#26 Sunshine (18-5)

avg: 1647.42  •  sd: 64.83  •  top 16/20: 1.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
49 Donuts Win 11-9 1645.72 Jul 8th Revolution 2023
37 LIT Ultimate Win 9-6 1927.77 Jul 8th Revolution 2023
90 Hive Win 10-6 1584.53 Jul 8th Revolution 2023
67 Robot Win 10-8 1501.66 Jul 9th Revolution 2023
33 Tower Win 11-6 2095.07 Jul 9th Revolution 2023
36 BW Ultimate Win 15-6 2111.22 Jul 9th Revolution 2023
25 MOONDOG Loss 10-13 1320.14 Aug 26th Northwest Fruit Bowl 2023
31 Kansas City United Win 13-12 1720.15 Aug 26th Northwest Fruit Bowl 2023
58 Lights Out Win 10-9 1436.04 Aug 26th Northwest Fruit Bowl 2023
10 Red Flag Loss 5-13 1276.21 Aug 27th Northwest Fruit Bowl 2023
11 Seattle Mixtape Loss 6-13 1274.03 Aug 27th Northwest Fruit Bowl 2023
32 Mile High Trash Win 13-5 2162.85 Aug 27th Northwest Fruit Bowl 2023
215 Quails** Win 13-2 1019.03 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
49 Donuts Win 12-7 1917.03 Sep 9th 2023 Mixed Nor Cal Sectional Championship
94 Mango Win 11-7 1544.07 Sep 9th 2023 Mixed Nor Cal Sectional Championship
37 LIT Ultimate Win 13-12 1634.2 Sep 9th 2023 Mixed Nor Cal Sectional Championship
15 Mischief Win 14-11 2144.22 Sep 10th 2023 Mixed Nor Cal Sectional Championship
67 Robot Win 14-10 1637.69 Sep 23rd 2023 Southwest Mixed Regional Championship
41 California Burrito Win 12-10 1726.35 Sep 23rd 2023 Southwest Mixed Regional Championship
17 Lawless Loss 8-13 1267.03 Sep 23rd 2023 Southwest Mixed Regional Championship
36 BW Ultimate Win 14-13 1636.22 Sep 24th 2023 Southwest Mixed Regional Championship
33 Tower Win 15-13 1762.55 Sep 24th 2023 Southwest Mixed Regional Championship
17 Lawless Loss 10-15 1309.58 Sep 24th 2023 Southwest Mixed 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)