#45 Washington University (14-6)

avg: 1479.26  •  sd: 80.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
40 Georgia Win 11-9 1779.91 Feb 11th Queen City Tune Up1
202 North Carolina-Wilmington** Win 10-4 645.79 Ignored Feb 11th Queen City Tune Up1
35 Michigan Loss 6-10 1123.41 Feb 11th Queen City Tune Up1
4 Tufts** Loss 4-15 1826.44 Ignored Feb 11th Queen City Tune Up1
21 North Carolina State Win 9-8 1881.31 Feb 12th Queen City Tune Up1
26 Notre Dame Loss 5-12 1088.33 Feb 12th Queen City Tune Up1
124 Saint Louis** Win 11-1 1419.4 Ignored Mar 4th Midwest Throwdown 2023
106 Marquette Win 11-6 1498.43 Mar 4th Midwest Throwdown 2023
148 Washington University-B** Win 11-1 1220.26 Ignored Mar 4th Midwest Throwdown 2023
188 Wisconsin-B** Win 11-2 837.78 Ignored Mar 4th Midwest Throwdown 2023
73 St. Olaf Win 13-1 1826.89 Mar 5th Midwest Throwdown 2023
70 Northwestern Win 7-6 1363.77 Mar 5th Midwest Throwdown 2023
165 Truman State** Win 13-1 1082.35 Ignored Mar 5th Midwest Throwdown 2023
54 Georgia Tech Loss 10-12 1114.74 Mar 18th Womens Centex1
70 Northwestern Win 13-10 1566.91 Mar 18th Womens Centex1
23 Texas-Dallas Loss 6-12 1160.19 Mar 18th Womens Centex1
82 Central Florida Win 11-5 1730.12 Mar 19th Womens Centex1
16 Middlebury Loss 4-15 1235.84 Mar 19th Womens Centex1
44 Pennsylvania Win 11-10 1609.31 Mar 19th Womens Centex1
48 Texas Win 7-5 1788.09 Mar 19th Womens Centex1
**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)