#71 ISO Atmo (14-5)

avg: 1209.37  •  sd: 59.26  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
64 (washed) Loss 12-15 1005.51 May 31st Cutthroat Round Robin 2025
179 Colorado Cutthroat U-17 Boys** Win 15-4 688.22 Ignored May 31st Cutthroat Round Robin 2025
94 Colorado Cutthroat U-20 Boys Win 13-11 1256.11 May 31st Cutthroat Round Robin 2025
66 Red Lotus Win 15-11 1630.89 May 31st Cutthroat Round Robin 2025
183 Colorado Cutthroat U-20 Boys TWO** Win 15-5 588.28 Ignored Jun 1st Cutthroat Round Robin 2025
121 The Incline Win 12-10 1026.22 Jun 1st Cutthroat Round Robin 2025
126 Nomads Win 13-5 1354.1 Jun 28th Colorado Summer Solstice Part 2
149 Reál Hogs FC [B]** Win 13-5 1136.05 Ignored Jun 28th Colorado Summer Solstice Part 2
97 Tugboat Win 13-11 1226.84 Jun 28th Colorado Summer Solstice Part 2
66 Red Lotus Loss 13-15 1035.55 Jun 29th Colorado Summer Solstice Part 2
94 Colorado Cutthroat U-20 Boys Win 12-11 1152.27 Jun 29th Colorado Summer Solstice Part 2
90 PowderHogs Loss 13-15 866.98 Jun 29th Colorado Summer Solstice Part 2
146 Rubicon Rapids** Win 15-5 1157.53 Ignored Jul 12th Heavyweights 2025
166 Scoop Win 15-7 982 Jul 12th Heavyweights 2025
137 STL Moonar Win 15-6 1236.1 Jul 12th Heavyweights 2025
98 Knights of Ni Win 15-9 1509.75 Jul 12th Heavyweights 2025
76 Zoboomafoo Win 15-12 1444.45 Jul 13th Heavyweights 2025
69 Trident Loss 11-12 1104.43 Jul 13th Heavyweights 2025
47 Tanasi Loss 12-14 1289.66 Jul 13th Heavyweights 2025
**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)