#105 Dreadnought (15-10)

avg: 1187.83  •  sd: 66.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
229 Riverside Messengers-B** Win 11-4 1051.74 Ignored Jul 22nd Riverside Classic 2023
97 Texas Duffy Win 10-9 1407.15 Jul 22nd Riverside Classic 2023
159 Choice City Hops Win 9-8 1009.65 Jul 22nd Riverside Classic 2023
77 BARNSTORM Loss 13-14 1249.75 Jul 23rd Riverside Classic 2023
129 Foxtrot Win 14-12 1263.85 Jul 23rd Riverside Classic 2023
97 Texas Duffy Loss 3-7 682.15 Jul 23rd Riverside Classic 2023
218 Supercell Win 11-6 1079.53 Aug 26th Ragna Rock 2023
214 Meadowlark** Win 11-3 1148.73 Ignored Aug 26th Ragna Rock 2023
237 Arkansas** Win 10-3 968.8 Ignored Aug 26th Ragna Rock 2023
89 Second Nature Loss 7-9 1020.26 Aug 26th Ragna Rock 2023
116 Atlanta Arson Win 11-4 1743.89 Aug 27th Ragna Rock 2023
77 BARNSTORM Loss 5-9 845.69 Aug 27th Ragna Rock 2023
172 Memphis Pharaohs Win 12-5 1425.27 Aug 27th Ragna Rock 2023
77 BARNSTORM Loss 8-12 933.6 Sep 9th 2023 Mens Ozarks Sectional Championship
255 Dreadnought: Battleship [B]** Win 13-3 524.74 Ignored Sep 9th 2023 Mens Ozarks Sectional Championship
218 Supercell Win 12-10 770.96 Sep 9th 2023 Mens Ozarks Sectional Championship
149 Rawhide Loss 9-10 802.02 Sep 10th 2023 Mens Ozarks Sectional Championship
218 Supercell** Win 15-4 1132.83 Ignored Sep 10th 2023 Mens Ozarks Sectional Championship
149 Rawhide Win 15-10 1380.63 Sep 10th 2023 Mens Ozarks Sectional Championship
57 Fungi Loss 10-12 1254.42 Sep 23rd 2023 South Central Mens Regional Championship
69 Clutch Win 14-7 1993.28 Sep 23rd 2023 South Central Mens Regional Championship
54 ISO Atmo Loss 9-10 1394.35 Sep 23rd 2023 South Central Mens Regional Championship
125 Cowtown Cannons Loss 8-10 793.95 Sep 23rd 2023 South Central Mens Regional Championship
98 Riverside Win 10-9 1403.58 Sep 24th 2023 South Central Mens Regional Championship
97 Texas Duffy Loss 8-10 1019.48 Sep 24th 2023 South Central 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)