#245 George Mason University (4-15)

avg: 494.17  •  sd: 122.42  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
80 James Madison** Loss 3-13 1047.06 Ignored Jan 27th Winta Binta Vinta Fest 2018
88 Georgetown** Loss 1-13 978.62 Ignored Jan 27th Winta Binta Vinta Fest 2018
215 Virginia-B Loss 5-7 448.22 Jan 27th Winta Binta Vinta Fest 2018
46 North Carolina-Wilmington** Loss 1-13 1378.21 Ignored Jan 27th Winta Binta Vinta Fest 2018
135 William & Mary** Loss 3-15 673.75 Ignored Jan 28th Winta Binta Vinta Fest 2018
215 Virginia-B Loss 2-11 176.36 Jan 28th Winta Binta Vinta Fest 2018
262 Notre Dame-B Win 11-5 712.35 Mar 10th Tally Classic XIII
23 Auburn** Loss 0-11 1608.85 Ignored Mar 10th Tally Classic XIII
32 Florida** Loss 0-11 1480.24 Ignored Mar 10th Tally Classic XIII
46 North Carolina-Wilmington** Loss 3-15 1378.21 Ignored Mar 10th Tally Classic XIII
243 Georgia State Win 13-10 833.24 Mar 10th Tally Classic XIII
107 LSU** Loss 3-15 866.19 Ignored Mar 11th Tally Classic XIII
180 South Florida Loss 2-14 400.57 Mar 11th Tally Classic XIII
252 Johns Hopkins Win 8-5 864.18 Mar 24th Country Roads Classic 2018
214 Allegheny Loss 4-10 181.28 Mar 24th Country Roads Classic 2018
214 Allegheny Loss 3-9 181.28 Mar 24th Country Roads Classic 2018
110 West Virginia** Loss 1-13 833.65 Ignored Mar 24th Country Roads Classic 2018
94 George Washington** Loss 0-13 938.16 Ignored Mar 24th Country Roads Classic 2018
252 Johns Hopkins Win 7-6 535.58 Mar 25th Country Roads Classic 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)