#164 Arizona State (6-5)

avg: 1103.22  •  sd: 94.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
434 Southern California-B** Win 9-1 389.71 Ignored Feb 2nd Presidents Day Qualifiers Men
261 Cal Poly-SLO-B Win 11-8 1186.75 Feb 2nd Presidents Day Qualifiers Men
328 Caltech Win 7-5 892.92 Feb 2nd Presidents Day Qualifiers Men
265 Cal State-Long Beach Win 9-5 1329.76 Feb 3rd Presidents Day Qualifiers Men
169 Chico State Loss 6-8 783.81 Feb 3rd Presidents Day Qualifiers Men
244 Colorado-B Win 11-5 1477.2 Feb 3rd Presidents Day Qualifiers Men
98 Kansas Loss 7-11 896.29 Mar 30th Huck Finn XXIII
55 Florida State Loss 5-7 1283.53 Mar 30th Huck Finn XXIII
92 John Brown Win 11-10 1502.68 Mar 31st Huck Finn XXIII
86 Marquette Loss 5-10 852.18 Mar 31st Huck Finn XXIII
111 Washington University Loss 3-6 766.77 Mar 31st Huck Finn XXIII
**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)