#160 Spoiler Alert (8-10)

avg: 785.69  •  sd: 65.82  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
67 Robot Loss 6-9 820.42 Jul 8th Revolution 2023
138 Firefly Loss 4-7 357.53 Jul 8th Revolution 2023
181 VU Loss 8-9 505.66 Jul 8th Revolution 2023
161 DR Loss 8-10 521.95 Jul 9th Revolution 2023
224 Moonlight Ultimate Win 14-4 927.66 Jul 9th Revolution 2023
121 Party Wave Loss 5-8 514.29 Aug 12th Flower Power 2023
202 Air Throwmads Win 10-9 640.63 Aug 12th Flower Power 2023
94 Mango Loss 8-9 952.18 Aug 12th Flower Power 2023
80 Flagstaff Ultimate Loss 3-12 542.99 Aug 12th Flower Power 2023
247 Erosion** Win 13-5 635.95 Ignored Aug 13th Flower Power 2023
230 Birds of Paradise Win 10-6 761.21 Aug 13th Flower Power 2023
224 Moonlight Ultimate Win 12-5 927.66 Aug 13th Flower Power 2023
247 Erosion** Win 15-4 635.95 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
17 Lawless** Loss 5-13 1163.19 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
152 Family Style Loss 8-11 430.04 Sep 9th 2023 Mixed So Cal Sectional Championship
67 Robot Loss 7-10 849.32 Sep 10th 2023 Mixed So Cal Sectional Championship
169 Octonauts Win 12-5 1331.99 Sep 10th 2023 Mixed So Cal Sectional Championship
80 Flagstaff Ultimate Win 9-8 1267.99 Sep 10th 2023 Mixed So 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)