#239 SkyLab (1-16)

avg: 180.19  •  sd: 83.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
190 Drip Loss 4-13 5.37 Jun 24th Buried In Disc Satisfaction BIDS
101 Igneous Ultimate** Loss 2-13 458.37 Ignored Jun 24th Buried In Disc Satisfaction BIDS
224 Night Cap Loss 7-13 -218.66 Jun 24th Buried In Disc Satisfaction BIDS
172 Nebula Loss 6-13 136.26 Jun 24th Buried In Disc Satisfaction BIDS
77 Seattle Soft Serve** Loss 0-11 588.81 Ignored Aug 12th Kleinman Eruption 2023
109 Garbage** Loss 3-11 425.87 Ignored Aug 12th Kleinman Eruption 2023
31 Spoke** Loss 3-11 1046.02 Ignored Aug 12th Kleinman Eruption 2023
128 Garage Sale** Loss 3-11 326.54 Ignored Aug 12th Kleinman Eruption 2023
90 Stump** Loss 2-13 515.58 Ignored Aug 13th Kleinman Eruption 2023
194 Breakers Mark Loss 4-13 -24.89 Aug 13th Kleinman Eruption 2023
224 Night Cap Win 13-9 757.44 Aug 13th Kleinman Eruption 2023
190 Drip Loss 6-13 5.37 Sep 9th 2023 Mixed Oregon Sectional Championship
101 Igneous Ultimate** Loss 4-13 458.37 Ignored Sep 9th 2023 Mixed Oregon Sectional Championship
91 Hive** Loss 4-13 503.4 Ignored Sep 9th 2023 Mixed Oregon Sectional Championship
128 Garage Sale Loss 6-13 326.54 Sep 9th 2023 Mixed Oregon Sectional Championship
168 Choco Ghost House Loss 7-12 221.59 Sep 10th 2023 Mixed Oregon Sectional Championship
224 Night Cap Loss 7-8 213.87 Sep 10th 2023 Mixed Oregon 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)