#229 Scoober Heroes (3-15)

avg: 306.56  •  sd: 100.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 Public Enemy** Loss 2-13 1110.07 Ignored Jun 24th Texas 2 Finger 2023
94 Risky Business** Loss 4-13 488.28 Ignored Jun 24th Texas 2 Finger 2023
139 Goosebumps Loss 4-8 305.64 Jun 24th Texas 2 Finger 2023
94 Risky Business Loss 5-11 488.28 Jun 25th Texas 2 Finger 2023
27 Waterloo** Loss 4-13 1086.05 Ignored Jun 25th Texas 2 Finger 2023
242 Central Arkansas Surge Win 5-3 535.89 Jun 25th Texas 2 Finger 2023
218 Hairy Otter Win 8-7 514.72 Aug 26th Ragna Rock 2023
223 LRU Loss 4-9 -258.73 Aug 26th Ragna Rock 2023
123 Amber** Loss 4-10 381.28 Ignored Aug 26th Ragna Rock 2023
139 Goosebumps Loss 5-12 270.45 Aug 27th Ragna Rock 2023
242 Central Arkansas Surge Win 11-5 717.33 Aug 27th Ragna Rock 2023
223 LRU Loss 6-8 40.78 Aug 27th Ragna Rock 2023
25 Public Enemy** Loss 1-13 1110.07 Ignored Sep 9th 2023 Mixed Texas Sectional Championship
139 Goosebumps Loss 5-13 270.45 Sep 9th 2023 Mixed Texas Sectional Championship
160 Moontower Loss 5-13 200.68 Sep 9th 2023 Mixed Texas Sectional Championship
27 Waterloo** Loss 3-13 1086.05 Ignored Sep 9th 2023 Mixed Texas Sectional Championship
94 Risky Business Loss 6-13 488.28 Sep 10th 2023 Mixed Texas Sectional Championship
160 Moontower Loss 7-13 243.15 Sep 10th 2023 Mixed Texas 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)