#307 Mary Washington (0-12)

avg: 683.16  •  sd: 82.78  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
75 Dartmouth Loss 6-11 1065.79 Jan 27th Mid Atlantic Warm Up
92 Pennsylvania** Loss 4-13 939.36 Ignored Jan 27th Mid Atlantic Warm Up
59 William & Mary Loss 7-13 1169.77 Jan 27th Mid Atlantic Warm Up
226 American Loss 6-11 486.43 Jan 28th Mid Atlantic Warm Up
166 RIT Loss 7-11 800.93 Jan 28th Mid Atlantic Warm Up
104 Liberty Loss 9-12 1145.36 Jan 28th Mid Atlantic Warm Up
148 Johns Hopkins Loss 8-14 779.08 Mar 30th Atlantic Coast Open 2024
127 Pittsburgh-B** Loss 4-15 788.73 Ignored Mar 30th Atlantic Coast Open 2024
204 Virginia Commonwealth Loss 10-15 654.38 Mar 30th Atlantic Coast Open 2024
262 Virginia Tech-B Loss 4-11 299.28 Mar 30th Atlantic Coast Open 2024
226 American Loss 7-15 433.12 Mar 31st Atlantic Coast Open 2024
209 Christopher Newport Loss 6-13 481.48 Mar 31st Atlantic Coast Open 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)