#77 Cal State-Long Beach (12-5)

avg: 1281.81  •  sd: 102.61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
224 Sonoma State** Win 9-2 963.71 Ignored Feb 2nd Presidents Day Qualifiers Women
232 California-Irvine** Win 11-3 886.61 Ignored Feb 2nd Presidents Day Qualifiers Women
23 California Loss 6-9 1499.35 Feb 2nd Presidents Day Qualifiers Women
250 California-Davis-B** Win 13-3 747.51 Ignored Feb 2nd Presidents Day Qualifiers Women
119 UCLA-B Loss 8-10 774.01 Feb 3rd Presidents Day Qualifiers Women
232 California-Irvine** Win 13-3 886.61 Ignored Feb 3rd Presidents Day Qualifiers Women
73 Northern Arizona Win 9-8 1449.82 Feb 9th Stanford Open 2019
224 Sonoma State** Win 13-3 963.71 Ignored Feb 9th Stanford Open 2019
54 Puget Sound Loss 7-10 1102.47 Feb 9th Stanford Open 2019
188 Idaho Win 10-9 721.02 Mar 30th 2019 NW Challenge Tier 2 3
84 Victoria Win 11-6 1792.05 Mar 30th 2019 NW Challenge Tier 2 3
129 Pacific Lutheran Win 11-0 1599.54 Mar 30th 2019 NW Challenge Tier 2 3
270 Gonzaga** Win 11-1 424 Ignored Mar 30th 2019 NW Challenge Tier 2 3
54 Puget Sound Loss 4-11 892.13 Mar 30th 2019 NW Challenge Tier 2 3
123 Boise State Win 13-3 1619.37 Mar 31st 2019 NW Challenge Tier 2 3
55 Portland Loss 7-12 967.46 Mar 31st 2019 NW Challenge Tier 2 3
129 Pacific Lutheran Win 13-0 1599.54 Mar 31st 2019 NW Challenge Tier 2 3
**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)