#64 TWISTED (10-13)

avg: 689.75  •  sd: 66.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 Crush City** Loss 3-13 713.39 Ignored Jun 24th Texas 2 Finger 2023
91 Firewheel Win 12-7 594.9 Jun 24th Texas 2 Finger 2023
46 San Antonio Problems Loss 5-8 505.7 Jun 24th Texas 2 Finger 2023
91 Firewheel Win 7-5 402.53 Jun 25th Texas 2 Finger 2023
40 Hayride Loss 2-11 477.74 Jun 25th Texas 2 Finger 2023
101 Inferno** Win 11-2 322.99 Ignored Jun 25th Texas 2 Finger 2023
60 Wicked Win 8-5 1176.52 Aug 26th Ragna Rock 2023
66 Banshee Win 5-4 750.96 Aug 26th Ragna Rock 2023
35 Huntsville Laika Loss 3-10 572.59 Aug 26th Ragna Rock 2023
40 Hayride Loss 6-8 777.25 Aug 27th Ragna Rock 2023
40 Hayride Loss 5-9 548.68 Aug 27th Ragna Rock 2023
35 Huntsville Laika Win 7-6 1297.59 Aug 27th Ragna Rock 2023
32 Crush City** Loss 4-14 713.39 Ignored Sep 9th 2023 Womens Texas Sectional Championship
91 Firewheel** Win 15-0 674.38 Ignored Sep 9th 2023 Womens Texas Sectional Championship
101 Inferno** Win 15-6 322.99 Ignored Sep 9th 2023 Womens Texas Sectional Championship
26 Vengeance** Loss 5-14 833.95 Ignored Sep 10th 2023 Womens Texas Sectional Championship
46 San Antonio Problems Loss 8-11 593.69 Sep 10th 2023 Womens Texas Sectional Championship
82 Venom Win 9-7 558.01 Sep 23rd 2023 South Central Womens Regional
25 Colorado Small Batch** Loss 4-12 859.47 Ignored Sep 23rd 2023 South Central Womens Regional
71 Jackwagon Win 7-5 825.87 Sep 23rd 2023 South Central Womens Regional
40 Hayride Loss 4-10 477.74 Sep 24th 2023 South Central Womens Regional
4 Molly Brown** Loss 1-15 1734.03 Ignored Sep 24th 2023 South Central Womens Regional
46 San Antonio Problems Loss 6-8 658.81 Sep 24th 2023 South Central Womens Regional
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)