#93 Ultraviolet (3-16)

avg: 158.84  •  sd: 99.88  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
89 Tempo Win 9-7 513.41 Jul 27th The Friend Zone
76 Viva Loss 1-10 -60.36 Jul 27th The Friend Zone
89 Tempo Loss 3-6 -312.63 Jul 27th The Friend Zone
76 Viva Loss 5-10 -34.26 Jul 27th The Friend Zone
49 Fiasco** Loss 3-10 425.1 Ignored Aug 24th Ski Town Classic 2019
66 Jackwagon Loss 4-13 79.19 Aug 24th Ski Town Classic 2019
58 Venom** Loss 2-13 224.13 Ignored Aug 24th Ski Town Classic 2019
42 Rampage** Loss 1-13 525.62 Ignored Aug 24th Ski Town Classic 2019
76 Viva Loss 3-13 -60.36 Aug 25th Ski Town Classic 2019
85 Seven Devils Win 13-0 892.68 Aug 25th Ski Town Classic 2019
89 Tempo Win 8-6 534.56 Sep 8th Nor Cal Womens Club Sectional Championship 2019
32 FAB** Loss 0-13 691.9 Ignored Sep 8th Nor Cal Womens Club Sectional Championship 2019
- 2nd Wave Loss 4-11 155.72 Sep 8th Nor Cal Womens Club Sectional Championship 2019
22 LOL** Loss 0-13 929.16 Ignored Sep 8th Nor Cal Womens Club Sectional Championship 2019
11 Wildfire** Loss 1-13 1267 Ignored Sep 21st Southwest Club Womens Regional Championship 2019
76 Viva Loss 6-13 -60.36 Sep 21st Southwest Club Womens Regional Championship 2019
58 Venom Loss 7-13 266.59 Sep 21st Southwest Club Womens Regional Championship 2019
1 Fury** Loss 0-13 1841.46 Ignored Sep 21st Southwest Club Womens Regional Championship 2019
89 Tempo Loss 8-11 -131.54 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)