#133 Claremont (11-10)

avg: 811.23  •  sd: 65.53  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Carleton College-Eclipse** Loss 2-12 938.86 Ignored Feb 8th DIII Grand Prix 2025
48 Whitman Loss 5-11 905.02 Feb 8th DIII Grand Prix 2025
79 Portland Loss 4-11 606.76 Feb 8th DIII Grand Prix 2025
67 Lewis & Clark Loss 9-11 1048.6 Feb 9th DIII Grand Prix 2025
116 Puget Sound Win 10-8 1206.48 Feb 9th DIII Grand Prix 2025
73 Colorado College Loss 2-13 660.16 Feb 9th DIII Grand Prix 2025
52 Oregon State** Loss 5-13 872.23 Ignored Feb 9th DIII Grand Prix 2025
82 California-Irvine Loss 4-9 590.17 Mar 2nd Claremont Classic 2025
123 California-San Diego-B Loss 5-7 582.34 Mar 2nd Claremont Classic 2025
249 California-San Diego-C Win 8-4 538.86 Mar 2nd Claremont Classic 2025
262 Southern California-B** Win 13-0 36.04 Ignored Mar 2nd Claremont Classic 2025
220 Cal State-Long Beach Win 7-1 816.03 Mar 8th Gnomageddon
82 California-Irvine Loss 4-10 590.17 Mar 8th Gnomageddon
123 California-San Diego-B Loss 3-8 310.49 Mar 8th Gnomageddon
249 California-San Diego-C** Win 7-1 574.05 Ignored Mar 8th Gnomageddon
249 California-San Diego-C** Win 10-2 574.05 Ignored Mar 9th Gnomageddon
222 California-Santa Barbara-B Win 6-4 572.18 Mar 9th Gnomageddon
178 Nevada-Reno Win 13-2 1155.8 Mar 29th Sinvite 2025
183 Occidental Win 13-6 1125.62 Mar 29th Sinvite 2025
183 Occidental Win 9-8 650.62 Apr 26th Southwest D III Womens Conferences 2025
183 Occidental Win 12-5 1125.62 Apr 26th Southwest D III 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)