#18 Starling Ultimate (15-4)

avg: 1674.5  •  sd: 88.75  •  top 16/20: 35.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Autonomous** Win 15-0 876.44 Ignored Jul 29th TCT Select Flight East 2023
54 Stellar** Win 13-5 1476.94 Ignored Jul 29th TCT Select Flight East 2023
55 Shiver** Win 15-3 1470.65 Ignored Jul 29th TCT Select Flight East 2023
41 Heist** Win 15-4 1641.1 Ignored Jul 30th TCT Select Flight East 2023
31 Rival Win 13-10 1694.58 Jul 30th TCT Select Flight East 2023
22 Siege Win 14-12 1762.44 Jul 30th TCT Select Flight East 2023
15 Iris Win 10-7 2136.8 Aug 12th Log Jam
15 Iris Win 11-10 1872.14 Aug 12th Log Jam
22 Siege Win 10-9 1666.48 Aug 12th Log Jam
15 Iris Loss 6-11 1200.44 Aug 13th Log Jam
15 Iris Win 11-9 1996.35 Sep 9th 2023 Womens West New England Sectional Championship
69 PLOW** Win 15-0 1140.55 Ignored Sep 9th 2023 Womens West New England Sectional Championship
7 BENT Loss 7-15 1531.43 Sep 23rd 2023 Northeast Womens Regional Championship
74 Frolic** Win 13-4 1058.73 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
15 Iris Loss 10-13 1419 Sep 23rd 2023 Northeast Womens Regional Championship
69 PLOW** Win 15-1 1140.55 Ignored Sep 23rd 2023 Northeast Womens Regional Championship
47 Vice Win 15-7 1548.25 Sep 24th 2023 Northeast Womens Regional Championship
15 Iris Loss 12-13 1622.14 Sep 24th 2023 Northeast Womens Regional Championship
22 Siege Win 14-12 1762.44 Sep 24th 2023 Northeast Womens 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)