#178 Nevada-Reno (5-12)

avg: 555.8  •  sd: 87.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
143 California-B Loss 8-9 601.7 Feb 1st Stanford Open Womens
41 Southern California** Loss 0-8 972.33 Ignored Feb 1st Stanford Open Womens
139 Stanford-B Loss 3-9 142.26 Feb 1st Stanford Open Womens
164 Cal Poly-SLO-B Loss 7-11 164.06 Feb 2nd Stanford Open Womens
218 Cal Poly-Humboldt Win 8-6 520.55 Feb 15th Santa Clara University WLT Tournament
143 California-B Loss 3-12 126.7 Feb 15th Santa Clara University WLT Tournament
68 Santa Clara Loss 7-12 775.96 Feb 15th Santa Clara University WLT Tournament
143 California-B Win 7-6 851.7 Feb 16th Santa Clara University WLT Tournament
194 California-Davis-B Win 8-5 922.27 Feb 16th Santa Clara University WLT Tournament
183 Occidental Loss 8-9 400.62 Feb 16th Santa Clara University WLT Tournament
133 Claremont Loss 2-13 211.23 Mar 29th Sinvite 2025
183 Occidental Loss 6-8 225.12 Mar 29th Sinvite 2025
183 Occidental Win 10-6 1021.78 Mar 30th Sinvite 2025
218 Cal Poly-Humboldt Win 15-3 820.06 Apr 12th NorCal D I Womens Conferences 2025
23 California-Davis** Loss 2-15 1275.64 Ignored Apr 12th NorCal D I Womens Conferences 2025
40 California** Loss 2-15 981.98 Ignored Apr 13th NorCal D I Womens Conferences 2025
68 Santa Clara Loss 6-11 749.78 Apr 13th NorCal D I Womens Conferences 2025
**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)