#75 Viva (7-11)

avg: 561.06  •  sd: 92.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
88 Ultraviolet Win 10-1 828.26 Jul 27th The Friend Zone
88 Ultraviolet Win 10-5 802.16 Jul 27th The Friend Zone
89 Tempo Loss 4-9 -385.48 Jul 27th The Friend Zone
89 Tempo Win 9-3 814.52 Jul 27th The Friend Zone
84 Autonomous Win 12-4 895.43 Aug 3rd Heavyweights 2019
31 Fusion** Loss 1-13 783.04 Ignored Aug 3rd Heavyweights 2019
76 Iowa Wild Rose Win 13-6 1079.76 Aug 3rd Heavyweights 2019
53 Stellar Loss 10-13 644.1 Aug 4th Heavyweights 2019
72 Helix Loss 4-11 33.8 Aug 4th Heavyweights 2019
47 Crush City Loss 6-13 485.87 Aug 24th Ski Town Classic 2019
82 Seven Devils Win 12-4 900.21 Aug 24th Ski Town Classic 2019
45 Queen Cake Loss 4-13 516.99 Aug 24th Ski Town Classic 2019
35 Seattle Soul** Loss 4-13 657.99 Ignored Aug 24th Ski Town Classic 2019
88 Ultraviolet Win 13-3 828.26 Aug 25th Ski Town Classic 2019
67 Jackwagon Loss 5-13 110.59 Aug 25th Ski Town Classic 2019
56 Venom Loss 5-11 271.77 Sep 7th So Cal Womens Club Sectional Championship 2019
36 Rampage** Loss 2-15 625.19 Ignored Sep 7th So Cal Womens Club Sectional Championship 2019
14 Wildfire** Loss 4-15 1270.04 Ignored Sep 7th So Cal 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)