#64 Hooch (16-9)

avg: 1448.57  •  sd: 44.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
37 Alliance Loss 10-11 1502.37 Jul 8th Club Terminus 2023
119 Tennessee Folklore Win 13-5 1700.59 Jul 8th Club Terminus 2023
138 Queen City Kings Win 13-7 1552.68 Jul 8th Club Terminus 2023
42 UpRoar Loss 8-13 1109.16 Jul 9th Club Terminus 2023
56 Little Red Wagon Win 11-10 1622.91 Jul 9th Club Terminus 2023
61 Lost Boys Win 12-10 1704.06 Jul 9th Club Terminus 2023
116 Atlanta Arson Win 9-7 1423.22 Aug 5th Trestlemania V
124 Battleship Win 13-8 1557.08 Aug 5th Trestlemania V
198 Capitol City Chaos Win 11-6 1242.89 Aug 5th Trestlemania V
56 Little Red Wagon Loss 8-12 1056.76 Aug 5th Trestlemania V
37 Alliance Loss 5-5 1627.37 Aug 6th Trestlemania V
93 Charleston Heat Stroke Win 11-9 1540.57 Aug 6th Trestlemania V
56 Little Red Wagon Win 9-8 1622.91 Aug 6th Trestlemania V
119 Tennessee Folklore Win 13-6 1700.59 Sep 9th 2023 Mens East Coast Sectional Championship
150 Nashville Mudcats Win 13-8 1422.36 Sep 9th 2023 Mens East Coast Sectional Championship
56 Little Red Wagon Loss 8-13 1001.75 Sep 9th 2023 Mens East Coast Sectional Championship
167 Pulp** Win 13-4 1438.31 Ignored Sep 9th 2023 Mens East Coast Sectional Championship
116 Atlanta Arson Win 14-9 1617.75 Sep 10th 2023 Mens East Coast Sectional Championship
93 Charleston Heat Stroke Win 13-12 1416.36 Sep 10th 2023 Mens East Coast Sectional Championship
6 Ring of Fire** Loss 1-13 1580.66 Ignored Sep 23rd 2023 Southeast Mens Regional Championship
28 Tanasi Loss 8-11 1366.84 Sep 23rd 2023 Southeast Mens Regional Championship
85 Space Cowboys Win 13-4 1912.3 Sep 23rd 2023 Southeast Mens Regional Championship
138 Queen City Kings Win 15-11 1376.31 Sep 23rd 2023 Southeast Mens Regional Championship
37 Alliance Loss 13-15 1413.2 Sep 24th 2023 Southeast Mens Regional Championship
89 Second Nature Loss 9-11 1050.39 Sep 24th 2023 Southeast Mens Regional 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)