#48 Portland Ivy (8-16)

avg: 832.97  •  sd: 89.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 FAB Loss 4-9 590.7 Jun 23rd Eugene Summer Solstice 40
- 101 Summertime Win 12-11 583.18 Jun 23rd Eugene Summer Solstice 40
- Korra Loss 7-8 663.84 Jun 23rd Eugene Summer Solstice 40
77 Sizzle** Win 11-3 584.12 Ignored Jun 23rd Eugene Summer Solstice 40
46 Venom Win 11-4 1473.67 Jun 24th Eugene Summer Solstice 40
68 Seven Devils Win 10-6 912.56 Jun 24th Eugene Summer Solstice 40
- Korra Win 11-8 1154.44 Jun 24th Eugene Summer Solstice 40
24 Wicked** Loss 5-13 892.17 Ignored Aug 18th TCT Elite Select Challenge 2018
11 Rival** Loss 1-13 1204.18 Ignored Aug 18th TCT Elite Select Challenge 2018
6 6ixers** Loss 3-13 1416.35 Ignored Aug 18th TCT Elite Select Challenge 2018
25 Colorado Small Batch Loss 7-13 889.57 Aug 19th TCT Elite Select Challenge 2018
31 Indy Rogue Loss 10-13 875.85 Aug 19th TCT Elite Select Challenge 2018
12 Traffic** Loss 0-13 1169.07 Ignored Sep 8th Washington Womens Sectional Championship 2018
30 Sneaky House Hippos Loss 3-13 730.71 Sep 8th Washington Womens Sectional Championship 2018
77 Sizzle Win 13-6 584.12 Sep 8th Washington Womens Sectional Championship 2018
36 Seattle Soul Loss 6-12 522.89 Sep 8th Washington Womens Sectional Championship 2018
72 Seattle END Win 13-7 866.12 Sep 9th Washington Womens Sectional Championship 2018
36 Seattle Soul Loss 9-13 683.64 Sep 9th Washington Womens Sectional Championship 2018
71 Koi Win 13-4 929.83 Sep 9th Washington Womens Sectional Championship 2018
12 Traffic** Loss 5-13 1169.07 Ignored Sep 22nd Northwest Womens Regional Championship 2018
26 Elevate Loss 7-12 880.6 Sep 22nd Northwest Womens Regional Championship 2018
9 Schwa** Loss 2-13 1237.97 Ignored Sep 22nd Northwest Womens Regional Championship 2018
20 Underground Loss 5-10 1069.64 Sep 23rd Northwest Womens Regional Championship 2018
30 Sneaky House Hippos Loss 9-13 912.15 Sep 23rd Northwest Womens Regional Championship 2018
**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)