#113 Shipwreck (8-12)

avg: 1021.03  •  sd: 67.31  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
153 DR Win 9-8 945.51 Jul 8th Revolution 2023
172 Nebula Win 9-3 1336.26 Jul 8th Revolution 2023
88 Mango Loss 4-10 525.86 Jul 8th Revolution 2023
59 Grit City Loss 4-9 712.32 Jul 8th Revolution 2023
134 Firefly Win 13-6 1497.39 Jul 9th Revolution 2023
91 Hive Loss 8-12 662.25 Jul 9th Revolution 2023
60 Cutthroat Loss 10-14 911.58 Jul 9th Revolution 2023
69 Robot Loss 9-11 1005.07 Aug 12th Flower Power 2023
102 Party Wave Win 10-9 1178.05 Aug 12th Flower Power 2023
165 Octonauts Win 10-5 1340.13 Aug 12th Flower Power 2023
88 Mango Loss 7-8 1000.86 Aug 12th Flower Power 2023
153 DR Loss 11-13 591.67 Aug 13th Flower Power 2023
80 Flagstaff Ultimate Loss 10-13 848.9 Aug 13th Flower Power 2023
88 Mango Loss 8-13 629.7 Aug 13th Flower Power 2023
69 Robot Win 8-7 1379.28 Sep 9th 2023 Mixed So Cal Sectional Championship
43 California Burrito Loss 6-13 893.76 Sep 9th 2023 Mixed So Cal Sectional Championship
230 Birds of Paradise** Win 13-2 905.44 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
18 Lawless Loss 6-12 1173.25 Sep 9th 2023 Mixed So Cal Sectional Championship
80 Flagstaff Ultimate Win 14-10 1575.74 Sep 10th 2023 Mixed So Cal Sectional Championship
43 California Burrito Loss 3-11 893.76 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)