#101 Triumph (9-13)

avg: 1212.73  •  sd: 48.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Red Wolves Loss 10-12 1109.86 Jun 25th River City Witching Hour
227 Brackish Win 15-8 1041.97 Jun 25th River City Witching Hour
161 MOB Ultimate Win 15-8 1429.59 Jun 25th River City Witching Hour
35 baNC Loss 4-13 1044.34 Jul 22nd Filling the Void 2023
85 Space Cowboys Win 12-11 1437.3 Jul 22nd Filling the Void 2023
213 Stag** Win 13-2 1158.46 Ignored Jul 22nd Filling the Void 2023
40 Cash Crop 2 Loss 13-14 1495.37 Jul 23rd Filling the Void 2023
248 Power Point Ultimate** Win 13-2 737.63 Ignored Jul 23rd Filling the Void 2023
93 Charleston Heat Stroke Loss 7-8 1166.36 Jul 23rd Filling the Void 2023
35 baNC Loss 10-14 1245.63 Aug 12th HoDown Showdown 2023
40 Cash Crop 2 Loss 11-14 1307.04 Aug 12th HoDown Showdown 2023
119 Tennessee Folklore Win 15-11 1481.75 Aug 12th HoDown Showdown 2023
89 Second Nature Win 12-11 1424.6 Aug 12th HoDown Showdown 2023
61 Lost Boys Loss 11-15 1084.77 Aug 13th HoDown Showdown 2023
85 Space Cowboys Loss 13-15 1098.12 Aug 13th HoDown Showdown 2023
40 Cash Crop 2 Loss 9-13 1201.81 Sep 9th 2023 Mens North Carolina Sectional Championship
138 Queen City Kings Win 13-11 1223.99 Sep 9th 2023 Mens North Carolina Sectional Championship
12 Raleigh-Durham United Loss 7-13 1449.09 Sep 9th 2023 Mens North Carolina Sectional Championship
210 Oakay** Win 13-5 1183.18 Ignored Sep 9th 2023 Mens North Carolina Sectional Championship
35 baNC Loss 10-13 1316.19 Sep 10th 2023 Mens North Carolina Sectional Championship
40 Cash Crop 2 Loss 3-15 1020.37 Sep 10th 2023 Mens North Carolina Sectional Championship
138 Queen City Kings Loss 10-14 596.45 Sep 10th 2023 Mens North Carolina 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)