#211 San Diego State (10-13)

avg: 1080.14  •  sd: 66.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
352 Cal Poly-SLO-C Win 11-6 1045.61 Jan 20th Pres Day Quals
230 California-Davis Loss 8-11 649.78 Jan 20th Pres Day Quals
334 California-Santa Barbara-B Win 11-8 947 Jan 20th Pres Day Quals
121 Cal Poly-SLO-B Loss 1-13 801.25 Jan 21st Pres Day Quals
158 UCLA-B Loss 6-11 742.38 Jan 21st Pres Day Quals
192 Loyola Marymount Loss 5-9 626.84 Jan 21st Pres Day Quals
229 Baylor Win 7-5 1347.61 Mar 9th Centex Tier 2 2024
128 Houston Loss 6-13 788.32 Mar 9th Centex Tier 2 2024
258 North Texas Win 9-8 1042.11 Mar 9th Centex Tier 2 2024
190 Texas-Dallas Win 9-6 1576.91 Mar 9th Centex Tier 2 2024
63 Iowa Loss 8-15 1121.88 Mar 10th Centex Tier 2 2024
197 Texas State Win 15-14 1254.97 Mar 10th Centex Tier 2 2024
109 Tarleton State Loss 13-15 1253.43 Mar 10th Centex Tier 2 2024
350 Arizona State-B Win 13-6 1111.42 Mar 24th Southwest Showdown 2024
255 Cal State-Long Beach Loss 8-11 557.18 Mar 24th Southwest Showdown 2024
235 Claremont Win 12-5 1595.21 Mar 24th Southwest Showdown 2024
192 Loyola Marymount Loss 7-13 598.36 Mar 24th Southwest Showdown 2024
255 Cal State-Long Beach Win 11-6 1469.48 Apr 13th SoCal D I Mens Conferences 2024
60 California-Santa Barbara** Loss 5-12 1115.6 Ignored Apr 13th SoCal D I Mens Conferences 2024
24 UCLA** Loss 4-13 1420.7 Ignored Apr 13th SoCal D I Mens Conferences 2024
113 Southern California Loss 6-12 874.72 Apr 13th SoCal D I Mens Conferences 2024
125 California-Irvine Loss 8-10 1129.25 Apr 14th SoCal D I Mens Conferences 2024
192 Loyola Marymount Win 10-7 1545.56 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)