#29 Wisconsin (7-12)

avg: 1913.61  •  sd: 58.44  •  top 16/20: 0.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 South Carolina Loss 10-13 1434.98 Feb 10th Queen City Tune Up 2024
2 Vermont** Loss 5-15 2189.63 Ignored Feb 10th Queen City Tune Up 2024
39 Virginia Win 14-9 2212.76 Feb 10th Queen City Tune Up 2024
57 William & Mary Win 10-8 1807.07 Feb 10th Queen City Tune Up 2024
20 Northeastern Loss 7-8 1976.67 Feb 11th Queen City Tune Up 2024
25 Pittsburgh Loss 10-14 1564.21 Feb 11th Queen City Tune Up 2024
11 Brigham Young Loss 10-13 1990.72 Mar 16th NW Challenge 2024
18 Victoria Loss 8-12 1680.21 Mar 16th NW Challenge 2024
5 Oregon Loss 6-13 2005.7 Mar 16th NW Challenge 2024
30 California Loss 9-13 1465.99 Mar 17th NW Challenge 2024
13 Western Washington Loss 9-13 1793.23 Mar 17th NW Challenge 2024
44 Whitman Win 13-6 2292.34 Mar 17th NW Challenge 2024
75 Columbia Win 13-4 1974.91 Mar 30th East Coast Invite 2024
7 Tufts** Loss 3-9 1920.18 Ignored Mar 30th East Coast Invite 2024
39 Virginia Win 12-8 2180.05 Mar 30th East Coast Invite 2024
52 Yale Win 15-2 2194.05 Mar 30th East Coast Invite 2024
2 Vermont** Loss 5-15 2189.63 Ignored Mar 31st East Coast Invite 2024
12 Michigan Loss 6-11 1764.91 Mar 31st East Coast Invite 2024
35 Ohio Win 10-8 2060.8 Mar 31st East Coast Invite 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)