#144 The Strangers (9-15)

avg: 829.16  •  sd: 71.66  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
101 Green Chiles Loss 4-11 444.26 Jun 24th Colorado Summer Solstice 2023
32 Mile High Trash Loss 6-13 962.85 Jun 24th Colorado Summer Solstice 2023
31 Kansas City United** Loss 5-12 995.15 Ignored Jun 24th Colorado Summer Solstice 2023
134 Sin Nombre Win 12-7 1383.34 Jun 25th Colorado Summer Solstice 2023
102 Space Ghosts Win 11-7 1505.05 Jun 25th Colorado Summer Solstice 2023
101 Green Chiles Loss 6-11 497.56 Jun 25th Colorado Summer Solstice 2023
102 Space Ghosts Win 12-10 1276.28 Jul 8th Colorado Cup
166 All Jeeps, All Night. Loss 5-10 187.72 Jul 8th Colorado Cup
117 Garrett's Prom Date Loss 9-13 570 Jul 8th Colorado Cup
101 Green Chiles Loss 4-13 444.26 Jul 8th Colorado Cup
197 Springs Mixed Greens Win 10-8 803.99 Jul 9th Colorado Cup
197 Springs Mixed Greens Win 11-4 1141.33 Jul 9th Colorado Cup
102 Space Ghosts Win 11-10 1163.16 Aug 19th Ski Town Classic 2023
75 Cutthroat Loss 6-9 768.38 Aug 19th Ski Town Classic 2023
139 Karma Loss 3-9 251.96 Aug 19th Ski Town Classic 2023
53 Quick Draw Loss 8-13 854.39 Aug 20th Ski Town Classic 2023
74 Sego Loss 6-13 591.34 Aug 20th Ski Town Classic 2023
152 Family Style Loss 10-11 670.65 Aug 20th Ski Town Classic 2023
134 Sin Nombre Loss 12-15 562.34 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
166 All Jeeps, All Night. Win 10-6 1257.78 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
28 Flight Club Loss 7-15 1035.27 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
166 All Jeeps, All Night. Win 13-11 990.46 Sep 10th 2023 Mixed Rocky Mountain Sectional Championship
157 Mesteño Loss 11-13 558.49 Sep 10th 2023 Mixed Rocky Mountain Sectional Championship
185 Run Like The Wind Win 12-8 1025.36 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)