#101 Igneous Ultimate (14-9)

avg: 1058.37  •  sd: 55.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
239 SkyLab** Win 13-2 780.19 Ignored Jun 24th Buried In Disc Satisfaction BIDS
128 Garage Sale Win 11-7 1393.43 Jun 24th Buried In Disc Satisfaction BIDS
172 Nebula Win 13-5 1336.26 Jun 24th Buried In Disc Satisfaction BIDS
224 Night Cap** Win 13-2 938.87 Ignored Jun 24th Buried In Disc Satisfaction BIDS
62 American Barbecue Loss 8-11 924.1 Aug 12th Kleinman Eruption 2023
90 Stump Win 12-11 1240.58 Aug 12th Kleinman Eruption 2023
224 Night Cap Win 13-10 667.01 Aug 12th Kleinman Eruption 2023
77 Seattle Soft Serve Loss 6-8 888.32 Aug 13th Kleinman Eruption 2023
79 Bullet Train Loss 10-11 1053.25 Aug 13th Kleinman Eruption 2023
172 Nebula Win 12-7 1256.77 Aug 13th Kleinman Eruption 2023
71 Sego Loss 7-10 847.07 Aug 19th Ski Town Classic 2023
95 Space Ghosts Loss 9-10 953.53 Aug 19th Ski Town Classic 2023
133 Karma Win 13-8 1394.71 Aug 19th Ski Town Classic 2023
51 Quick Draw Loss 8-10 1123.78 Aug 20th Ski Town Classic 2023
71 Sego Win 10-9 1361.74 Aug 20th Ski Town Classic 2023
60 Cutthroat Loss 7-11 843.39 Aug 20th Ski Town Classic 2023
190 Drip Win 13-6 1205.37 Sep 9th 2023 Mixed Oregon Sectional Championship
239 SkyLab** Win 13-4 780.19 Ignored Sep 9th 2023 Mixed Oregon Sectional Championship
128 Garage Sale Win 12-10 1164.66 Sep 9th 2023 Mixed Oregon Sectional Championship
91 Hive Loss 7-13 545.87 Sep 9th 2023 Mixed Oregon Sectional Championship
90 Stump Win 14-12 1336.53 Sep 10th 2023 Mixed Oregon Sectional Championship
172 Nebula Win 15-8 1301.07 Sep 10th 2023 Mixed Oregon Sectional Championship
91 Hive Loss 8-15 538.59 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)