#37 Agency (13-5)

avg: 1149.08  •  sd: 100.22  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
59 Virginia Rebellion Win 12-8 1176.38 Jun 24th Scuffletown Throwdown 2023
94 Dissent** Win 13-4 626.69 Ignored Jun 24th Scuffletown Throwdown 2023
39 Brooklyn Book Club Win 13-5 1683.2 Jun 24th Scuffletown Throwdown 2023
65 Warhawks Win 13-7 1214.26 Jun 24th Scuffletown Throwdown 2023
42 Wave Win 10-8 1300.11 Jun 25th Scuffletown Throwdown 2023
55 Shiver Win 10-8 1133.32 Jun 25th Scuffletown Throwdown 2023
29 Pop Loss 8-15 837.32 Jul 15th TCT Pro Elite Challenge East 2023
1 Scandal** Loss 1-15 2006.7 Ignored Jul 15th TCT Pro Elite Challenge East 2023
22 Siege Loss 3-14 941.48 Jul 15th TCT Pro Elite Challenge East 2023
23 Flight Loss 8-12 1098.25 Jul 16th TCT Pro Elite Challenge East 2023
86 Versa** Win 13-3 763.15 Ignored Aug 5th Philly Open 2023
63 Pine Baroness Win 11-8 1062.27 Aug 5th Philly Open 2023
73 Incline** Win 13-3 1070.29 Ignored Aug 5th Philly Open 2023
47 Vice Win 10-7 1337.92 Aug 6th Philly Open 2023
42 Wave Loss 8-9 912.44 Aug 6th Philly Open 2023
94 Dissent** Win 13-2 626.69 Ignored Aug 6th Philly Open 2023
59 Virginia Rebellion Win 11-7 1202.12 Sep 9th 2023 Womens Capital Sectional Championship
- Pickup Lines** Win 13-1 26.69 Ignored Sep 9th 2023 Womens Capital 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)