#262 Virginia Tech-B (10-8)

avg: 899.28  •  sd: 47.53  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
209 Christopher Newport Loss 8-9 956.48 Mar 23rd Fishbowl
393 George Washington-B** Win 12-4 757.82 Ignored Mar 23rd Fishbowl
342 James Madison-B Win 9-4 1154.86 Mar 23rd Fishbowl
394 William & Mary-B** Win 12-5 753.15 Ignored Mar 23rd Fishbowl
209 Christopher Newport Loss 8-10 818.82 Mar 24th Fishbowl
336 Virginia-B Win 13-9 988.5 Mar 24th Fishbowl
148 Johns Hopkins Loss 8-15 750.31 Mar 30th Atlantic Coast Open 2024
166 RIT Loss 7-15 667.82 Mar 30th Atlantic Coast Open 2024
204 Virginia Commonwealth Loss 10-11 982.98 Mar 30th Atlantic Coast Open 2024
307 Mary Washington Win 11-4 1283.16 Mar 30th Atlantic Coast Open 2024
226 American Loss 12-15 732.63 Mar 31st Atlantic Coast Open 2024
209 Christopher Newport Loss 8-13 585.32 Mar 31st Atlantic Coast Open 2024
214 North Carolina-B Win 10-9 1198.8 Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
392 Richmond-B** Win 11-4 767.68 Ignored Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
394 William & Mary-B Win 10-6 649.31 Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
336 Virginia-B Win 12-8 1011.09 Apr 20th Southern Atlantic Coast Dev Mens Conferences 2024
214 North Carolina-B Loss 11-15 692.63 Apr 21st Southern Atlantic Coast Dev Mens Conferences 2024
325 South Carolina-B Win 12-8 1062.08 Apr 21st Southern Atlantic Coast Dev Mens Conferences 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)