#67 Nevada-Reno (11-6)

avg: 1439.92  •  sd: 79.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
148 Cal Poly-SLO-B Win 11-1 1481.05 Feb 17th Santa Clara University Presidents Day
88 California-Irvine Win 9-8 1434.28 Feb 17th Santa Clara University Presidents Day
131 Occidental Win 13-3 1614.86 Feb 17th Santa Clara University Presidents Day
79 San Diego State Loss 7-9 1080.92 Feb 18th Santa Clara University Presidents Day
184 California-Davis-B Win 8-4 1161.09 Feb 18th Santa Clara University Presidents Day
88 California-Irvine Win 7-6 1434.28 Feb 18th Santa Clara University Presidents Day
60 Utah State Loss 4-7 1012.98 Mar 9th Big Sky Brawl 2024
116 Montana Win 11-3 1712.89 Mar 9th Big Sky Brawl 2024
171 Montana State** Win 6-2 1354.59 Ignored Mar 9th Big Sky Brawl 2024
187 Colorado Mines** Win 8-2 1178.65 Ignored Mar 10th Big Sky Brawl 2024
60 Utah State Loss 5-7 1181 Mar 10th Big Sky Brawl 2024
116 Montana Win 8-4 1677.69 Mar 10th Big Sky Brawl 2024
5 Stanford** Loss 3-15 1919.88 Ignored Apr 13th NorCal D I Womens Conferences 2024
28 California Loss 4-15 1256.51 Apr 13th NorCal D I Womens Conferences 2024
72 Santa Clara Win 11-10 1521.79 Apr 14th NorCal D I Womens Conferences 2024
248 Cal Poly-Humboldt** Win 13-0 600 Ignored Apr 14th NorCal D I Womens Conferences 2024
17 California-Santa Cruz Loss 9-11 1829.26 Apr 14th NorCal D I Womens Conferences 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)