#131 Grand Valley (11-13)

avg: 814.79  •  sd: 60.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
58 Davenport Loss 2-10 803.13 Mar 15th Davenport Spring Skirmish
199 Oberlin Win 8-4 1005.64 Mar 15th Davenport Spring Skirmish
87 Michigan Tech Loss 0-9 533.92 Mar 15th Davenport Spring Skirmish
58 Davenport Loss 1-9 803.13 Mar 16th Davenport Spring Skirmish
110 Michigan State Loss 5-9 476.97 Mar 16th Davenport Spring Skirmish
110 Michigan State Loss 3-9 406.03 Mar 16th Davenport Spring Skirmish
213 Dayton Win 9-4 868.9 Mar 29th Corny Classic College 2025
180 Michigan-B Win 8-2 1142.67 Mar 29th Corny Classic College 2025
66 Illinois Loss 7-12 788.02 Mar 29th Corny Classic College 2025
197 Indiana Win 6-5 579.59 Mar 30th Corny Classic College 2025
144 Knox Win 7-6 850.69 Mar 30th Corny Classic College 2025
66 Illinois Loss 6-7 1183.53 Mar 30th Corny Classic College 2025
180 Michigan-B Win 11-5 1142.67 Apr 12th Eastern Great Lakes D I Womens Conferences 2025
17 Notre Dame** Loss 2-12 1370.55 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
110 Michigan State Loss 8-9 881.03 Apr 12th Eastern Great Lakes D I Womens Conferences 2025
254 Purdue-B** Win 13-1 485.98 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
246 Notre Dame-B** Win 13-1 620.99 Ignored Apr 13th Eastern Great Lakes D I Womens Conferences 2025
197 Indiana Win 13-0 1054.59 Apr 13th Eastern Great Lakes D I Womens Conferences 2025
66 Illinois Loss 7-10 918.86 Apr 26th Great Lakes D I Womens Regionals 2025
120 Loyola-Chicago Loss 4-8 363.98 Apr 26th Great Lakes D I Womens Regionals 2025
110 Michigan State Loss 6-8 705.54 Apr 26th Great Lakes D I Womens Regionals 2025
11 Michigan** Loss 0-13 1513.92 Ignored Apr 26th Great Lakes D I Womens Regionals 2025
246 Notre Dame-B** Win 15-0 620.99 Ignored Apr 27th Great Lakes D I Womens Regionals 2025
180 Michigan-B Win 10-8 805.34 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)