#55 Shiver (11-15)

avg: 870.65  •  sd: 61.53  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
42 Wave Loss 10-11 912.44 Jun 24th Scuffletown Throwdown 2023
39 Brooklyn Book Club Loss 8-11 717.59 Jun 24th Scuffletown Throwdown 2023
94 Dissent Win 13-7 584.22 Jun 24th Scuffletown Throwdown 2023
65 Warhawks Win 7-4 1152.89 Jun 24th Scuffletown Throwdown 2023
59 Virginia Rebellion Loss 7-8 610.23 Jun 25th Scuffletown Throwdown 2023
37 Agency Loss 8-10 886.41 Jun 25th Scuffletown Throwdown 2023
39 Brooklyn Book Club Loss 4-7 587.04 Jun 25th Scuffletown Throwdown 2023
66 Banshee Loss 8-11 260.35 Jul 29th TCT Select Flight East 2023
18 Starling Ultimate** Loss 3-15 1074.5 Ignored Jul 29th TCT Select Flight East 2023
31 Rival Loss 5-10 792.54 Jul 29th TCT Select Flight East 2023
43 Zephyr Loss 9-11 776.08 Jul 30th TCT Select Flight East 2023
83 Autonomous Win 14-2 876.44 Jul 30th TCT Select Flight East 2023
50 Drift Loss 8-9 789.94 Jul 30th TCT Select Flight East 2023
75 Calypso Win 14-6 1054.85 Aug 12th HoDown Showdown 2023
72 KnoxFusion Win 12-6 1072.51 Aug 12th HoDown Showdown 2023
67 Magma Win 12-8 1039.3 Aug 12th HoDown Showdown 2023
46 San Antonio Problems Loss 8-12 518.15 Aug 12th HoDown Showdown 2023
75 Calypso Win 13-6 1054.85 Aug 13th HoDown Showdown 2023
44 Juice Box Win 10-7 1378.1 Aug 13th HoDown Showdown 2023
46 San Antonio Problems Win 11-9 1208.51 Aug 13th HoDown Showdown 2023
44 Juice Box Loss 10-12 750.32 Sep 9th 2023 Womens North Carolina Sectional Championship
75 Calypso Win 12-3 1054.85 Sep 23rd 2023 Southeast Womens Regional Championship
67 Magma Win 9-6 1016.71 Sep 23rd 2023 Southeast Womens Regional Championship
2 Phoenix** Loss 1-13 1879.74 Ignored Sep 23rd 2023 Southeast Womens Regional Championship
30 Tabby Rosa Loss 7-11 917.34 Sep 23rd 2023 Southeast Womens Regional Championship
35 Huntsville Laika Loss 7-13 615.06 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)