#120 Loyola-Chicago (12-11)

avg: 928.79  •  sd: 87.92  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
128 Winona State Loss 4-6 478.99 Mar 22nd Meltdown 2025
192 St Olaf-B Win 11-4 1070.42 Mar 22nd Meltdown 2025
208 Wheaton (Illinois)** Win 11-4 894.48 Ignored Mar 22nd Meltdown 2025
28 Missouri** Loss 2-13 1131.99 Ignored Mar 22nd Meltdown 2025
128 Winona State Win 8-2 1444.6 Mar 23rd Meltdown 2025
28 Missouri Win 6-4 2097.6 Mar 23rd Meltdown 2025
197 Indiana Win 13-2 1054.59 Mar 29th Corny Classic College 2025
221 North Park** Win 13-0 808.2 Ignored Mar 29th Corny Classic College 2025
215 Washington University-B** Win 13-3 862.07 Ignored Mar 29th Corny Classic College 2025
122 Cincinnati Loss 3-5 492.99 Mar 30th Corny Classic College 2025
197 Indiana Win 9-6 873.16 Mar 30th Corny Classic College 2025
200 Truman State Win 10-9 558.99 Mar 30th Corny Classic College 2025
63 Chicago Loss 2-13 744.44 Apr 12th Illinois D I Womens Conferences 2025
66 Illinois Loss 3-12 708.53 Apr 12th Illinois D I Womens Conferences 2025
117 Northwestern Loss 5-6 814.88 Apr 12th Illinois D I Womens Conferences 2025
253 Northwestern-B** Win 13-1 508.95 Ignored Apr 12th Illinois D I Womens Conferences 2025
131 Grand Valley Win 8-4 1379.59 Apr 26th Great Lakes D I Womens Regionals 2025
66 Illinois Loss 5-10 734.63 Apr 26th Great Lakes D I Womens Regionals 2025
110 Michigan State Loss 5-10 432.13 Apr 26th Great Lakes D I Womens Regionals 2025
11 Michigan** Loss 1-13 1513.92 Ignored Apr 26th Great Lakes D I Womens Regionals 2025
17 Notre Dame** Loss 1-13 1370.55 Ignored Apr 27th Great Lakes D I Womens Regionals 2025
72 Purdue Loss 5-13 660.94 Apr 27th Great Lakes D I Womens Regionals 2025
110 Michigan State Win 11-5 1606.03 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)