#202 George Mason (5-11)

avg: 1116.33  •  sd: 62.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
215 East Carolina Win 8-6 1366.08 Feb 24th Monument Melee
170 Villanova Win 10-8 1514.63 Feb 24th Monument Melee
217 George Washington Loss 8-9 933.5 Feb 24th Monument Melee
226 American Loss 7-11 566.23 Feb 25th Monument Melee
289 Drexel Win 10-5 1343.56 Feb 25th Monument Melee
215 East Carolina Loss 7-8 940.59 Feb 25th Monument Melee
79 Case Western Reserve Loss 5-12 995.69 Mar 30th East Coast Invite 2024
75 Dartmouth Loss 8-10 1349.82 Mar 30th East Coast Invite 2024
162 Rutgers Loss 6-8 982.05 Mar 30th East Coast Invite 2024
93 Princeton Loss 9-13 1119.47 Mar 30th East Coast Invite 2024
163 Columbia Win 9-8 1400.42 Mar 31st East Coast Invite 2024
300 SUNY-Stony Brook Win 12-3 1314.83 Mar 31st East Coast Invite 2024
44 Virginia Tech** Loss 5-12 1214.41 Ignored Apr 20th Virginia D I Mens Conferences 2024
204 Virginia Commonwealth Loss 7-10 718.31 Apr 20th Virginia D I Mens Conferences 2024
104 Liberty Loss 9-12 1145.36 Apr 21st Virginia D I Mens Conferences 2024
59 William & Mary** Loss 3-13 1127.3 Ignored Apr 21st Virginia D I Mens Conferences 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)