#49 Northwestern (7-16)

avg: 1637.69  •  sd: 62.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
4 Pittsburgh Loss 7-13 1627.39 Feb 8th Florida Warm Up 2019
48 Kennesaw State Loss 8-13 1150.33 Feb 8th Florida Warm Up 2019
28 Northeastern Win 12-11 1900.83 Feb 8th Florida Warm Up 2019
6 Brigham Young Loss 9-13 1716.17 Feb 9th Florida Warm Up 2019
2 Brown Loss 10-12 1991.03 Feb 9th Florida Warm Up 2019
65 Florida Loss 10-11 1410.75 Feb 9th Florida Warm Up 2019
80 Oklahoma Loss 13-15 1237.79 Feb 9th Florida Warm Up 2019
83 Rutgers Win 14-13 1557.97 Feb 10th Florida Warm Up 2019
55 Florida State Win 13-6 2211.67 Feb 10th Florida Warm Up 2019
6 Brigham Young Loss 9-11 1885.53 Mar 2nd Stanford Invite 2019
21 California Loss 6-13 1243.46 Mar 2nd Stanford Invite 2019
3 Oregon Loss 8-13 1692.83 Mar 2nd Stanford Invite 2019
14 Ohio State Loss 9-12 1646.7 Mar 2nd Stanford Invite 2019
50 Stanford Win 12-11 1757.74 Mar 3rd Stanford Invite 2019
17 Minnesota Loss 6-10 1454.89 Mar 3rd Stanford Invite 2019
10 Washington Loss 5-13 1444.51 Mar 3rd Stanford Invite 2019
4 Pittsburgh Loss 7-13 1627.39 Mar 30th Easterns 2019 Men
32 William & Mary Loss 10-12 1508.56 Mar 30th Easterns 2019 Men
20 Tufts Win 13-12 1989.15 Mar 30th Easterns 2019 Men
26 North Carolina-Wilmington Win 13-11 2009.82 Mar 30th Easterns 2019 Men
45 California-Santa Barbara Loss 7-15 1063.25 Mar 31st Easterns 2019 Men
47 Maryland Win 13-10 1984.47 Mar 31st Easterns 2019 Men
54 Virginia Tech Loss 11-12 1494.44 Mar 31st Easterns 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)