#134 Charleston Heat Stroke (4-7)

avg: 687.65  •  sd: 40.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
129 Harvest Moon Loss 10-11 600.63 Jun 21st Space Jam 2025
100 GVL Trout Loss 10-13 640.86 Jun 21st Space Jam 2025
170 PARLAY Win 13-3 852.03 Jun 21st Space Jam 2025
157 Tennessee Folklore Win 13-9 871.68 Jun 22nd Space Jam 2025
177 Gambit Win 13-1 760.92 Jun 22nd Space Jam 2025
59 Big Wrench** Loss 5-13 756.46 Ignored Jul 12th Boston Invite 2025
58 Magma Bears** Loss 3-13 770.9 Ignored Jul 12th Boston Invite 2025
104 Resurgam Ultimate Loss 9-12 569.81 Jul 12th Boston Invite 2025
78 Odyssey Loss 9-15 619.31 Jul 13th Boston Invite 2025
92 Crypt Loss 8-15 493.07 Jul 13th Boston Invite 2025
140 Crossfire Win 14-12 823.14 Jul 13th Boston Invite 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)