#116 Atlanta Arson (16-12)

avg: 1143.89  •  sd: 55.96  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
146 Ronin Win 11-7 1418.39 Jun 24th Huntsville Huckfest
150 Nashville Mudcats Win 11-3 1526.2 Jun 24th Huntsville Huckfest
207 Villains Win 11-4 1199.94 Jun 24th Huntsville Huckfest
118 Raptor Win 11-3 1711.59 Jun 24th Huntsville Huckfest
37 Alliance Loss 5-11 1027.37 Jun 25th Huntsville Huckfest
124 Battleship Win 9-8 1185.92 Jun 25th Huntsville Huckfest
129 Foxtrot Win 11-9 1292.1 Jun 25th Huntsville Huckfest
150 Nashville Mudcats Win 4-4 926.2 Jun 25th Huntsville Huckfest
124 Battleship Win 11-9 1310.13 Aug 5th Trestlemania V
64 Hooch Loss 7-9 1169.23 Aug 5th Trestlemania V
56 Little Red Wagon Win 8-7 1622.91 Aug 5th Trestlemania V
198 Capitol City Chaos Win 11-10 821.2 Aug 5th Trestlemania V
37 Alliance Loss 4-12 1027.37 Aug 6th Trestlemania V
134 Dyno Win 10-5 1600.05 Aug 6th Trestlemania V
56 Little Red Wagon Loss 2-5 897.91 Aug 6th Trestlemania V
77 BARNSTORM Loss 6-11 828.06 Aug 26th Ragna Rock 2023
149 Rawhide Win 10-6 1423.18 Aug 26th Ragna Rock 2023
172 Memphis Pharaohs Win 13-1 1425.27 Aug 26th Ragna Rock 2023
103 Scythe Win 10-8 1465.75 Aug 27th Ragna Rock 2023
89 Second Nature Loss 5-9 770.54 Aug 27th Ragna Rock 2023
105 Dreadnought Loss 4-11 587.83 Aug 27th Ragna Rock 2023
119 Tennessee Folklore Win 12-11 1225.59 Sep 9th 2023 Mens East Coast Sectional Championship
28 Tanasi Loss 4-13 1132.45 Sep 9th 2023 Mens East Coast Sectional Championship
30 Delirium Loss 5-12 1078.68 Sep 9th 2023 Mens East Coast Sectional Championship
100 Python Loss 8-11 848.41 Sep 9th 2023 Mens East Coast Sectional Championship
134 Dyno Win 14-11 1339.49 Sep 10th 2023 Mens East Coast Sectional Championship
64 Hooch Loss 9-14 974.7 Sep 10th 2023 Mens East Coast Sectional Championship
93 Charleston Heat Stroke Loss 7-15 691.36 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)