#26 Michigan (17-6)

avg: 1927.18  •  sd: 67.44  •  top 16/20: 13.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Alabama-Huntsville Win 12-9 1975.28 Jan 31st Florida Warm Up 2025
51 Cornell Win 11-8 2089.5 Jan 31st Florida Warm Up 2025
71 Florida Win 11-8 1975.86 Jan 31st Florida Warm Up 2025
90 Texas A&M Win 11-10 1621.44 Feb 1st Florida Warm Up 2025
1 Massachusetts Loss 7-13 1859.14 Feb 1st Florida Warm Up 2025
12 Washington University Loss 8-13 1608.14 Feb 1st Florida Warm Up 2025
20 Vermont Win 12-11 2153.48 Feb 2nd Florida Warm Up 2025
19 Tufts Win 13-9 2449.1 Mar 29th Easterns 2025
20 Vermont Loss 9-13 1609.91 Mar 29th Easterns 2025
27 Minnesota Loss 5-13 1291.78 Mar 29th Easterns 2025
4 North Carolina Loss 3-13 1759.83 Mar 29th Easterns 2025
59 James Madison Loss 11-13 1440.89 Mar 30th Easterns 2025
29 Pittsburgh Win 13-11 2107.65 Mar 30th Easterns 2025
202 Eastern Michigan** Win 13-5 1659.12 Ignored Apr 12th Michigan D I Mens Conferences 2025
148 Grand Valley** Win 13-3 1879.74 Ignored Apr 12th Michigan D I Mens Conferences 2025
302 Western Michigan** Win 13-1 1280.56 Ignored Apr 12th Michigan D I Mens Conferences 2025
61 Michigan State Win 13-8 2154.8 Apr 12th Michigan D I Mens Conferences 2025
153 Kentucky** Win 13-2 1865.58 Ignored Apr 26th Great Lakes D I Mens Regionals 2025
296 Loyola-Chicago** Win 13-5 1317.04 Ignored Apr 26th Great Lakes D I Mens Regionals 2025
61 Michigan State Win 13-8 2154.8 Apr 26th Great Lakes D I Mens Regionals 2025
58 Illinois Win 13-9 2091.51 Apr 27th Great Lakes D I Mens Regionals 2025
56 Indiana Win 14-9 2152.77 Apr 27th Great Lakes D I Mens Regionals 2025
121 Northwestern Win 15-8 1928.16 Apr 27th Great Lakes D I Mens Regionals 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)