#136 Cal State-Long Beach (6-6)

avg: 1271.41  •  sd: 66.23  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
176 Occidental Win 5-4 1135.03 Feb 18th Santa Clara Tournament 2018
138 Santa Clara Loss 6-7 1134.54 Feb 18th Santa Clara Tournament 2018
249 California-B** Win 13-2 1024.8 Ignored Feb 18th Santa Clara Tournament 2018
176 Occidental Win 10-3 1610.03 Feb 19th Santa Clara Tournament 2018
87 California-Santa Cruz Loss 5-7 1259.61 Feb 19th Santa Clara Tournament 2018
242 California-Davis-B** Win 9-1 1110.9 Ignored Feb 19th Santa Clara Tournament 2018
76 Pacific Lutheran Loss 5-15 1092.71 Mar 24th NW Challenge 2018
67 Puget Sound Loss 5-12 1168.81 Mar 24th NW Challenge 2018
196 Idaho Win 9-8 1051.06 Mar 24th NW Challenge 2018
126 Portland Win 10-6 1809.86 Mar 25th NW Challenge 2018
67 Puget Sound Loss 7-15 1168.81 Mar 25th NW Challenge 2018
72 Simon Fraser University Loss 6-9 1310.44 Mar 25th NW Challenge 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)