#138 Santa Clara (12-8)

avg: 1259.54  •  sd: 73.17  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
124 Carleton College-Eclipse Win 11-8 1718.13 Feb 10th Stanford Open 2018
147 Humboldt State Win 13-9 1611.13 Feb 10th Stanford Open 2018
189 Sonoma State Win 11-6 1486.35 Feb 10th Stanford Open 2018
65 Utah Loss 8-11 1416.31 Feb 11th Stanford Open 2018
87 California-Santa Cruz Loss 6-12 1008.44 Feb 11th Stanford Open 2018
67 Puget Sound Loss 5-11 1168.81 Feb 11th Stanford Open 2018
176 Occidental Win 10-3 1610.03 Feb 18th Santa Clara Tournament 2018
249 California-B** Win 13-4 1024.8 Ignored Feb 18th Santa Clara Tournament 2018
136 Cal State-Long Beach Win 7-6 1396.41 Feb 18th Santa Clara Tournament 2018
176 Occidental Win 13-6 1610.03 Feb 19th Santa Clara Tournament 2018
87 California-Santa Cruz Loss 8-11 1222.14 Feb 19th Santa Clara Tournament 2018
265 Cal Poly-SLO-B** Win 12-3 620.41 Ignored Feb 19th Santa Clara Tournament 2018
- Utah State Win 8-6 898.9 Mar 24th Trouble in Vegas 2018
111 California-Irvine Loss 1-8 833.34 Mar 24th Trouble in Vegas 2018
100 Arizona State Loss 3-10 906.24 Mar 24th Trouble in Vegas 2018
228 New Mexico Win 12-5 1277.57 Mar 24th Trouble in Vegas 2018
197 Arizona-B Loss 2-3 789.92 Mar 24th Trouble in Vegas 2018
176 Occidental Win 7-5 1338.18 Mar 25th Trouble in Vegas 2018
176 Occidental Win 7-3 1610.03 Mar 25th Trouble in Vegas 2018
105 Chico State Loss 3-8 879.34 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)