#197 San Jose State (8-3)

avg: 951.05  •  sd: 78.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
232 Chico State Win 7-6 907.35 Feb 4th Stanford Open
159 Puget Sound Loss 4-8 550.19 Feb 4th Stanford Open
111 Washington-B Win 9-7 1597.9 Feb 4th Stanford Open
221 California-B Win 7-6 979.61 Feb 5th Stanford Open
342 Santa Clara-B** Win 12-5 764.22 Ignored Feb 5th Stanford Open
265 Fresno State Win 10-8 929.55 Mar 11th Silicon Valley Rally
105 California-Davis Loss 6-11 798.19 Mar 11th Silicon Valley Rally
291 California-Santa Cruz-B Win 9-2 1123.06 Mar 11th Silicon Valley Rally
178 San Diego State Loss 6-8 739.35 Mar 11th Silicon Valley Rally
232 Chico State Win 9-7 1061.69 Mar 12th Silicon Valley Rally
320 Stanford-B Win 9-5 842.72 Mar 12th Silicon Valley Rally
**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)