#151 George Mason (7-13)

avg: 1116.84  •  sd: 72.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
30 Auburn Loss 5-11 1109.26 Feb 3rd Queen City Tune Up 2018 College Open
64 North Carolina-Charlotte Loss 9-11 1213.3 Feb 3rd Queen City Tune Up 2018 College Open
16 North Carolina-Wilmington Loss 6-11 1337.81 Feb 3rd Queen City Tune Up 2018 College Open
91 Penn State Win 11-8 1739.51 Feb 3rd Queen City Tune Up 2018 College Open
133 Case Western Reserve Loss 4-11 575.77 Feb 3rd Queen City Tune Up 2018 College Open
62 Vermont Win 8-5 1919.43 Feb 4th Queen City Tune Up 2018 College Open
61 James Madison Loss 7-13 914.99 Feb 17th Easterns Qualifier 2018
48 Dartmouth Loss 7-12 1044.92 Feb 17th Easterns Qualifier 2018
149 Davidson Win 10-8 1403.52 Feb 17th Easterns Qualifier 2018
66 Kennesaw State Loss 6-13 858.01 Feb 17th Easterns Qualifier 2018
98 Clemson Loss 3-13 738.04 Feb 17th Easterns Qualifier 2018
73 Michigan State Loss 12-15 1119.05 Feb 18th Easterns Qualifier 2018
224 Georgia Southern Win 15-9 1376.72 Feb 18th Easterns Qualifier 2018
84 Virginia Loss 6-15 802.14 Feb 18th Easterns Qualifier 2018
113 Lehigh Loss 12-13 1159.08 Mar 24th Atlantic Coast Open 2018
270 American Win 12-9 1052.67 Mar 24th Atlantic Coast Open 2018
86 Duke Loss 8-13 902.82 Mar 24th Atlantic Coast Open 2018
243 Rowan Win 15-8 1348.82 Mar 25th Atlantic Coast Open 2018
174 East Carolina Loss 10-11 907.42 Mar 25th Atlantic Coast Open 2018
368 Edinboro** Win 15-3 911.46 Ignored Mar 25th Atlantic Coast Open 2018
**Blowout Eligible

FAQ

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)