#296 South Carolina-B (3-14)

avg: 369.12  •  sd: 52.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
125 Davidson** Loss 3-13 546.57 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
199 Messiah Loss 5-11 232.24 Feb 17th Commonwealth Cup Weekend 1 2024
216 North Carolina State-B Win 7-6 885.32 Feb 17th Commonwealth Cup Weekend 1 2024
216 North Carolina State-B Loss 3-8 160.32 Feb 18th Commonwealth Cup Weekend 1 2024
301 Virginia-B Win 8-7 474.24 Feb 18th Commonwealth Cup Weekend 1 2024
238 Wake Forest Loss 7-8 550.13 Feb 18th Commonwealth Cup Weekend 1 2024
201 Alabama-Birmingham Loss 6-12 247.56 Feb 24th Joint Summit 2024
324 Coastal Carolina Win 8-6 526.64 Feb 24th Joint Summit 2024
250 Georgia Tech-B Loss 5-9 98.8 Feb 24th Joint Summit 2024
185 South Florida Loss 4-13 266.3 Feb 24th Joint Summit 2024
201 Alabama-Birmingham Loss 6-13 226.87 Feb 25th Joint Summit 2024
173 Clemson Loss 5-13 339.13 Feb 25th Joint Summit 2024
271 High Point Loss 6-7 386.1 Mar 23rd Needle in a Ho Stack 2024
158 Kennesaw State** Loss 2-12 410.72 Ignored Mar 23rd Needle in a Ho Stack 2024
216 North Carolina State-B Loss 5-6 635.32 Mar 24th Needle in a Ho Stack 2024
116 Liberty** Loss 1-11 582.96 Ignored Mar 24th Needle in a Ho Stack 2024
238 Wake Forest Loss 5-8 221.53 Mar 24th Needle in a Ho Stack 2024
**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)