#336 Virginia-B (9-13)

avg: 569.94  •  sd: 69.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
127 Pittsburgh-B** Loss 2-13 788.73 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
64 Maryland** Loss 2-13 1077.89 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
212 West Virginia Loss 0-13 479.5 Feb 17th Commonwealth Cup Weekend 1 2024
214 North Carolina-B Loss 10-11 948.8 Feb 18th Commonwealth Cup Weekend 1 2024
325 South Carolina-B Loss 7-8 495.93 Feb 18th Commonwealth Cup Weekend 1 2024
209 Christopher Newport Loss 7-9 802.14 Mar 23rd Fishbowl
393 George Washington-B Win 13-2 757.82 Mar 23rd Fishbowl
342 James Madison-B Win 11-7 1021.76 Mar 23rd Fishbowl
394 William & Mary-B Win 11-2 753.15 Mar 23rd Fishbowl
342 James Madison-B Loss 6-12 -24.45 Mar 24th Fishbowl
262 Virginia Tech-B Loss 9-13 480.71 Mar 24th Fishbowl
214 North Carolina-B Loss 6-14 473.8 Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
325 South Carolina-B Loss 8-9 495.93 Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
262 Virginia Tech-B Loss 8-12 458.12 Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
394 William & Mary-B Win 11-9 402.36 Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
342 James Madison-B Win 10-9 679.86 Apr 21st Southern Atlantic Coast Dev Mens Conferences 2024
251 North Carolina State-B Win 13-9 1361.5 Apr 21st Southern Atlantic Coast Dev Mens Conferences 2024
392 Richmond-B Win 14-8 703.71 Apr 21st Southern Atlantic Coast Dev Mens Conferences 2024
404 American-B Win 13-8 530.45 May 4th Atlantic Coast Dev College Mens Regionals 2024
325 South Carolina-B Loss 6-15 20.93 May 4th Atlantic Coast Dev College Mens Regionals 2024
311 Maryland-B Loss 8-14 138.52 May 4th Atlantic Coast Dev College Mens Regionals 2024
390 Delaware-B Win 15-9 705.41 May 5th Atlantic Coast Dev College Mens Regionals 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)