#157 Virginia Commonwealth (5-7)

avg: 670.86  •  sd: 90.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
101 SUNY-Binghamton Loss 8-12 583.78 Jan 27th Mid Atlantic Warm Up
80 Case Western Reserve Loss 5-12 589.08 Jan 27th Mid Atlantic Warm Up
94 Connecticut Loss 7-10 682.58 Jan 27th Mid Atlantic Warm Up
142 Johns Hopkins Win 11-10 917.02 Jan 28th Mid Atlantic Warm Up
94 Connecticut Loss 2-15 472.25 Jan 28th Mid Atlantic Warm Up
132 Pennsylvania Loss 0-15 266.57 Jan 28th Mid Atlantic Warm Up
184 American Win 11-7 955.19 Feb 24th Monument Melee
203 Drexel Loss 6-8 33.15 Feb 24th Monument Melee
158 Maryland-Baltimore County Win 11-7 1136 Feb 24th Monument Melee
116 Villanova Loss 7-12 442.04 Feb 25th Monument Melee
166 East Carolina Win 10-7 1011.02 Feb 25th Monument Melee
184 American Win 14-11 801.64 Feb 25th Monument Melee
**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)