#64 Medusa (4-7)

avg: 135.77  •  sd: 48.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- Illinois Lucid Win 12-5 176.77 Jun 21st SCINNY 2025
67 Solstice Loss 5-6 -116.97 Jun 21st SCINNY 2025
61 Incline Loss 8-13 -142.7 Jun 22nd SCINNY 2025
41 Indy Rogue** Loss 5-13 180.17 Ignored Jun 22nd SCINNY 2025
53 Sureshot Loss 5-9 66.52 Jun 22nd SCINNY 2025
52 Dish Loss 5-13 31.69 Jul 12th Heavyweights 2025
37 Lake Erie Walleye** Loss 5-15 387.51 Ignored Jul 12th Heavyweights 2025
73 Lakeshore Drive** Win 13-4 35.22 Ignored Jul 12th Heavyweights 2025
35 River Monsters** Loss 5-14 420.94 Jul 12th Heavyweights 2025
66 Compact Disc Win 11-10 187.4 Jul 13th Heavyweights 2025
67 Solstice Win 14-11 321.37 Jul 13th Heavyweights 2025
**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)