#110 Michigan State (13-11)

avg: 1005.94  •  sd: 70.79  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
58 Davenport Loss 3-6 856.75 Mar 15th Davenport Spring Skirmish
199 Oberlin Win 8-6 741.77 Mar 15th Davenport Spring Skirmish
88 Michigan Tech Loss 6-7 1010.42 Mar 15th Davenport Spring Skirmish
135 Grand Valley Win 9-5 1344.32 Mar 16th Davenport Spring Skirmish
135 Grand Valley Win 9-3 1415.26 Mar 16th Davenport Spring Skirmish
88 Michigan Tech Loss 5-6 1010.42 Mar 16th Davenport Spring Skirmish
92 Iowa State Win 10-8 1374.68 Mar 29th Old Capitol Open 2025
118 Northwestern Win 11-9 1188.82 Mar 29th Old Capitol Open 2025
217 Wisconsin-Milwaukee** Win 13-5 861.14 Ignored Mar 29th Old Capitol Open 2025
62 Chicago Win 5-4 1468.7 Mar 30th Old Capitol Open 2025
142 Saint Louis Win 10-5 1312.31 Mar 30th Old Capitol Open 2025
74 Purdue Loss 1-6 660.76 Mar 30th Old Capitol Open 2025
135 Grand Valley Win 9-8 940.26 Apr 12th Eastern Great Lakes D I Womens Conferences 2025
181 Michigan-B Win 10-5 1110.64 Apr 12th Eastern Great Lakes D I Womens Conferences 2025
254 Purdue-B** Win 12-1 490.29 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
17 Notre Dame** Loss 2-12 1369.03 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
11 Michigan** Loss 1-11 1510.35 Ignored Apr 13th Eastern Great Lakes D I Womens Conferences 2025
135 Grand Valley Win 8-6 1115.75 Apr 26th Great Lakes D I Womens Regionals 2025
67 Illinois Loss 6-13 709.59 Apr 26th Great Lakes D I Womens Regionals 2025
121 Loyola-Chicago Win 10-5 1503.6 Apr 26th Great Lakes D I Womens Regionals 2025
11 Michigan** Loss 0-13 1510.35 Ignored Apr 26th Great Lakes D I Womens Regionals 2025
62 Chicago Loss 4-13 743.7 Apr 27th Great Lakes D I Womens Regionals 2025
121 Loyola-Chicago Loss 5-11 329.71 Apr 27th Great Lakes D I Womens Regionals 2025
118 Northwestern Loss 9-11 690.41 Apr 27th Great Lakes D I Womens 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)