#237 Loyola Marymount (5-12)

avg: 907.24  •  sd: 58.8  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
174 California-Santa Cruz-B Loss 10-12 924.86 Feb 8th Stanford Open Mens
214 UCLA-B Win 11-9 1257 Feb 8th Stanford Open Mens
281 California-B Loss 10-12 523.7 Feb 9th Stanford Open Mens
342 Stanford-B Win 11-8 877.9 Feb 9th Stanford Open Mens
214 UCLA-B Win 11-10 1132.79 Feb 9th Stanford Open Mens
187 Arizona Loss 5-10 532.68 Feb 15th Vice Presidents Day Invite 2025
65 Grand Canyon** Loss 4-13 1024.33 Ignored Feb 15th Vice Presidents Day Invite 2025
109 San Diego State Loss 9-12 1065.43 Feb 15th Vice Presidents Day Invite 2025
187 Arizona Loss 7-9 827.24 Feb 16th Vice Presidents Day Invite 2025
121 Arizona State Loss 6-11 800.61 Feb 16th Vice Presidents Day Invite 2025
217 Cal Poly-Pomona Loss 6-11 449.59 Feb 16th Vice Presidents Day Invite 2025
217 Cal Poly-Pomona Win 11-9 1245.49 Apr 12th SoCal D I Mens Conferences 2025
32 California-Santa Barbara** Loss 4-13 1236.17 Ignored Apr 12th SoCal D I Mens Conferences 2025
85 Southern California Loss 7-13 941.8 Apr 12th SoCal D I Mens Conferences 2025
54 UCLA Loss 6-13 1083.5 Apr 12th SoCal D I Mens Conferences 2025
333 Cal State-Long Beach Win 15-12 866.17 Apr 13th SoCal D I Mens Conferences 2025
138 California-Irvine Loss 9-15 784.81 Apr 13th SoCal D I Mens Conferences 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)