#47 Crush City (16-6)

avg: 1085.87  •  sd: 112.26  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
104 Cazadora** Win 13-2 63.7 Ignored Jun 29th Texas 2 Finger Mens and Womens
101 Maeve** Win 13-1 273.02 Ignored Jun 29th Texas 2 Finger Mens and Womens
94 Inferno** Win 13-3 689.54 Ignored Jun 29th Texas 2 Finger Mens and Womens
- Texas Tango** Win 11-3 413.51 Ignored Jun 29th Texas 2 Finger Mens and Womens
63 Huntsville Laika Win 12-5 1365.09 Jul 20th 2019 Club Terminus
45 Queen Cake Win 12-5 1716.99 Jul 20th 2019 Club Terminus
86 Honey Pot** Win 13-2 852 Ignored Jul 20th 2019 Club Terminus
32 Steel Loss 7-13 822.11 Jul 20th 2019 Club Terminus
45 Queen Cake Win 11-10 1241.99 Jul 21st 2019 Club Terminus
32 Steel Loss 5-13 779.65 Jul 21st 2019 Club Terminus
75 Viva Win 13-6 1161.06 Aug 24th Ski Town Classic 2019
35 Seattle Soul Win 12-9 1603.35 Aug 24th Ski Town Classic 2019
82 Seven Devils** Win 13-4 900.21 Ignored Aug 24th Ski Town Classic 2019
45 Queen Cake Loss 9-11 867.79 Aug 24th Ski Town Classic 2019
56 Venom Win 12-9 1217.13 Aug 25th Ski Town Classic 2019
36 Rampage Loss 5-13 625.19 Aug 25th Ski Town Classic 2019
45 Queen Cake Loss 4-10 516.99 Aug 25th Ski Town Classic 2019
96 Austin Hex** Win 11-3 551.21 Ignored Sep 7th Texas Womens Club Sectional Championship 2019
104 Cazadora** Win 11-0 63.7 Ignored Sep 7th Texas Womens Club Sectional Championship 2019
101 Maeve** Win 11-0 273.02 Ignored Sep 7th Texas Womens Club Sectional Championship 2019
94 Inferno** Win 11-0 689.54 Ignored Sep 7th Texas Womens Club Sectional Championship 2019
17 Showdown Loss 6-11 1180.54 Sep 7th Texas Womens 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)