#33 Wisconsin (8-11)

avg: 1645.5  •  sd: 62.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
12 Alabama-Huntsville Loss 8-13 1497.51 Feb 2nd Florida Warm Up 2024
17 Brigham Young Loss 10-13 1547.3 Feb 2nd Florida Warm Up 2024
97 Florida State Win 12-11 1372.77 Feb 2nd Florida Warm Up 2024
4 Massachusetts Loss 5-13 1634.96 Feb 3rd Florida Warm Up 2024
14 Texas Loss 3-12 1336.64 Feb 3rd Florida Warm Up 2024
74 Cincinnati Win 14-11 1674.53 Feb 4th Florida Warm Up 2024
37 Texas A&M Win 15-11 1972.1 Feb 4th Florida Warm Up 2024
43 California-San Diego Win 12-11 1687.26 Mar 2nd Stanford Invite 2024
40 Illinois Win 11-6 2126.38 Mar 2nd Stanford Invite 2024
35 California-Santa Cruz Win 11-10 1762.21 Mar 3rd Stanford Invite 2024
6 Oregon Loss 6-13 1509.26 Mar 3rd Stanford Invite 2024
65 Stanford Win 13-6 2004.93 Mar 3rd Stanford Invite 2024
2 Georgia Loss 11-13 2043.97 Mar 30th Easterns 2024
28 North Carolina-Wilmington Loss 8-12 1293.35 Mar 30th Easterns 2024
7 Pittsburgh Loss 8-12 1651.99 Mar 30th Easterns 2024
21 Tufts Loss 12-13 1703.7 Mar 30th Easterns 2024
36 North Carolina-Charlotte Loss 11-15 1237.09 Mar 31st Easterns 2024
20 Northeastern Loss 8-15 1265.51 Mar 31st Easterns 2024
34 Ohio State Win 14-11 1955.21 Mar 31st Easterns 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)