#58 Fiasco (7-12)

avg: 789.62  •  sd: 86.93  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
78 cATLanta Win 9-5 888.28 Jul 8th Club Terminus 2023
61 Steel Win 9-5 1242.68 Jul 8th Club Terminus 2023
35 Huntsville Laika Loss 5-12 572.59 Jul 8th Club Terminus 2023
75 Calypso Win 11-7 921.74 Jul 9th Club Terminus 2023
67 Magma Loss 7-8 473.14 Jul 9th Club Terminus 2023
78 cATLanta Loss 8-9 234.22 Jul 9th Club Terminus 2023
19 Dark Sky Loss 7-11 1114.35 Aug 19th Ski Town Classic 2023
100 Just Add Water** Win 12-0 418.85 Ignored Aug 19th Ski Town Classic 2023
87 Haboob Win 8-5 610.32 Aug 19th Ski Town Classic 2023
32 Crush City Loss 8-10 1050.73 Aug 20th Ski Town Classic 2023
45 Rampage Loss 4-10 373.75 Aug 20th Ski Town Classic 2023
49 Trainwreck Loss 7-8 809.06 Aug 20th Ski Town Classic 2023
75 Calypso Win 12-11 579.85 Sep 9th 2023 Womens Florida Sectional Championship
30 Tabby Rosa Loss 7-10 994.57 Sep 9th 2023 Womens Florida Sectional Championship
35 Huntsville Laika Loss 3-14 572.59 Sep 23rd 2023 Southeast Womens Regional Championship
17 Ozone** Loss 1-15 1086.4 Ignored Sep 23rd 2023 Southeast Womens Regional Championship
44 Juice Box Loss 11-12 863.44 Sep 23rd 2023 Southeast Womens Regional Championship
2 Phoenix** Loss 2-15 1879.74 Ignored Sep 24th 2023 Southeast Womens Regional Championship
67 Magma Win 12-7 1118.65 Sep 24th 2023 Southeast 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)