#36 Michigan (10-8)

avg: 1638.31  •  sd: 55.48  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
61 James Madison Win 11-5 2072.52 Feb 3rd Queen City Tune Up 2018 College Open
116 Appalachian State Win 11-7 1741.35 Feb 3rd Queen City Tune Up 2018 College Open
41 Northeastern Win 9-7 1882.7 Feb 3rd Queen City Tune Up 2018 College Open
50 Notre Dame Win 11-5 2139.28 Feb 3rd Queen City Tune Up 2018 College Open
42 Connecticut Loss 8-9 1470.56 Feb 3rd Queen City Tune Up 2018 College Open
4 Minnesota Loss 9-12 1724.55 Feb 16th Warm Up A Florida Affair 2018
31 LSU Loss 10-13 1371.42 Feb 16th Warm Up A Florida Affair 2018
160 Oklahoma Win 13-5 1692.6 Feb 16th Warm Up A Florida Affair 2018
41 Northeastern Loss 8-11 1237.75 Feb 16th Warm Up A Florida Affair 2018
168 South Florida Win 15-11 1445.16 Feb 17th Warm Up A Florida Affair 2018
81 Florida State Win 10-9 1533.72 Feb 17th Warm Up A Florida Affair 2018
10 Virginia Tech Loss 8-13 1427.14 Feb 17th Warm Up A Florida Affair 2018
111 Arizona State Win 12-9 1634.58 Feb 18th Warm Up A Florida Affair 2018
81 Florida State Win 11-7 1875.62 Feb 18th Warm Up A Florida Affair 2018
1 North Carolina** Loss 5-15 1745.34 Ignored Mar 31st Easterns 2018
33 Maryland Win 13-12 1809.28 Mar 31st Easterns 2018
14 Florida Loss 13-15 1672.64 Mar 31st Easterns 2018
13 Wisconsin Loss 7-13 1359.59 Mar 31st Easterns 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)