#29 Wisconsin (8-8)

avg: 1704.95  •  sd: 54.76  •  top 16/20: 1.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
47 Auburn Win 12-9 1872.98 Feb 14th Florida Warm Up 2020 Weekend 1
17 Michigan Loss 10-12 1609.53 Feb 14th Florida Warm Up 2020 Weekend 1
62 Florida Win 13-12 1543.86 Feb 14th Florida Warm Up 2020 Weekend 1
3 Brigham Young Loss 10-13 1851.02 Feb 14th Florida Warm Up 2020 Weekend 1
11 Minnesota Loss 10-15 1539.17 Feb 15th Florida Warm Up 2020 Weekend 1
30 Texas Loss 8-11 1332.81 Feb 15th Florida Warm Up 2020 Weekend 1
55 Virginia Tech Win 13-6 2066.95 Feb 15th Florida Warm Up 2020 Weekend 1
82 Cornell Win 15-6 1917.05 Feb 16th Florida Warm Up 2020 Weekend 1
31 Texas-Dallas Win 14-11 2010.61 Feb 16th Florida Warm Up 2020 Weekend 1
20 North Carolina-Wilmington Loss 13-15 1610.79 Mar 7th Smoky Mountain Invite 2020
13 Brown Loss 8-13 1431.9 Mar 7th Smoky Mountain Invite 2020
51 Tennessee Win 13-11 1725.57 Mar 7th Smoky Mountain Invite 2020
30 Texas Win 12-10 1936.55 Mar 7th Smoky Mountain Invite 2020
50 Purdue Win 15-11 1878.54 Mar 8th Smoky Mountain Invite 2020
21 North Carolina State Loss 10-13 1492.78 Mar 8th Smoky Mountain Invite 2020
10 Carleton College-CUT Loss 9-15 1518.15 Mar 8th Smoky Mountain Invite 2020
**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)