#11 Seattle Mixtape (16-6)

avg: 1874.03  •  sd: 58.5  •  top 16/20: 92.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
23 Oregon Scorch Loss 8-15 1111.95 Jul 8th TCT Pro Elite Challenge West 2023
28 Flight Club Win 14-10 2033.97 Jul 8th TCT Pro Elite Challenge West 2023
25 MOONDOG Loss 7-11 1181.39 Jul 8th TCT Pro Elite Challenge West 2023
32 Mile High Trash Win 14-10 1961.55 Jul 9th TCT Pro Elite Challenge West 2023
51 Classy Win 14-11 1697.14 Jul 9th TCT Pro Elite Challenge West 2023
77 Bullet Train** Win 15-5 1762.69 Ignored Jul 9th TCT Pro Elite Challenge West 2023
3 AMP Win 15-12 2368.34 Aug 4th 2023 US Open Club Championships ICC
7 XIST Loss 11-13 1692.02 Aug 4th 2023 US Open Club Championships ICC
1 shame. Loss 12-15 1866.72 Aug 5th 2023 US Open Club Championships ICC
7 XIST Loss 10-11 1795.86 Aug 6th 2023 US Open Club Championships ICC
39 Lotus Win 13-9 1913.6 Aug 26th Northwest Fruit Bowl 2023
63 Pegasus Win 13-8 1755.58 Aug 26th Northwest Fruit Bowl 2023
51 Classy Win 13-6 1983.8 Aug 26th Northwest Fruit Bowl 2023
26 Sunshine Win 13-6 2247.42 Aug 27th Northwest Fruit Bowl 2023
24 Loco Win 13-12 1795.85 Aug 27th Northwest Fruit Bowl 2023
25 MOONDOG Win 13-11 1877.12 Aug 27th Northwest Fruit Bowl 2023
125 Garage Sale Win 15-7 1533.16 Sep 23rd 2023 Northwest Mixed Regional Championship
34 Spoke Win 15-10 1997.94 Sep 23rd 2023 Northwest Mixed Regional Championship
4 BFG Loss 13-14 1834.6 Sep 23rd 2023 Northwest Mixed Regional Championship
100 Igneous Ultimate** Win 15-6 1645.47 Ignored Sep 23rd 2023 Northwest Mixed Regional Championship
10 Red Flag Win 15-11 2257.38 Sep 24th 2023 Northwest Mixed Regional Championship
4 BFG Win 14-13 2084.6 Sep 24th 2023 Northwest 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)