#44 Colorado State (10-9)

avg: 1981.6  •  sd: 68.45  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
87 California-Santa Cruz Win 13-11 1816.59 Jan 27th Santa Barbara Invitational 2018
18 Brigham Young Win 12-11 2411.51 Jan 27th Santa Barbara Invitational 2018
28 Washington University Win 13-11 2342.73 Jan 27th Santa Barbara Invitational 2018
4 Stanford** Loss 3-13 2095.52 Ignored Jan 27th Santa Barbara Invitational 2018
35 Cal Poly-SLO Loss 8-13 1540.39 Jan 28th Santa Barbara Invitational 2018
19 Vermont Loss 6-13 1663.48 Jan 28th Santa Barbara Invitational 2018
38 Victoria Loss 9-10 1895.2 Jan 28th Santa Barbara Invitational 2018
37 Northwestern Loss 7-9 1748.84 Mar 3rd Midwest Throwdown 2018
96 St Olaf Win 12-4 2126.06 Mar 3rd Midwest Throwdown 2018
55 Iowa State Win 9-7 2125.79 Mar 3rd Midwest Throwdown 2018
42 Wisconsin Win 11-10 2128.7 Mar 4th Midwest Throwdown 2018
143 Truman State** Win 12-2 1830.82 Ignored Mar 4th Midwest Throwdown 2018
22 Minnesota Win 11-10 2353.83 Mar 4th Midwest Throwdown 2018
34 Northeastern Loss 12-13 1929.9 Mar 24th Womens Centex 2018
42 Wisconsin Win 11-8 2369.31 Mar 24th Womens Centex 2018
7 Tufts Loss 6-13 1908.79 Mar 24th Womens Centex 2018
66 Virginia Win 10-5 2345.6 Mar 24th Womens Centex 2018
42 Wisconsin Loss 6-12 1424.39 Mar 25th Womens Centex 2018
41 Georgia Tech Loss 9-13 1590.85 Mar 25th Womens Centex 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)