#105 Chico State (7-13)

avg: 1479.34  •  sd: 76.39  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
87 California-Santa Cruz Win 10-8 1850.42 Feb 10th Stanford Open 2018
118 Lewis & Clark Win 9-7 1675.79 Feb 10th Stanford Open 2018
73 San Diego State Win 8-6 2024.22 Feb 10th Stanford Open 2018
87 California-Santa Cruz Loss 5-10 1013.85 Feb 11th Stanford Open 2018
77 Brown Loss 8-9 1563.92 Feb 11th Stanford Open 2018
67 Puget Sound Win 8-7 1893.81 Feb 11th Stanford Open 2018
79 Chicago Loss 5-9 1124.33 Feb 17th Presidents Day Invitational Tournament 2018
4 Stanford** Loss 1-13 2095.52 Ignored Feb 17th Presidents Day Invitational Tournament 2018
36 Colorado College Loss 3-13 1433.18 Feb 17th Presidents Day Invitational Tournament 2018
55 Iowa State Loss 7-13 1288.93 Feb 17th Presidents Day Invitational Tournament 2018
37 Northwestern Loss 3-14 1428.18 Feb 18th Presidents Day Invitational Tournament 2018
16 Western Washington** Loss 1-15 1744.39 Ignored Feb 18th Presidents Day Invitational Tournament 2018
61 California-Davis Loss 8-9 1692.92 Feb 19th Presidents Day Invitational Tournament 2018
98 Northern Arizona Loss 5-9 994.79 Mar 24th Trouble in Vegas 2018
120 California-San Diego-B Win 8-5 1832.31 Mar 24th Trouble in Vegas 2018
63 Arizona Loss 2-10 1186.23 Mar 24th Trouble in Vegas 2018
200 Nevada-Reno Win 6-4 1248.79 Mar 24th Trouble in Vegas 2018
73 San Diego State Loss 2-13 1123.73 Mar 25th Trouble in Vegas 2018
138 Santa Clara Win 8-3 1859.54 Mar 25th Trouble in Vegas 2018
63 Arizona Loss 7-8 1661.23 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)