#33 Mile High Trash (16-8)

avg: 1642.35  •  sd: 50.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
99 Green Chiles Win 11-5 1661.22 Jun 24th Colorado Summer Solstice 2023
137 The Strangers Win 13-6 1478.93 Jun 24th Colorado Summer Solstice 2023
32 Kansas City United Loss 9-12 1298.06 Jun 24th Colorado Summer Solstice 2023
133 Karma** Win 15-5 1498.56 Ignored Jun 24th Colorado Summer Solstice 2023
1 shame. Loss 8-12 1826.38 Jun 25th Colorado Summer Solstice 2023
28 Flight Club Win 11-9 1927.63 Jun 25th Colorado Summer Solstice 2023
43 California Burrito Win 12-11 1618.76 Jun 25th Colorado Summer Solstice 2023
13 Polar Bears Loss 11-15 1475.29 Jul 8th TCT Pro Elite Challenge West 2023
28 Flight Club Loss 8-11 1312.81 Jul 8th TCT Pro Elite Challenge West 2023
23 MOONDOG Win 11-8 2085.89 Jul 8th TCT Pro Elite Challenge West 2023
11 Seattle Mixtape Loss 10-14 1506.47 Jul 9th TCT Pro Elite Challenge West 2023
79 Bullet Train Win 14-6 1778.25 Jul 9th TCT Pro Elite Challenge West 2023
44 Classy Win 13-12 1584.93 Jul 9th TCT Pro Elite Challenge West 2023
47 Donuts Win 11-10 1568.38 Aug 26th Northwest Fruit Bowl 2023
22 Oregon Scorch Win 13-9 2149.93 Aug 26th Northwest Fruit Bowl 2023
43 California Burrito Win 12-10 1731.88 Aug 26th Northwest Fruit Bowl 2023
21 Sunshine Loss 5-13 1133.69 Aug 27th Northwest Fruit Bowl 2023
44 Classy Win 13-5 2059.93 Aug 27th Northwest Fruit Bowl 2023
26 Loco Loss 10-12 1458.4 Aug 27th Northwest Fruit Bowl 2023
131 Mostly Harmless** Win 15-6 1506.01 Ignored Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
99 Green Chiles Win 15-6 1661.22 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
147 Mesteño** Win 15-4 1438.07 Ignored Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
95 Space Ghosts Win 15-6 1678.53 Sep 10th 2023 Mixed Rocky Mountain Sectional Championship
28 Flight Club Loss 12-14 1457.46 Sep 10th 2023 Mixed Rocky Mountain 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)