#100 Just Add Water (1-20)

avg: -181.15  •  sd: 137.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 FAB** Loss 1-15 519.26 Ignored Jun 10th Bay Area Ultimate Classic 2023
21 LOL** Loss 2-15 959.51 Ignored Jun 10th Bay Area Ultimate Classic 2023
98 Ultraviolet Loss 8-11 -454.39 Jun 10th Bay Area Ultimate Classic 2023
52 Void Cat Rewind** Loss 5-15 293.58 Ignored Jun 11th Bay Area Ultimate Classic 2023
89 Tempo Win 11-8 504.06 Jun 11th Bay Area Ultimate Classic 2023
99 Off Their Rockers Loss 7-8 -283.41 Jun 11th Bay Area Ultimate Classic 2023
38 FAB** Loss 1-11 519.26 Ignored Jul 15th TCT Select Flight West 2023
71 Jackwagon Loss 5-9 -31.34 Jul 15th TCT Select Flight West 2023
45 Rampage** Loss 0-11 373.75 Ignored Jul 15th TCT Select Flight West 2023
20 Wildfire** Loss 2-11 960.61 Ignored Jul 16th TCT Select Flight West 2023
87 Haboob Loss 5-12 -443.29 Jul 16th TCT Select Flight West 2023
19 Dark Sky** Loss 0-13 981.24 Ignored Aug 19th Ski Town Classic 2023
58 Fiasco** Loss 0-12 189.62 Ignored Aug 19th Ski Town Classic 2023
51 Seven Devils** Loss 2-13 307.19 Ignored Aug 19th Ski Town Classic 2023
87 Haboob Loss 8-11 -208.9 Aug 20th Ski Town Classic 2023
84 Seattle Soul Loss 8-9 144.19 Aug 20th Ski Town Classic 2023
79 Swell Loss 5-12 -250.35 Aug 20th Ski Town Classic 2023
68 Venom** Loss 2-10 -44.5 Ignored Sep 9th 2023 Womens SoCal Sectional Championship
20 Wildfire** Loss 0-13 960.61 Ignored Sep 9th 2023 Womens SoCal Sectional Championship
87 Haboob Loss 6-13 -443.29 Sep 9th 2023 Womens SoCal Sectional Championship
45 Rampage** Loss 1-13 373.75 Ignored Sep 9th 2023 Womens SoCal Sectional 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)