#40 Wisconsin (11-11)

avg: 1628.01  •  sd: 66.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
19 Georgia Loss 9-12 1547.13 Jan 31st Florida Warm Up 2025
51 Purdue Win 13-6 2155.86 Jan 31st Florida Warm Up 2025
103 Texas A&M Win 13-8 1736.82 Jan 31st Florida Warm Up 2025
119 Central Florida Win 13-8 1677.54 Feb 1st Florida Warm Up 2025
51 Purdue Win 13-8 2052.02 Feb 1st Florida Warm Up 2025
20 Vermont Loss 8-13 1361.92 Feb 1st Florida Warm Up 2025
16 Brown Loss 9-13 1501.16 Feb 2nd Florida Warm Up 2025
31 Minnesota Win 11-9 1962.68 Feb 2nd Florida Warm Up 2025
35 Chicago Loss 8-12 1213.18 Mar 15th Mens Centex 2025
66 Dartmouth Win 8-7 1577.25 Mar 15th Mens Centex 2025
46 Middlebury Win 13-9 2016.36 Mar 15th Mens Centex 2025
17 Tufts Loss 8-11 1531.4 Mar 15th Mens Centex 2025
35 Chicago Win 15-10 2107.94 Mar 16th Mens Centex 2025
13 Texas Loss 10-15 1517.25 Mar 16th Mens Centex 2025
29 Utah Valley Win 15-11 2133.42 Mar 16th Mens Centex 2025
8 Brigham Young Loss 9-15 1555.08 Mar 22nd Northwest Challenge 2025 mens
33 California-Santa Barbara Loss 10-14 1262.23 Mar 22nd Northwest Challenge 2025 mens
109 Gonzaga Win 15-11 1598.83 Mar 22nd Northwest Challenge 2025 mens
38 Utah State Loss 14-15 1507.62 Mar 22nd Northwest Challenge 2025 mens
41 California-San Diego Loss 11-15 1240.3 Mar 23rd Northwest Challenge 2025 mens
109 Gonzaga Win 11-3 1817.66 Mar 23rd Northwest Challenge 2025 mens
32 Virginia Loss 8-15 1115.06 Mar 23rd Northwest Challenge 2025 mens
**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)