#256 Virginia Tech-B (5-7)

avg: 576.62  •  sd: 64.66  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
231 Christopher Newport Loss 8-9 587.61 Mar 23rd Fishbowl
346 George Washington-B Win 12-4 643.86 Mar 23rd Fishbowl
308 James Madison-B Win 9-4 916.58 Mar 23rd Fishbowl
372 William & Mary-B** Win 12-5 328.1 Ignored Mar 23rd Fishbowl
231 Christopher Newport Loss 8-10 449.94 Mar 24th Fishbowl
301 Virginia-B Win 13-9 767.81 Mar 24th Fishbowl
156 Johns Hopkins Loss 8-15 454.23 Mar 30th Atlantic Coast Open 2024
165 RIT Loss 7-15 365.29 Mar 30th Atlantic Coast Open 2024
208 Virginia Commonwealth Loss 10-11 660.38 Mar 30th Atlantic Coast Open 2024
298 Mary Washington Win 11-4 963.78 Mar 30th Atlantic Coast Open 2024
224 American Loss 12-15 431.26 Mar 31st Atlantic Coast Open 2024
231 Christopher Newport Loss 8-13 216.45 Mar 31st Atlantic Coast Open 2024
**Blowout Eligible


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
  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
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)