#129 San Jose State (12-4)

avg: 1126.45  •  sd: 95.31  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
234 Claremont Win 11-3 1303.14 Feb 3rd Stanford Open 2024
356 Oregon State-B** Win 13-4 591.5 Ignored Feb 3rd Stanford Open 2024
190 Portland Loss 7-9 582.41 Feb 3rd Stanford Open 2024
349 Cal Poly-Humboldt** Win 12-4 627.1 Ignored Mar 9th Silicon Valley Rally 2024
202 California-B Win 10-8 1089.19 Mar 9th Silicon Valley Rally 2024
194 California-Davis Win 10-8 1110.73 Mar 9th Silicon Valley Rally 2024
202 California-B Loss 6-9 407.96 Mar 10th Silicon Valley Rally 2024
292 California-Santa Barbara-B** Win 13-4 1002.65 Ignored Mar 10th Silicon Valley Rally 2024
164 UCLA-B Win 13-6 1571.59 Mar 10th Silicon Valley Rally 2024
227 Cal State-Long Beach Win 7-3 1322 Mar 30th 2024 Sinvite
330 California-San Diego-B** Win 12-1 771.51 Ignored Mar 30th 2024 Sinvite
79 Grand Canyon Loss 5-7 1012.55 Mar 30th 2024 Sinvite
285 Southern California-B** Win 11-4 1027.27 Ignored Mar 30th 2024 Sinvite
110 Arizona State Win 11-6 1739.28 Mar 31st 2024 Sinvite
202 California-B Win 12-8 1267.68 Mar 31st 2024 Sinvite
79 Grand Canyon Loss 5-11 740.7 Mar 31st 2024 Sinvite
**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)