#296 Loyola-Chicago (8-15)

avg: 717.04  •  sd: 76.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
242 Grace Win 8-7 1034.7 Mar 22nd Meltdown 2025
163 Truman State Loss 7-8 1097.22 Mar 22nd Meltdown 2025
300 Wisconsin-Stevens Point Loss 6-10 185.45 Mar 22nd Meltdown 2025
384 Carthage Win 10-1 819.74 Mar 23rd Meltdown 2025
308 Illinois-Chicago Win 9-5 1175.75 Mar 23rd Meltdown 2025
292 Ball State Loss 9-10 605.42 Mar 29th Corny Classic College 2025
256 Illinois-B Win 6-4 1226.43 Mar 29th Corny Classic College 2025
345 North Park Win 10-9 598.64 Mar 29th Corny Classic College 2025
190 Vanderbilt Loss 6-10 610.16 Mar 29th Corny Classic College 2025
205 Missouri State Loss 4-8 479.32 Mar 30th Corny Classic College 2025
260 Toledo Loss 4-11 237.21 Mar 30th Corny Classic College 2025
49 Chicago** Loss 1-13 1148.65 Ignored Apr 12th Illinois D I Mens Conferences 2025
257 DePaul Win 11-9 1106.23 Apr 12th Illinois D I Mens Conferences 2025
121 Northwestern Loss 5-11 763.35 Apr 12th Illinois D I Mens Conferences 2025
93 Southern Illinois-Edwardsville** Loss 2-15 887.63 Ignored Apr 12th Illinois D I Mens Conferences 2025
257 DePaul Win 10-9 982.03 Apr 13th Illinois D I Mens Conferences 2025
308 Illinois-Chicago Loss 5-11 46.69 Apr 13th Illinois D I Mens Conferences 2025
308 Illinois-Chicago Win 11-5 1246.69 Apr 13th Illinois D I Mens Conferences 2025
153 Kentucky Loss 7-13 708.05 Apr 26th Great Lakes D I Mens Regionals 2025
26 Michigan** Loss 5-13 1327.18 Ignored Apr 26th Great Lakes D I Mens Regionals 2025
61 Michigan State** Loss 0-13 1058.64 Ignored Apr 26th Great Lakes D I Mens Regionals 2025
238 Illinois State Loss 10-15 470.92 Apr 27th Great Lakes D I Mens Regionals 2025
256 Illinois-B Loss 6-9 442.25 Apr 27th Great Lakes D I Mens 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)