#123 Amber (12-4)

avg: 981.28  •  sd: 79.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Waterloo Loss 8-13 1189.89 Jun 24th Texas 2 Finger 2023
162 Dallas Nightfall Loss 5-9 253.05 Jun 24th Texas 2 Finger 2023
242 Central Arkansas Surge Win 11-7 584.22 Jun 24th Texas 2 Finger 2023
94 Risky Business Win 10-9 1213.28 Jun 25th Texas 2 Finger 2023
27 Waterloo** Loss 3-10 1086.05 Ignored Jun 25th Texas 2 Finger 2023
139 Goosebumps Win 9-7 1149.79 Jun 25th Texas 2 Finger 2023
229 Scoober Heroes** Win 10-4 906.56 Ignored Aug 26th Ragna Rock 2023
223 LRU Win 13-8 837.43 Aug 26th Ragna Rock 2023
218 Hairy Otter Win 12-5 989.72 Aug 26th Ragna Rock 2023
252 Trophic Cascade** Win 13-3 337.4 Ignored Aug 27th Ragna Rock 2023
204 New Orleans Boil Win 10-8 786.23 Aug 27th Ragna Rock 2023
139 Goosebumps Win 9-5 1399.51 Aug 27th Ragna Rock 2023
204 New Orleans Boil Win 15-7 1123.57 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
150 Memphis STAX Win 11-8 1193.04 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
126 Barefoot Loss 9-11 717.92 Sep 9th 2023 Mixed Gulf Coast Sectional Championship
126 Barefoot Win 13-12 1092.13 Sep 10th 2023 Mixed Gulf Coast 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)