#13 Wisconsin (14-7)

avg: 2000.97  •  sd: 62.51  •  top 16/20: 97.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Brigham Young Win 15-14 2259.73 Feb 8th Florida Warm Up 2019
65 Florida Win 13-11 1764.59 Feb 8th Florida Warm Up 2019
54 Virginia Tech Loss 12-13 1494.44 Feb 8th Florida Warm Up 2019
4 Pittsburgh Loss 7-10 1795.26 Feb 9th Florida Warm Up 2019
27 LSU Loss 8-11 1412.13 Feb 9th Florida Warm Up 2019
106 Illinois State Win 15-7 1927.34 Feb 9th Florida Warm Up 2019
20 Tufts Win 10-9 1989.15 Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Win 15-7 2083.99 Feb 10th Florida Warm Up 2019
69 Emory Win 15-5 2108.46 Feb 10th Florida Warm Up 2019
50 Stanford Win 11-10 1757.74 Mar 2nd Stanford Invite 2019
1 North Carolina Loss 8-12 1790.77 Mar 2nd Stanford Invite 2019
30 Victoria Win 13-5 2365.9 Mar 2nd Stanford Invite 2019
21 California Loss 10-11 1718.46 Mar 3rd Stanford Invite 2019
8 Colorado Loss 7-10 1705.78 Mar 3rd Stanford Invite 2019
30 Victoria Win 13-4 2365.9 Mar 3rd Stanford Invite 2019
37 Illinois Win 13-7 2277.92 Mar 16th Centex 2019 Men
31 Texas A&M Win 13-5 2348.41 Mar 16th Centex 2019 Men
12 Texas Win 13-9 2428.47 Mar 16th Centex 2019 Men
27 LSU Win 15-13 1991.92 Mar 17th Centex 2019 Men
8 Colorado Loss 13-14 1970.44 Mar 17th Centex 2019 Men
19 Colorado State Win 15-13 2113.73 Mar 17th Centex 2019 Men
**Blowout Eligible

FAQ

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
  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
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)