#42 Wisconsin (7-9)

avg: 2003.7  •  sd: 81.41  •  top 16/20: 0.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 Kennesaw State Win 9-5 2546.65 Feb 3rd Queen City Tune Up 2018 College Women
7 Tufts Loss 6-10 2012.63 Feb 3rd Queen City Tune Up 2018 College Women
15 North Carolina State Loss 5-8 1899.47 Feb 3rd Queen City Tune Up 2018 College Women
10 Pittsburgh Loss 5-10 1907.85 Feb 3rd Queen City Tune Up 2018 College Women
112 Illinois Win 14-5 2031.05 Mar 3rd Midwest Throwdown 2018
22 Minnesota Loss 9-13 1810.26 Mar 3rd Midwest Throwdown 2018
37 Northwestern Win 11-8 2393.78 Mar 4th Midwest Throwdown 2018
28 Washington University Win 8-6 2414.38 Mar 4th Midwest Throwdown 2018
44 Colorado State Loss 10-11 1856.6 Mar 4th Midwest Throwdown 2018
55 Iowa State Win 13-10 2174.6 Mar 4th Midwest Throwdown 2018
32 Florida Loss 7-14 1497.36 Mar 24th Womens Centex 2018
66 Virginia Win 10-8 2034.37 Mar 24th Womens Centex 2018
44 Colorado State Loss 8-11 1615.99 Mar 24th Womens Centex 2018
7 Tufts Loss 6-12 1929.48 Mar 24th Womens Centex 2018
44 Colorado State Win 12-6 2560.91 Mar 25th Womens Centex 2018
33 UCLA Loss 7-10 1673.03 Mar 25th Womens Centex 2018
**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)