#60 Cutthroat (15-10)

avg: 1310.28  •  sd: 67.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Robot Loss 7-10 864.61 Jul 8th Revolution 2023
62 American Barbecue Loss 6-8 989.21 Jul 8th Revolution 2023
41 BW Ultimate Win 11-10 1625.27 Jul 8th Revolution 2023
36 Tower Loss 7-10 1163.27 Jul 8th Revolution 2023
113 Shipwreck Win 14-10 1419.73 Jul 9th Revolution 2023
172 Nebula Win 11-6 1282.96 Jul 9th Revolution 2023
59 Grit City Loss 10-12 1074.2 Jul 9th Revolution 2023
71 Sego Win 13-7 1794.27 Jul 15th TCT Select Flight West 2023
43 California Burrito Loss 5-15 893.76 Jul 15th TCT Select Flight West 2023
54 Lights Out Win 14-10 1741.83 Jul 15th TCT Select Flight West 2023
63 Pegasus Win 12-9 1633.89 Jul 16th TCT Select Flight West 2023
36 Tower Win 11-6 2099.63 Jul 16th TCT Select Flight West 2023
71 Sego Win 13-12 1361.74 Aug 19th Ski Town Classic 2023
133 Karma Win 11-5 1498.56 Aug 19th Ski Town Classic 2023
137 The Strangers Win 9-6 1297.49 Aug 19th Ski Town Classic 2023
145 Family Style Win 13-9 1258.31 Aug 20th Ski Town Classic 2023
36 Tower Loss 10-11 1427.94 Aug 20th Ski Town Classic 2023
101 Igneous Ultimate Win 11-7 1525.27 Aug 20th Ski Town Classic 2023
62 American Barbecue Win 11-9 1538.91 Sep 9th 2023 Mixed Nor Cal Sectional Championship
41 BW Ultimate Loss 8-15 935.46 Sep 9th 2023 Mixed Nor Cal Sectional Championship
221 Moonlight Ultimate** Win 13-3 966.77 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
15 Mischief Loss 6-13 1222.59 Sep 9th 2023 Mixed Nor Cal Sectional Championship
47 Donuts Loss 6-15 843.38 Sep 10th 2023 Mixed Nor Cal Sectional Championship
62 American Barbecue Loss 6-11 743.01 Sep 10th 2023 Mixed Nor Cal Sectional Championship
88 Mango Win 14-10 1524.56 Sep 10th 2023 Mixed Nor Cal 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)