#42 Michigan (9-10)

avg: 1566.42  •  sd: 89.28  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
9 Brown Loss 9-13 1606.51 Feb 2nd Florida Warm Up 2024
97 Florida State Win 13-10 1575.91 Feb 2nd Florida Warm Up 2024
20 Northeastern Loss 9-13 1411.75 Feb 2nd Florida Warm Up 2024
17 Brigham Young Loss 12-13 1750.44 Feb 3rd Florida Warm Up 2024
56 Emory Loss 11-12 1322.58 Feb 3rd Florida Warm Up 2024
37 Texas A&M Loss 10-14 1192.24 Feb 4th Florida Warm Up 2024
355 Concordia-Wisconsin** Win 13-2 607.19 Ignored Mar 16th Grand Rapids College Invite
275 Western Michigan** Win 13-2 1102.94 Ignored Mar 16th Grand Rapids College Invite
364 Michigan State-B** Win 13-4 544.62 Ignored Mar 16th Grand Rapids College Invite
102 Davenport Win 10-8 1479.93 Mar 17th Grand Rapids College Invite
155 Grace Win 12-10 1260.36 Mar 17th Grand Rapids College Invite
122 Oberlin Win 11-9 1401.76 Mar 17th Grand Rapids College Invite
1 North Carolina Loss 7-13 1731.03 Mar 30th Easterns 2024
34 Ohio State Win 13-11 1870.71 Mar 30th Easterns 2024
8 Vermont Loss 8-13 1542.44 Mar 30th Easterns 2024
11 Minnesota Loss 5-13 1402.24 Mar 30th Easterns 2024
12 Alabama-Huntsville Loss 10-13 1665.53 Mar 31st Easterns 2024
21 Tufts Win 15-14 1953.7 Mar 31st Easterns 2024
13 North Carolina State Loss 11-14 1633.26 Mar 31st Easterns 2024
**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)