#170 Spoiler Alert (6-20)

avg: 758.52  •  sd: 77.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
131 Absolute Zero Win 11-8 1331.67 Jun 9th Bay Area Ultimate Classic 2018
74 Alchemy Loss 5-12 665.2 Jun 9th Bay Area Ultimate Classic 2018
37 BW Ultimate Loss 7-9 1221.22 Jun 9th Bay Area Ultimate Classic 2018
67 Firefly Loss 5-12 682.6 Jun 9th Bay Area Ultimate Classic 2018
210 VU Win 11-4 1126.65 Jun 10th Bay Area Ultimate Classic 2018
161 AC Bandits Win 12-7 1320.8 Jun 10th Bay Area Ultimate Classic 2018
119 Buckwild Win 10-4 1631.1 Jun 10th Bay Area Ultimate Classic 2018
61 Donuts Loss 7-15 692.12 Jul 21st Revolution 2018
119 Buckwild Loss 8-11 665.49 Jul 21st Revolution 2018
62 Long Beach Legacy Loss 4-15 691.8 Jul 21st Revolution 2018
65 Family Style Loss 4-9 687.22 Jul 21st Revolution 2018
61 Donuts Loss 8-11 926.52 Jul 22nd Revolution 2018
187 Megalodon Win 10-9 768.4 Jul 22nd Revolution 2018
182 Sebastopol Orchard Loss 6-10 176.11 Jul 22nd Revolution 2018
52 Instant Karma Loss 5-15 745.05 Aug 4th Coconino Classic 2018
47 ROBOS** Loss 3-15 787.34 Ignored Aug 4th Coconino Classic 2018
48 Rubix Loss 7-13 821.3 Aug 4th Coconino Classic 2018
110 California Burrito Loss 4-11 467.08 Aug 5th Coconino Classic 2018
162 Fear and Loathing Loss 9-12 452.53 Aug 5th Coconino Classic 2018
200 Rogue Loss 7-8 447.74 Aug 5th Coconino Classic 2018
162 Fear and Loathing Win 12-11 922.89 Sep 8th So Cal Mixed Sectional Championship 2018
47 ROBOS Loss 7-13 829.81 Sep 8th So Cal Mixed Sectional Championship 2018
48 Rubix Loss 8-13 882.68 Sep 8th So Cal Mixed Sectional Championship 2018
52 Instant Karma Loss 9-12 999.69 Sep 9th So Cal Mixed Sectional Championship 2018
109 Superstition Loss 7-13 514.93 Sep 9th So Cal Mixed Sectional Championship 2018
200 Rogue Loss 8-11 207.13 Sep 9th So Cal Mixed Sectional Championship 2018
**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)