#192 Loyola Marymount (10-7)

avg: 1155.9  •  sd: 92.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
221 California-B Win 11-4 1648.92 Jan 20th Pres Day Quals
396 California-San Diego-C** Win 13-2 717.11 Ignored Jan 20th Pres Day Quals
211 San Diego State Win 9-5 1609.2 Jan 21st Pres Day Quals
158 UCLA-B Win 10-7 1678.74 Jan 21st Pres Day Quals
230 California-Davis Loss 7-8 890.39 Feb 3rd Stanford Open 2024
344 Chico State** Win 13-2 1140.15 Ignored Feb 3rd Stanford Open 2024
144 Santa Clara Loss 4-9 736.2 Feb 3rd Stanford Open 2024
328 Nevada-Reno Win 13-6 1202 Mar 24th Southwest Showdown 2024
339 Occidental Win 15-1 1158.41 Mar 24th Southwest Showdown 2024
211 San Diego State Win 13-7 1637.67 Mar 24th Southwest Showdown 2024
416 San Diego State-B** Win 15-3 600 Ignored Mar 24th Southwest Showdown 2024
5 Cal Poly-SLO** Loss 1-13 1796.85 Ignored Apr 13th SoCal D I Mens Conferences 2024
125 California-Irvine Loss 7-10 1002.25 Apr 13th SoCal D I Mens Conferences 2024
34 California-San Diego** Loss 5-12 1303.45 Ignored Apr 13th SoCal D I Mens Conferences 2024
255 Cal State-Long Beach Win 8-6 1223.28 Apr 14th SoCal D I Mens Conferences 2024
211 San Diego State Loss 7-10 690.47 Apr 14th SoCal D I Mens Conferences 2024
113 Southern California Loss 7-13 896.5 Apr 14th SoCal D I Mens Conferences 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)