#134 Dyno (7-17)

avg: 1026.16  •  sd: 54.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
37 Alliance** Loss 3-11 1027.37 Ignored Jun 24th Huntsville Huckfest
89 Second Nature Loss 4-11 699.6 Jun 24th Huntsville Huckfest
198 Capitol City Chaos Win 11-3 1296.2 Jun 24th Huntsville Huckfest
144 Music City Mafia Loss 9-10 840.1 Jun 24th Huntsville Huckfest
172 Memphis Pharaohs Loss 6-10 329.11 Jun 25th Huntsville Huckfest
56 Little Red Wagon Loss 5-11 897.91 Jun 25th Huntsville Huckfest
118 Raptor Loss 9-10 986.59 Jun 25th Huntsville Huckfest
93 Charleston Heat Stroke Loss 11-13 1062.52 Jul 8th Club Terminus 2023
56 Little Red Wagon Loss 3-13 897.91 Jul 8th Club Terminus 2023
61 Lost Boys Loss 8-13 969.78 Jul 8th Club Terminus 2023
120 El Niño Loss 10-11 966.7 Jul 9th Club Terminus 2023
138 Queen City Kings Win 10-8 1257.81 Jul 9th Club Terminus 2023
119 Tennessee Folklore Win 11-10 1225.59 Jul 9th Club Terminus 2023
37 Alliance** Loss 5-13 1027.37 Ignored Aug 5th Trestlemania V
150 Nashville Mudcats Win 10-3 1526.2 Aug 5th Trestlemania V
144 Music City Mafia Win 10-8 1227.77 Aug 5th Trestlemania V
93 Charleston Heat Stroke Loss 9-10 1166.36 Aug 5th Trestlemania V
116 Atlanta Arson Loss 5-10 569.99 Aug 6th Trestlemania V
93 Charleston Heat Stroke Loss 7-9 1012.03 Aug 6th Trestlemania V
85 Space Cowboys Loss 6-13 712.3 Sep 9th 2023 Mens East Coast Sectional Championship
248 Power Point Ultimate** Win 13-2 737.63 Ignored Sep 9th 2023 Mens East Coast Sectional Championship
144 Music City Mafia Win 13-5 1565.1 Sep 9th 2023 Mens East Coast Sectional Championship
116 Atlanta Arson Loss 11-14 830.55 Sep 10th 2023 Mens East Coast Sectional Championship
93 Charleston Heat Stroke Loss 14-15 1166.36 Sep 10th 2023 Mens East Coast 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)