#222 Notre Dame-B (2-14)

avg: 250.6  •  sd: 154.6  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
107 Florida State** Loss 1-12 582.08 Ignored Mar 16th Tally Classic XVIII
234 Florida Tech Loss 4-10 -538.05 Mar 16th Tally Classic XVIII
55 Georgia Tech** Loss 3-13 948.96 Ignored Mar 16th Tally Classic XVIII
234 Florida Tech Win 8-7 186.95 Mar 17th Tally Classic XVIII
170 Jacksonville State Loss 5-14 161.77 Mar 17th Tally Classic XVIII
155 Tulane Loss 3-9 248.55 Mar 17th Tally Classic XVIII
141 Grand Valley Loss 4-8 357.88 Apr 13th Eastern Great Lakes D I Womens Conferences 2024
12 Michigan** Loss 0-13 1599.69 Ignored Apr 13th Eastern Great Lakes D I Womens Conferences 2024
117 Michigan State Loss 4-6 747.27 Apr 13th Eastern Great Lakes D I Womens Conferences 2024
59 Purdue** Loss 2-13 916.42 Ignored Apr 13th Eastern Great Lakes D I Womens Conferences 2024
190 Michigan-B Loss 8-10 290.47 Apr 14th Eastern Great Lakes D I Womens Conferences 2024
235 Purdue-B Win 8-5 504.83 Apr 14th Eastern Great Lakes D I Womens Conferences 2024
129 Illinois** Loss 1-13 437.5 Ignored Apr 27th Great Lakes D I College Womens Regionals 2024
160 Loyola-Chicago Loss 3-10 218.85 Apr 27th Great Lakes D I College Womens Regionals 2024
12 Michigan** Loss 0-15 1599.69 Ignored Apr 27th Great Lakes D I College Womens Regionals 2024
59 Purdue** Loss 0-13 916.42 Ignored Apr 27th Great Lakes D I College Womens 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)