#165 Humboldt State (9-8)

avg: 1066.68  •  sd: 86.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
290 Portland State Win 15-3 1218.12 Jan 20th Flat Tail Open Tournament 2018
146 Nevada-Reno Loss 11-12 1024.3 Jan 20th Flat Tail Open Tournament 2018
205 Gonzaga Win 15-6 1525.34 Jan 20th Flat Tail Open Tournament 2018
121 Puget Sound Loss 8-9 1131.91 Jan 21st Flat Tail Open Tournament 2018
271 Central Washington Loss 6-9 288.3 Jan 21st Flat Tail Open Tournament 2018
158 Lewis & Clark Loss 6-8 801.64 Feb 10th Stanford Open 2018
329 California-Irvine Win 7-6 589.87 Feb 10th Stanford Open 2018
85 Colorado College Loss 4-9 799.36 Feb 10th Stanford Open 2018
141 Boston College Win 12-10 1404.29 Feb 11th Stanford Open 2018
65 California-Santa Barbara Win 13-12 1587.37 Feb 11th Stanford Open 2018
186 Cal Poly-Pomona Win 13-10 1311.25 Feb 11th Stanford Open 2018
146 Nevada-Reno Win 13-10 1477.44 Mar 10th Silicon Valley Rally 2018
276 San Jose State Win 13-4 1298.83 Mar 10th Silicon Valley Rally 2018
79 California-Davis Loss 8-13 918.47 Mar 10th Silicon Valley Rally 2018
225 California-B Win 13-11 1082.44 Mar 10th Silicon Valley Rally 2018
195 Sonoma State Loss 10-13 635.17 Mar 11th Silicon Valley Rally 2018
131 Chico State Loss 8-11 823.03 Mar 11th Silicon Valley Rally 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)