win 8-7 433.17 Sep 21st South Central Club Womens Regional Championship 2019
28 Colorado Small Batch Loss 3-13 795.57 Sep 22nd South Central Club Womens Regional Championship 2019
4 Molly Brown** Loss 0-13 1680.55 Ignored Sep 22nd South Central Club Womens 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)