#158 Claremont (3-9)

avg: 826.85  •  sd: 82.82  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
234 Nevada-Reno Win 13-3 871.07 Feb 9th Stanford Open 2019
21 Cal Poly-SLO** Loss 1-13 1343.59 Ignored Feb 9th Stanford Open 2019
68 Lewis & Clark Loss 5-11 730.23 Feb 9th Stanford Open 2019
136 Occidental Loss 8-9 813.15 Mar 2nd 2019 Claremont Ultimate Classic
108 Southern California Loss 4-13 472.45 Mar 2nd 2019 Claremont Ultimate Classic
108 Southern California Loss 9-11 823.24 Mar 2nd 2019 Claremont Ultimate Classic
55 Portland** Loss 3-13 887.97 Ignored Mar 30th 2019 NW Challenge Tier 2 3
123 Boise State Loss 8-11 653.76 Mar 30th 2019 NW Challenge Tier 2 3
68 Lewis & Clark Loss 9-12 984.87 Mar 30th 2019 NW Challenge Tier 2 3
23 California** Loss 1-13 1317.92 Ignored Mar 30th 2019 NW Challenge Tier 2 3
188 Idaho Win 12-6 1175.33 Mar 31st 2019 NW Challenge Tier 2 3
270 Gonzaga** Win 13-0 424 Ignored 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)