#23 Wisconsin (13-15)

avg: 1894.52  •  sd: 47.33  •  top 16/20: 16%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Brown Loss 11-13 1845.88 Feb 3rd Florida Warm Up 2023
112 Illinois Win 13-6 1915.98 Feb 3rd Florida Warm Up 2023
88 Central Florida Win 13-7 1991.75 Feb 4th Florida Warm Up 2023
12 Minnesota Loss 13-15 1856.73 Feb 4th Florida Warm Up 2023
77 Temple Win 13-9 1898.88 Feb 4th Florida Warm Up 2023
4 Texas Win 11-10 2339.75 Feb 4th Florida Warm Up 2023
34 Michigan Win 13-10 2116.97 Feb 5th Florida Warm Up 2023
8 Pittsburgh Loss 8-13 1659.02 Feb 5th Florida Warm Up 2023
58 California-San Diego Win 13-7 2138.94 Mar 4th Stanford Invite Mens
10 California-Santa Cruz Loss 10-11 1964.74 Mar 4th Stanford Invite Mens
109 Southern California Win 13-4 1923.91 Mar 4th Stanford Invite Mens
16 British Columbia Loss 10-11 1867.55 Mar 5th Stanford Invite Mens
47 Colorado State Win 12-7 2167.73 Mar 5th Stanford Invite Mens
44 Victoria Loss 10-12 1458.6 Mar 5th Stanford Invite Mens
2 Brigham Young Loss 10-13 1990.16 Mar 17th Centex 2023
6 Colorado Loss 6-11 1650.87 Mar 18th Centex 2023
47 Colorado State Win 12-11 1772.22 Mar 18th Centex 2023
13 Tufts Loss 9-12 1722.86 Mar 18th Centex 2023
14 Carleton College Loss 9-15 1534.39 Mar 19th Centex 2023
79 Texas A&M Win 15-6 2073.68 Mar 19th Centex 2023
28 Oklahoma Christian Loss 11-14 1530.15 Mar 19th Centex 2023
19 Georgia Win 10-8 2213.52 Apr 1st Easterns 2023
13 Tufts Loss 6-12 1488.91 Apr 1st Easterns 2023
7 Cal Poly-SLO Win 11-9 2424.56 Apr 1st Easterns 2023
5 Vermont Loss 8-11 1844.45 Apr 1st Easterns 2023
14 Carleton College Loss 12-15 1749.38 Apr 2nd Easterns 2023
25 North Carolina-Wilmington Loss 13-14 1759.26 Apr 2nd Easterns 2023
27 South Carolina Win 15-11 2229.34 Apr 2nd Easterns 2023
**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)