#18 Michigan (18-3)

avg: 1908.77  •  sd: 46.54  •  top 16/20: 80.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Rutgers Win 13-10 1761.11 Feb 8th Florida Warm Up 2019
127 Boston College Win 13-9 1693.29 Feb 8th Florida Warm Up 2019
65 Florida Win 13-6 2135.75 Feb 8th Florida Warm Up 2019
69 Emory Win 13-6 2108.46 Feb 9th Florida Warm Up 2019
31 Texas A&M Win 12-8 2189.57 Feb 9th Florida Warm Up 2019
150 Cornell** Win 13-0 1778.08 Ignored Feb 9th Florida Warm Up 2019
22 Georgia Loss 8-13 1338.33 Feb 9th Florida Warm Up 2019
31 Texas A&M Loss 13-14 1623.41 Feb 10th Florida Warm Up 2019
29 Texas-Dallas Win 10-9 1896.91 Feb 10th Florida Warm Up 2019
120 James Madison Win 13-6 1882.8 Mar 16th Oak Creek Invite 2019
142 Princeton Win 13-6 1809.71 Mar 16th Oak Creek Invite 2019
163 SUNY-Geneseo** Win 13-4 1706.58 Ignored Mar 16th Oak Creek Invite 2019
147 Delaware** Win 13-1 1787.94 Ignored Mar 16th Oak Creek Invite 2019
33 Johns Hopkins Win 15-12 2031.66 Mar 17th Oak Creek Invite 2019
73 Temple Win 13-9 1899.44 Mar 17th Oak Creek Invite 2019
32 William & Mary Win 14-13 1871.68 Mar 17th Oak Creek Invite 2019
31 Texas A&M Win 9-8 1873.41 Mar 30th Huck Finn XXIII
23 Texas Tech Loss 9-10 1706.13 Mar 30th Huck Finn XXIII
38 Purdue Win 10-8 1969.71 Mar 31st Huck Finn XXIII
39 Vermont Win 9-6 2124.33 Mar 31st Huck Finn XXIII
37 Illinois Win 11-5 2320.39 Mar 31st Huck Finn XXIII
**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)