#76 Viva (8-16)

avg: 539.64  •  sd: 92.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
93 Ultraviolet Win 10-1 758.84 Jul 27th The Friend Zone
93 Ultraviolet Win 10-5 732.74 Jul 27th The Friend Zone
89 Tempo Loss 4-9 -365.93 Jul 27th The Friend Zone
89 Tempo Win 9-3 834.07 Jul 27th The Friend Zone
82 Autonomous Win 12-4 921.37 Aug 3rd Heavyweights 2019
33 Fusion** Loss 1-13 682 Ignored Aug 3rd Heavyweights 2019
74 Iowa Wild Rose Win 13-6 1163.26 Aug 3rd Heavyweights 2019
52 Stellar Loss 10-13 631.35 Aug 4th Heavyweights 2019
67 Helix Loss 4-11 62.47 Aug 4th Heavyweights 2019
50 Crush City Loss 6-13 399.31 Aug 24th Ski Town Classic 2019
85 Seven Devils Win 12-4 892.68 Aug 24th Ski Town Classic 2019
46 Queen Cake Loss 4-13 470.96 Aug 24th Ski Town Classic 2019
36 Seattle Soul** Loss 4-13 628.88 Ignored Aug 24th Ski Town Classic 2019
93 Ultraviolet Win 13-3 758.84 Aug 25th Ski Town Classic 2019
66 Jackwagon Loss 5-13 79.19 Aug 25th Ski Town Classic 2019
58 Venom Loss 5-11 224.13 Sep 7th So Cal Womens Club Sectional Championship 2019
42 Rampage Loss 2-15 525.62 Sep 7th So Cal Womens Club Sectional Championship 2019
11 Wildfire** Loss 4-15 1267 Ignored Sep 7th So Cal Womens Club Sectional Championship 2019
93 Ultraviolet Win 13-6 758.84 Sep 21st Southwest Club Womens Regional Championship 2019
58 Venom Loss 8-12 382.97 Sep 21st Southwest Club Womens Regional Championship 2019
1 Fury** Loss 0-13 1841.46 Ignored Sep 21st Southwest Club Womens Regional Championship 2019
11 Wildfire** Loss 2-13 1267 Ignored Sep 21st Southwest Club Womens Regional Championship 2019
58 Venom Loss 10-13 495.98 Sep 22nd Southwest Club Womens Regional Championship 2019
32 FAB** Loss 4-11 691.9 Ignored Sep 22nd Southwest 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)