#99 Green Chiles (15-10)

avg: 1061.22  •  sd: 54.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 Kansas City United Loss 5-12 1043.43 Jun 24th Colorado Summer Solstice 2023
33 Mile High Trash Loss 5-11 1042.35 Jun 24th Colorado Summer Solstice 2023
137 The Strangers Win 11-4 1478.93 Jun 24th Colorado Summer Solstice 2023
37 Impact Loss 4-10 950.49 Jun 24th Colorado Summer Solstice 2023
71 Sego Win 10-9 1361.74 Jun 25th Colorado Summer Solstice 2023
133 Karma Win 11-8 1264.16 Jun 25th Colorado Summer Solstice 2023
137 The Strangers Win 11-6 1425.62 Jun 25th Colorado Summer Solstice 2023
95 Space Ghosts Loss 8-13 582.37 Jul 8th Colorado Cup
159 All Jeeps, All Night. Win 12-10 1041.59 Jul 8th Colorado Cup
192 Springs Mixed Greens Win 9-3 1191.15 Jul 8th Colorado Cup
137 The Strangers Win 13-4 1478.93 Jul 8th Colorado Cup
95 Space Ghosts Loss 7-12 558.02 Jul 9th Colorado Cup
159 All Jeeps, All Night. Win 12-10 1041.59 Jul 9th Colorado Cup
108 Garrett's Prom Date Win 11-8 1394.3 Jul 9th Colorado Cup
51 Quick Draw Loss 3-13 786.45 Aug 19th Ski Town Classic 2023
127 Sin Nombre Win 13-12 1077.79 Aug 19th Ski Town Classic 2023
145 Family Style Win 13-10 1167.88 Aug 19th Ski Town Classic 2023
71 Sego Loss 7-12 716.23 Aug 20th Ski Town Classic 2023
145 Family Style Win 11-9 1088.95 Aug 20th Ski Town Classic 2023
36 Tower Loss 7-13 995.41 Aug 20th Ski Town Classic 2023
183 Run Like The Wind Win 14-12 846.98 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
187 Celebrities of Ultimate Win 15-6 1213.13 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
33 Mile High Trash Loss 6-15 1042.35 Sep 9th 2023 Mixed Rocky Mountain Sectional Championship
127 Sin Nombre Win 15-9 1468.27 Sep 10th 2023 Mixed Rocky Mountain Sectional Championship
95 Space Ghosts Loss 10-14 679.83 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)