#140 George Mason (13-13)

avg: 1288.12  •  sd: 49.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
37 Cincinnati Loss 11-12 1671.25 Jan 25th Mid Atlantic Warm Up 2025
70 Dartmouth Loss 4-13 995.96 Jan 25th Mid Atlantic Warm Up 2025
155 Johns Hopkins Win 9-8 1363.52 Jan 25th Mid Atlantic Warm Up 2025
220 Christopher Newport Win 13-7 1538.89 Jan 26th Mid Atlantic Warm Up 2025
77 Richmond Loss 10-11 1439.2 Jan 26th Mid Atlantic Warm Up 2025
93 Yale Loss 8-14 940.72 Jan 26th Mid Atlantic Warm Up 2025
300 Maryland-Baltimore County Win 12-7 1206.13 Feb 22nd Monument Melee 2025
238 Virginia Commonwealth Win 9-6 1324.34 Feb 22nd Monument Melee 2025
155 Johns Hopkins Win 9-7 1517.85 Feb 22nd Monument Melee 2025
181 American Win 11-10 1263.96 Feb 23rd Monument Melee 2025
173 East Carolina Win 12-10 1404.5 Feb 23rd Monument Melee 2025
332 Villanova Win 10-9 694.58 Feb 23rd Monument Melee 2025
63 Duke Loss 11-12 1507.68 Mar 22nd Atlantic Coast Open 2025
111 Liberty Loss 8-10 1126.31 Mar 22nd Atlantic Coast Open 2025
165 Massachusetts -B Win 11-9 1455.27 Mar 22nd Atlantic Coast Open 2025
264 Virginia Tech-B Win 11-7 1273.23 Mar 22nd Atlantic Coast Open 2025
181 American Loss 11-15 757.79 Mar 23rd Atlantic Coast Open 2025
173 East Carolina Loss 9-10 1041.37 Mar 23rd Atlantic Coast Open 2025
239 Wake Forest Win 15-5 1504.77 Mar 23rd Atlantic Coast Open 2025
29 Virginia Loss 8-14 1325.95 Apr 12th Virginia D I Mens Conferences 2025
52 William & Mary Loss 12-13 1568.54 Apr 12th Virginia D I Mens Conferences 2025
369 Radford** Win 13-4 922.87 Ignored Apr 12th Virginia D I Mens Conferences 2025
58 James Madison Loss 9-11 1401.46 Apr 12th Virginia D I Mens Conferences 2025
238 Virginia Commonwealth Win 15-9 1421.25 Apr 13th Virginia D I Mens Conferences 2025
52 William & Mary Loss 9-13 1274.98 Apr 13th Virginia D I Mens Conferences 2025
111 Liberty Loss 9-11 1139.77 Apr 13th Virginia D I Mens Conferences 2025
**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)