#181 George Washington (5-11)

avg: 307.11  •  sd: 60.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 American** Loss 3-8 573.83 Ignored Feb 4th Cherry Blossom Classic 2023
115 Delaware Loss 6-9 475.36 Feb 4th Cherry Blossom Classic 2023
160 Towson Win 9-8 634.42 Feb 4th Cherry Blossom Classic 2023
134 Johns Hopkins Loss 4-7 239.73 Feb 4th Cherry Blossom Classic 2023
159 Franciscan Win 7-6 638.9 Feb 18th Commonwealth Cup Weekend1 2023
175 Michigan-B Loss 4-7 -103.77 Feb 18th Commonwealth Cup Weekend1 2023
215 Elon Win 11-1 352.67 Feb 18th Commonwealth Cup Weekend1 2023
135 Mary Washington Loss 5-7 398.61 Feb 18th Commonwealth Cup Weekend1 2023
201 Wake Forest Win 12-5 662.67 Feb 19th Commonwealth Cup Weekend1 2023
186 Richmond Win 7-6 386.23 Feb 19th Commonwealth Cup Weekend1 2023
134 Johns Hopkins Loss 4-8 171.08 Mar 5th Cherry Blossom Classic 2023
160 Towson Loss 7-10 119.75 Mar 5th Cherry Blossom Classic 2023
- George Mason Loss 7-8 170.3 Apr 1st Atlantic Coast Open 2023
55 Cornell** Loss 3-13 745.99 Ignored Apr 1st Atlantic Coast Open 2023
130 Liberty Loss 5-11 173.74 Apr 1st Atlantic Coast Open 2023
130 Liberty Loss 5-12 173.74 Apr 2nd Atlantic Coast Open 2023
**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)