#37 Washington University (13-7)

avg: 1768.65  •  sd: 91.4  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
97 Appalachian State Win 15-3 1820.34 Feb 10th Queen City Tune Up 2024
3 Carleton College** Loss 0-15 2106.55 Ignored Feb 10th Queen City Tune Up 2024
22 Notre Dame Loss 12-13 1930.17 Feb 10th Queen City Tune Up 2024
25 Pittsburgh Loss 8-10 1700.25 Feb 10th Queen City Tune Up 2024
17 Pennsylvania Win 11-10 2249.71 Feb 11th Queen City Tune Up 2024
57 William & Mary Win 9-5 2073.46 Feb 11th Queen City Tune Up 2024
92 Saint Louis Win 8-5 1726.08 Mar 2nd Midwest Throwdown 2024
216 Northwestern-B** Win 13-0 573.93 Ignored Mar 2nd Midwest Throwdown 2024
187 Washington University-B** Win 10-4 1002.54 Ignored Mar 2nd Midwest Throwdown 2024
109 Truman State** Win 11-2 1724.52 Ignored Mar 2nd Midwest Throwdown 2024
92 Saint Louis Win 9-4 1872.48 Mar 3rd Midwest Throwdown 2024
84 Iowa State Win 7-6 1448.81 Mar 3rd Midwest Throwdown 2024
109 Truman State** Win 9-2 1724.52 Ignored Mar 3rd Midwest Throwdown 2024
55 Southern California Win 13-9 1974.65 Mar 16th Womens Centex 2024
21 Ohio State Loss 8-13 1586 Mar 16th Womens Centex 2024
36 Texas-Dallas Win 12-10 2031.62 Mar 16th Womens Centex 2024
27 Utah Loss 5-8 1468.32 Mar 17th Womens Centex 2024
27 Utah Loss 10-11 1796.93 Mar 17th Womens Centex 2024
36 Texas-Dallas Win 11-10 1918.5 Mar 17th Womens Centex 2024
46 Texas Loss 6-15 1076.99 Mar 17th Womens Centex 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)