#31 Kansas City United (15-7)

avg: 1595.15  •  sd: 67.88  •  top 16/20: 0.2%

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# Opponent Result Game Rating Status Date Event
101 Green Chiles Win 12-5 1644.26 Jun 24th Colorado Summer Solstice 2023
144 The Strangers** Win 12-5 1429.16 Ignored Jun 24th Colorado Summer Solstice 2023
32 Mile High Trash Win 12-9 1908.22 Jun 24th Colorado Summer Solstice 2023
1 shame. Loss 7-13 1609.68 Jun 25th Colorado Summer Solstice 2023
80 Flagstaff Ultimate Win 12-7 1663.51 Jun 25th Colorado Summer Solstice 2023
21 Love Tractor Win 12-10 1945.15 Jun 25th Colorado Summer Solstice 2023
29 RAMP Loss 8-11 1257.14 Jul 29th TCT Select Flight East 2023
47 Darkwing Win 8-7 1544.5 Jul 29th TCT Select Flight East 2023
77 Bullet Train Win 15-4 1762.69 Jul 29th TCT Select Flight East 2023
29 RAMP Win 13-10 1950.89 Jul 30th TCT Select Flight East 2023
5 Cleveland Crocs Loss 9-15 1421.03 Jul 30th TCT Select Flight East 2023
25 MOONDOG Loss 9-12 1302.91 Jul 30th TCT Select Flight East 2023
63 Pegasus Win 13-8 1755.58 Aug 26th Northwest Fruit Bowl 2023
26 Sunshine Loss 12-13 1522.42 Aug 26th Northwest Fruit Bowl 2023
25 MOONDOG Loss 9-13 1229.71 Aug 26th Northwest Fruit Bowl 2023
58 Lights Out Win 13-8 1807.2 Aug 26th Northwest Fruit Bowl 2023
36 BW Ultimate Loss 12-13 1386.22 Aug 27th Northwest Fruit Bowl 2023
58 Lights Out Win 13-8 1807.2 Aug 27th Northwest Fruit Bowl 2023
225 Arms Race** Win 15-6 922.21 Ignored Sep 9th 2023 Mixed West Plains Sectional Championship
212 Chalice Win 15-8 1009.72 Sep 9th 2023 Mixed West Plains Sectional Championship
212 Chalice** Win 15-4 1044.91 Ignored Sep 10th 2023 Mixed West Plains Sectional Championship
35 Impact Win 15-9 2032.58 Sep 10th 2023 Mixed West Plains 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)