#15 Iris (12-7)

avg: 1747.14  •  sd: 82.23  •  top 16/20: 69.1%

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# Opponent Result Game Rating Status Date Event
48 Venus Win 13-6 1546.23 Jul 15th Boston Invite 2023
63 Pine Baroness** Win 13-5 1296.66 Ignored Jul 15th Boston Invite 2023
- Rip Tide** Win 13-2 1171.65 Ignored Jul 15th Boston Invite 2023
- Vintage Win 13-6 1761.15 Jul 15th Boston Invite 2023
42 Wave** Win 8-3 1637.44 Ignored Jul 16th Boston Invite 2023
22 Siege Loss 10-11 1416.48 Aug 12th Log Jam
18 Starling Ultimate Loss 7-10 1284.83 Aug 12th Log Jam
22 Siege Win 13-8 2037.64 Aug 12th Log Jam
18 Starling Ultimate Loss 10-11 1549.5 Aug 12th Log Jam
18 Starling Ultimate Win 11-6 2221.19 Aug 13th Log Jam
69 PLOW** Win 15-1 1140.55 Ignored Sep 9th 2023 Womens West New England Sectional Championship
18 Starling Ultimate Loss 9-11 1425.29 Sep 9th 2023 Womens West New England Sectional Championship
8 6ixers Loss 8-15 1541.12 Sep 23rd 2023 Northeast Womens Regional Championship
74 Frolic** Win 13-5 1058.73 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
18 Starling Ultimate Win 13-10 2002.64 Sep 23rd 2023 Northeast Womens Regional Championship
22 Siege Win 15-9 2056.96 Sep 23rd 2023 Northeast Womens Regional Championship
8 6ixers Loss 11-15 1724.77 Sep 24th 2023 Northeast Womens Regional Championship
5 Brute Squad Loss 11-15 1921.66 Sep 24th 2023 Northeast Womens Regional Championship
18 Starling Ultimate Win 13-12 1799.5 Sep 24th 2023 Northeast 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)