#49 Michigan State (18-4)

avg: 1778.48  •  sd: 81.74  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 Colorado College Win 7-6 1533.16 Mar 16th College Mens Centex Tier 1
41 Oklahoma Christian Win 12-8 2294.03 Mar 16th College Mens Centex Tier 1
38 Texas A&M Loss 6-13 1285.46 Mar 16th College Mens Centex Tier 1
116 LSU Win 12-9 1776.46 Mar 16th College Mens Centex Tier 1
25 Middlebury Loss 7-10 1614.4 Mar 17th College Mens Centex Tier 1
193 Grinnell** Win 11-3 1754.63 Ignored Mar 30th Old Capitol Open 2024
400 Iowa-B** Win 13-0 642.04 Ignored Mar 30th Old Capitol Open 2024
200 Northern Iowa** Win 11-3 1721.31 Ignored Mar 30th Old Capitol Open 2024
100 Colorado-B Win 11-7 1980.32 Mar 31st Old Capitol Open 2024
63 Iowa Win 9-8 1811.69 Mar 31st Old Capitol Open 2024
185 Minnesota-Duluth** Win 10-4 1777.59 Ignored Mar 31st Old Capitol Open 2024
223 Eastern Michigan Win 11-6 1586.68 Apr 13th Michigan D I Mens Conferences 2024
135 Grand Valley Win 13-8 1867.31 Apr 13th Michigan D I Mens Conferences 2024
280 Western Michigan** Win 6-2 1428.64 Ignored Apr 13th Michigan D I Mens Conferences 2024
37 Michigan Win 12-9 2231.11 Apr 14th Michigan D I Mens Conferences 2024
37 Michigan Loss 10-14 1487.04 Apr 14th Michigan D I Mens Conferences 2024
61 Chicago Win 11-8 2059.94 Apr 27th Great Lakes D I College Mens Regionals 2024
135 Grand Valley Win 13-10 1699.3 Apr 27th Great Lakes D I College Mens Regionals 2024
357 Michigan State-B** Win 15-1 1074.64 Ignored Apr 27th Great Lakes D I College Mens Regionals 2024
96 Notre Dame Loss 13-15 1316.81 Apr 28th Great Lakes D I College Mens Regionals 2024
106 Northwestern Win 15-9 1998.39 Apr 28th Great Lakes D I College Mens Regionals 2024
62 Purdue Win 15-14 1814.75 Apr 28th Great Lakes D I College Mens Regionals 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)