#124 Battleship (9-10)

avg: 1060.92  •  sd: 55.61  •  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 Loss 10-11 801.2 Jun 24th Huntsville Huckfest
118 Raptor Win 11-9 1360.8 Jun 24th Huntsville Huckfest
129 Foxtrot Win 10-8 1305.56 Jun 24th Huntsville Huckfest
116 Atlanta Arson Loss 8-9 1018.89 Jun 25th Huntsville Huckfest
207 Villains Win 11-8 965.54 Jun 25th Huntsville Huckfest
144 Music City Mafia Loss 6-11 418.4 Jun 25th Huntsville Huckfest
116 Atlanta Arson Loss 9-11 894.68 Aug 5th Trestlemania V
56 Little Red Wagon Loss 9-13 1079.35 Aug 5th Trestlemania V
198 Capitol City Chaos Win 10-8 958.86 Aug 5th Trestlemania V
64 Hooch Loss 8-13 952.41 Aug 5th Trestlemania V
150 Nashville Mudcats Loss 11-12 801.2 Aug 6th Trestlemania V
37 Alliance Loss 7-13 1069.84 Sep 9th 2023 Mens Gulf Coast Sectional Championship
172 Memphis Pharaohs Win 11-8 1190.88 Sep 9th 2023 Mens Gulf Coast Sectional Championship
198 Capitol City Chaos Win 12-9 1041.56 Sep 9th 2023 Mens Gulf Coast Sectional Championship
89 Second Nature Loss 8-13 803.44 Sep 9th 2023 Mens Gulf Coast Sectional Championship
146 Ronin Win 13-10 1279.64 Sep 10th 2023 Mens Gulf Coast Sectional Championship
146 Ronin Win 15-8 1516.31 Sep 10th 2023 Mens Gulf Coast Sectional Championship
89 Second Nature Loss 10-12 1061.48 Sep 10th 2023 Mens Gulf 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)