#34 Michigan (8-13)

avg: 1497.72  •  sd: 75.42  •  top 16/20: 2.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 Georgia Win 9-6 1831.76 Feb 11th Queen City Tune Up1
199 North Carolina-Wilmington** Win 13-4 527.43 Ignored Feb 11th Queen City Tune Up1
4 Tufts Loss 8-15 1735.48 Feb 11th Queen City Tune Up1
47 Washington University Win 10-6 1851.32 Feb 11th Queen City Tune Up1
26 Minnesota Loss 8-9 1439.32 Feb 12th Queen City Tune Up1
13 Pittsburgh Loss 7-9 1531.28 Feb 12th Queen City Tune Up1
40 Georgia Loss 9-12 1067.83 Feb 25th Commonwealth Cup Weekend2 2023
39 Brown Loss 10-11 1293.32 Feb 25th Commonwealth Cup Weekend2 2023
16 Yale Loss 8-12 1302.33 Feb 25th Commonwealth Cup Weekend2 2023
13 Pittsburgh Loss 7-11 1343.72 Feb 25th Commonwealth Cup Weekend2 2023
66 Case Western Reserve Win 13-3 1722.19 Feb 26th Commonwealth Cup Weekend2 2023
67 Massachusetts Win 13-6 1719.65 Feb 26th Commonwealth Cup Weekend2 2023
27 Notre Dame Loss 7-8 1431.17 Feb 26th Commonwealth Cup Weekend2 2023
57 Penn State Loss 10-11 1099.92 Feb 26th Commonwealth Cup Weekend2 2023
39 Brown Win 11-6 1965.01 Mar 18th Womens Centex1
49 Texas Win 13-10 1657.77 Mar 18th Womens Centex1
14 Virginia Loss 9-12 1463.27 Mar 18th Womens Centex1
19 Colorado State Loss 10-15 1233.39 Mar 19th Womens Centex1
32 Ohio State Loss 10-12 1277.31 Mar 19th Womens Centex1
43 Wisconsin Loss 11-13 1145.54 Mar 19th Womens Centex1
43 Wisconsin Win 13-3 1974.38 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)