#47 Washington University (14-6)

avg: 1355.16  •  sd: 79.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 Georgia Win 11-9 1662.4 Feb 11th Queen City Tune Up1
199 North Carolina-Wilmington** Win 10-4 527.43 Ignored Feb 11th Queen City Tune Up1
34 Michigan Loss 6-10 1001.56 Feb 11th Queen City Tune Up1
4 Tufts** Loss 4-15 1700.29 Ignored Feb 11th Queen City Tune Up1
21 North Carolina State Win 9-8 1766.53 Feb 12th Queen City Tune Up1
27 Notre Dame Loss 5-12 956.17 Feb 12th Queen City Tune Up1
121 Saint Louis** Win 11-1 1298.72 Ignored Mar 4th Midwest Throwdown 2023
114 Marquette Win 11-6 1336.36 Mar 4th Midwest Throwdown 2023
151 Washington University-B** Win 11-1 1100.64 Ignored Mar 4th Midwest Throwdown 2023
187 Wisconsin-B** Win 11-2 723.84 Ignored Mar 4th Midwest Throwdown 2023
79 St. Olaf Win 13-1 1607.47 Mar 5th Midwest Throwdown 2023
68 Northwestern Win 7-6 1240.06 Mar 5th Midwest Throwdown 2023
163 Truman State** Win 13-1 950.79 Ignored Mar 5th Midwest Throwdown 2023
56 Georgia Tech Loss 10-12 988.09 Mar 18th Womens Centex1
68 Northwestern Win 13-10 1443.21 Mar 18th Womens Centex1
22 Texas-Dallas Loss 6-12 1044.54 Mar 18th Womens Centex1
78 Central Florida Win 11-5 1612.53 Mar 19th Womens Centex1
15 Middlebury Loss 4-15 1206.54 Mar 19th Womens Centex1
44 Pennsylvania Win 11-10 1492.58 Mar 19th Womens Centex1
49 Texas Win 7-5 1657.77 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)