#100 Arizona (10-10)

avg: 1335.48  •  sd: 84.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
17 Colorado State Loss 11-12 1744.76 Jan 27th Santa Barbara Invitational 2018
148 San Diego State Loss 8-12 705.92 Jan 27th Santa Barbara Invitational 2018
20 Cal Poly-SLO Loss 7-13 1285.59 Jan 27th Santa Barbara Invitational 2018
67 Utah Win 13-11 1686.81 Jan 27th Santa Barbara Invitational 2018
146 Nevada-Reno Loss 8-13 653.14 Jan 28th Santa Barbara Invitational 2018
74 Washington University Loss 10-13 1090.35 Jan 28th Santa Barbara Invitational 2018
214 California-Santa Cruz Win 8-4 1470.38 Feb 10th Stanford Open 2018
32 California Loss 5-12 1095.8 Feb 10th Stanford Open 2018
320 Caltech** Win 11-3 1104.85 Ignored Feb 10th Stanford Open 2018
35 Air Force Loss 6-13 1039.57 Feb 11th Stanford Open 2018
57 Whitman Win 12-8 1947.71 Feb 11th Stanford Open 2018
53 UCLA Win 13-10 1862.56 Feb 11th Stanford Open 2018
131 Chico State Loss 8-9 1063.64 Feb 11th Stanford Open 2018
129 Claremont Win 13-7 1753.39 Mar 24th Trouble in Vegas 2018
202 Utah Valley Win 13-3 1532.09 Mar 24th Trouble in Vegas 2018
130 North Texas Loss 5-8 738.53 Mar 24th Trouble in Vegas 2018
214 California-Santa Cruz Win 11-4 1505.57 Mar 24th Trouble in Vegas 2018
90 Northern Arizona Win 6-5 1502.6 Mar 24th Trouble in Vegas 2018
235 Arizona State-B Win 12-5 1402.66 Mar 25th Trouble in Vegas 2018
67 Utah Loss 6-9 1039.41 Mar 25th Trouble in Vegas 2018
**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)