#357 San Jose State (2-7)

avg: 448.54  •  sd: 84.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
353 California-San Diego-B Win 11-10 600.68 Feb 2nd Presidents Day Qualifiers Men
272 Arizona State-B Loss 4-7 280.31 Feb 2nd Presidents Day Qualifiers Men
431 Cal Poly-SLO-C Win 13-5 519.84 Feb 2nd Presidents Day Qualifiers Men
254 Cal Poly-Pomona Loss 4-12 240.19 Feb 2nd Presidents Day Qualifiers Men
265 Cal State-Long Beach Loss 9-11 551.49 Feb 3rd Presidents Day Qualifiers Men
367 Texas-B Loss 10-11 266.24 Feb 9th Stanford Open 2019
90 Santa Clara** Loss 5-13 786.86 Ignored Feb 9th Stanford Open 2019
58 Whitman** Loss 0-13 979.65 Ignored Feb 9th Stanford Open 2019
184 California-B Loss 7-10 642.85 Feb 9th Stanford Open 2019
**Blowout Eligible

FAQ

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
  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
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)