#17 Showdown (9-10)

avg: 1727.24  •  sd: 151.31  •  top 16/20: 44.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Heist Win 11-8 1737.33 Jul 13th TCT Pro Elite Challenge 2019
3 Molly Brown Loss 9-13 1930.87 Jul 13th TCT Pro Elite Challenge 2019
15 Nemesis Loss 8-10 1501.32 Jul 13th TCT Pro Elite Challenge 2019
6 6ixers Loss 7-13 1681.83 Jul 14th TCT Pro Elite Challenge 2019
15 Nemesis Loss 9-10 1638.98 Jul 14th TCT Pro Elite Challenge 2019
9 Nightlock Win 12-8 2389.46 Jul 14th TCT Pro Elite Challenge 2019
12 Rival Loss 7-8 1788.04 Jul 14th TCT Pro Elite Challenge 2019
14 Wildfire Loss 11-14 1556.7 Aug 17th TCT Elite Select Challenge 2019
55 Dish** Win 15-4 1475.68 Ignored Aug 17th TCT Elite Select Challenge 2019
13 Ozone Loss 10-15 1450.97 Aug 17th TCT Elite Select Challenge 2019
18 Underground Loss 9-10 1571.48 Aug 18th TCT Elite Select Challenge 2019
19 BENT Loss 7-8 1568.55 Aug 18th TCT Elite Select Challenge 2019
23 LOL Win 11-5 2112.02 Aug 18th TCT Elite Select Challenge 2019
12 Rival Loss 7-8 1788.04 Aug 18th TCT Elite Select Challenge 2019
96 Austin Hex** Win 11-0 551.21 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
47 Crush City Win 11-6 1632.57 Sep 7th Texas Womens Club Sectional Championship 2019
94 Inferno** Win 11-0 689.54 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
**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)