#69 Robot (14-6)

avg: 1254.28  •  sd: 74.23  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
151 Spoiler Alert Win 9-6 1243.15 Jul 8th Revolution 2023
134 Firefly Win 11-4 1497.39 Jul 8th Revolution 2023
60 Cutthroat Win 10-7 1699.95 Jul 8th Revolution 2023
21 Sunshine Loss 8-10 1471.02 Jul 9th Revolution 2023
47 Donuts Loss 10-11 1318.38 Jul 9th Revolution 2023
88 Mango Loss 8-11 760.25 Jul 9th Revolution 2023
113 Shipwreck Win 11-9 1270.23 Aug 12th Flower Power 2023
165 Octonauts Win 8-7 891.23 Aug 12th Flower Power 2023
102 Party Wave Win 12-6 1632.36 Aug 12th Flower Power 2023
80 Flagstaff Ultimate Win 10-6 1673.2 Aug 12th Flower Power 2023
153 DR Win 14-5 1420.51 Aug 13th Flower Power 2023
134 Firefly Win 15-5 1497.39 Aug 13th Flower Power 2023
35 LIT Ultimate Loss 9-11 1313.87 Aug 13th Flower Power 2023
- Altitude [B]** Win 13-2 371.19 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
80 Flagstaff Ultimate Win 12-11 1302.04 Sep 9th 2023 Mixed So Cal Sectional Championship
113 Shipwreck Loss 7-8 896.03 Sep 9th 2023 Mixed So Cal Sectional Championship
165 Octonauts Win 11-8 1131.84 Sep 9th 2023 Mixed So Cal Sectional Championship
43 California Burrito Loss 6-15 893.76 Sep 10th 2023 Mixed So Cal Sectional Championship
133 Karma Win 8-7 1023.56 Sep 10th 2023 Mixed So Cal Sectional Championship
151 Spoiler Alert Win 10-7 1214.25 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)