#176 Battery (6-11)

avg: 817.3  •  sd: 55.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
70 OAT Loss 6-14 799.46 Jul 15th TCT Select Flight West 2023
87 Ghost Train Loss 7-13 751.87 Jul 15th TCT Select Flight West 2023
36 Kansas City Smokestack** Loss 4-13 1041.9 Ignored Jul 15th TCT Select Flight West 2023
182 Anchor Win 11-10 910.91 Jul 16th TCT Select Flight West 2023
78 Drought Loss 4-15 774.11 Jul 16th TCT Select Flight West 2023
188 Sauce Loss 10-14 350.12 Sep 9th 2023 Mens Nor Cal Sectional Championship
107 Ghost Loss 9-13 763.25 Sep 9th 2023 Mens Nor Cal Sectional Championship
211 Ursa Win 15-6 1175.38 Sep 9th 2023 Mens Nor Cal Sectional Championship
182 Anchor Win 10-9 910.91 Sep 10th 2023 Mens Nor Cal Sectional Championship
188 Sauce Win 15-6 1348.82 Sep 10th 2023 Mens Nor Cal Sectional Championship
107 Ghost Loss 10-15 728.21 Sep 10th 2023 Mens Nor Cal Sectional Championship
66 OC Crows Loss 7-14 854 Sep 23rd 2023 Southwest Mens Regional Championship
240 Bonsoon Win 15-3 850.91 Sep 23rd 2023 Southwest Mens Regional Championship
70 OAT Loss 9-15 883.98 Sep 23rd 2023 Southwest Mens Regional Championship
104 Offshore Loss 10-15 738.57 Sep 24th 2023 Southwest Mens Regional Championship
171 Sonoran Dog Loss 9-13 409.64 Sep 24th 2023 Southwest Mens Regional Championship
240 Bonsoon Win 15-7 850.91 Sep 24th 2023 Southwest 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)