#21 LOL (18-3)

avg: 1559.51  •  sd: 133.39  •  top 16/20: 12%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
100 Just Add Water** Win 15-2 418.85 Ignored Jun 10th Bay Area Ultimate Classic 2023
98 Ultraviolet** Win 15-0 511.22 Ignored Jun 10th Bay Area Ultimate Classic 2023
52 Void Cat Rewind** Win 15-5 1493.58 Ignored Jun 11th Bay Area Ultimate Classic 2023
99 Off Their Rockers** Win 15-2 441.59 Ignored Jun 11th Bay Area Ultimate Classic 2023
89 Tempo** Win 15-3 738.45 Ignored Jun 11th Bay Area Ultimate Classic 2023
68 Venom** Win 15-0 1155.5 Ignored Jul 15th TCT Select Flight West 2023
87 Haboob** Win 15-5 756.71 Ignored Jul 15th TCT Select Flight West 2023
52 Void Cat Rewind** Win 15-4 1493.58 Ignored Jul 16th TCT Select Flight West 2023
20 Wildfire Win 7-6 1685.61 Jul 16th TCT Select Flight West 2023
38 FAB Win 15-10 1572.87 Jul 16th TCT Select Flight West 2023
98 Ultraviolet** Win 15-1 511.22 Ignored Sep 9th 2023 Womens NorCal Sectional Championship
99 Off Their Rockers** Win 15-1 441.59 Ignored Sep 9th 2023 Womens NorCal Sectional Championship
52 Void Cat Rewind** Win 15-5 1493.58 Ignored Sep 10th 2023 Womens NorCal Sectional Championship
38 FAB Win 15-8 1684.07 Sep 10th 2023 Womens NorCal Sectional Championship
68 Venom** Win 13-1 1155.5 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
52 Void Cat Rewind Win 13-6 1493.58 Sep 23rd 2023 Southwest Womens Regional Championship
38 FAB Win 13-8 1615.42 Sep 23rd 2023 Southwest Womens Regional Championship
3 Fury** Loss 4-13 1854.65 Ignored Sep 23rd 2023 Southwest Womens Regional Championship
20 Wildfire Loss 12-14 1339.66 Sep 24th 2023 Southwest Womens Regional Championship
38 FAB Win 15-11 1500.43 Sep 24th 2023 Southwest Womens Regional Championship
6 Flipside Loss 7-15 1667.63 Sep 24th 2023 Southwest Womens Regional Championship
**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)