#44 Juice Box (13-7)

avg: 988.44  •  sd: 55.09  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
75 Calypso Win 13-3 1054.85 Jul 8th Club Terminus 2023
17 Ozone** Loss 5-13 1086.4 Ignored Jul 8th Club Terminus 2023
67 Magma Win 9-7 877.48 Jul 8th Club Terminus 2023
35 Huntsville Laika Loss 8-9 1047.59 Jul 9th Club Terminus 2023
61 Steel Win 12-5 1313.62 Jul 9th Club Terminus 2023
67 Magma Win 11-8 963.75 Jul 9th Club Terminus 2023
75 Calypso Win 13-6 1054.85 Aug 12th HoDown Showdown 2023
67 Magma Win 15-6 1198.14 Aug 12th HoDown Showdown 2023
72 KnoxFusion Win 15-5 1093.2 Aug 12th HoDown Showdown 2023
46 San Antonio Problems Win 14-8 1495.34 Aug 12th HoDown Showdown 2023
75 Calypso Win 9-8 579.85 Aug 13th HoDown Showdown 2023
55 Shiver Loss 7-10 480.99 Aug 13th HoDown Showdown 2023
46 San Antonio Problems Loss 10-15 505.7 Aug 13th HoDown Showdown 2023
55 Shiver Win 12-10 1108.78 Sep 9th 2023 Womens North Carolina Sectional Championship
58 Fiasco Win 12-11 914.62 Sep 23rd 2023 Southeast Womens Regional Championship
35 Huntsville Laika Loss 9-12 827.23 Sep 23rd 2023 Southeast Womens Regional Championship
17 Ozone** Loss 0-13 1086.4 Ignored Sep 23rd 2023 Southeast Womens Regional Championship
75 Calypso Win 15-7 1054.85 Sep 24th 2023 Southeast Womens Regional Championship
30 Tabby Rosa Loss 11-15 1003.07 Sep 24th 2023 Southeast Womens Regional Championship
67 Magma Win 13-3 1198.14 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)