#239 Wake Forest (8-16)

avg: 919.66  •  sd: 72.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
142 Davidson Loss 4-13 691.44 Feb 15th 2025 Commonwealth Cup Weekend 1
227 Michigan-B Win 8-7 1086.98 Feb 15th 2025 Commonwealth Cup Weekend 1
141 Pittsburgh-B Loss 7-12 777.8 Feb 15th 2025 Commonwealth Cup Weekend 1
256 Illinois-B Win 7-5 1188.96 Feb 16th 2025 Commonwealth Cup Weekend 1
198 North Carolina-B Win 9-6 1494.75 Feb 16th 2025 Commonwealth Cup Weekend 1
178 Ohio Loss 5-10 592.85 Feb 16th 2025 Commonwealth Cup Weekend 1
232 George Washington Win 9-6 1359.23 Mar 22nd Atlantic Coast Open 2025
94 Lehigh Loss 9-15 965.34 Mar 22nd Atlantic Coast Open 2025
115 RIT Loss 3-15 784.22 Mar 22nd Atlantic Coast Open 2025
45 Virginia Tech** Loss 1-15 1175.6 Ignored Mar 22nd Atlantic Coast Open 2025
132 Florida State Loss 4-15 727.53 Mar 23rd Atlantic Coast Open 2025
140 George Mason Loss 5-15 704.92 Mar 23rd Atlantic Coast Open 2025
164 Massachusetts -B Loss 4-10 620.7 Mar 23rd Atlantic Coast Open 2025
209 Cedarville Win 10-8 1294.07 Mar 29th Needle in a Ho Stack 2025
252 East Tennessee State Loss 11-15 489.35 Mar 29th Needle in a Ho Stack 2025
330 South Carolina-B Win 7-5 903.46 Mar 29th Needle in a Ho Stack 2025
98 Tennessee-Chattanooga Loss 5-13 872.22 Mar 29th Needle in a Ho Stack 2025
359 Georgia College Win 14-9 866.6 Mar 30th Needle in a Ho Stack 2025
203 North Carolina State-B Loss 8-13 559.58 Mar 30th Needle in a Ho Stack 2025
122 Clemson Loss 7-13 804.06 Apr 12th Carolina D I Mens Conferences 2025
122 Clemson Loss 5-10 787.7 Apr 12th Carolina D I Mens Conferences 2025
63 Duke** Loss 5-13 1048.79 Ignored Apr 12th Carolina D I Mens Conferences 2025
4 North Carolina** Loss 1-13 1759.83 Ignored Apr 12th Carolina D I Mens Conferences 2025
168 Charleston Win 15-4 1807.88 Apr 13th Carolina D I Mens Conferences 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)