#197 Springs Mixed Greens (2-15)

avg: 541.33  •  sd: 85.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
21 Love Tractor** Loss 3-15 1107.03 Ignored Jun 24th Colorado Summer Solstice 2023
35 Impact** Loss 4-15 917.09 Ignored Jun 24th Colorado Summer Solstice 2023
139 Karma Loss 7-9 572.63 Jun 24th Colorado Summer Solstice 2023
134 Sin Nombre Loss 6-13 262.83 Jun 25th Colorado Summer Solstice 2023
102 Space Ghosts Loss 8-12 597 Jun 25th Colorado Summer Solstice 2023
157 Mesteño Win 9-5 1316.39 Jun 25th Colorado Summer Solstice 2023
102 Space Ghosts Loss 6-13 438.16 Jul 8th Colorado Cup
166 All Jeeps, All Night. Loss 6-13 161.62 Jul 8th Colorado Cup
117 Garrett's Prom Date Loss 4-13 388.57 Jul 8th Colorado Cup
101 Green Chiles Loss 3-9 444.26 Jul 8th Colorado Cup
144 The Strangers Loss 8-10 566.5 Jul 9th Colorado Cup
144 The Strangers Loss 4-11 229.16 Jul 9th Colorado Cup
134 Sin Nombre Loss 9-10 737.83 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
102 Space Ghosts Loss 4-15 438.16 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
166 All Jeeps, All Night. Win 14-9 1235.49 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
193 Celebrities of Ultimate Loss 11-15 179.03 Sep 10th 2023 Mixed Rocky Mountain Sectional Championship
185 Run Like The Wind Loss 11-12 459.2 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)