#234 Claremont (11-9)

avg: 703.14  •  sd: 79.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
190 Portland Win 10-6 1357.91 Feb 3rd Stanford Open 2024
129 San Jose State Loss 3-11 526.45 Feb 3rd Stanford Open 2024
356 Oregon State-B** Win 13-3 591.5 Ignored Feb 3rd Stanford Open 2024
190 Portland Loss 7-11 394.86 Feb 10th DIII Grand Prix
80 Lewis & Clark Loss 7-12 819.17 Feb 10th DIII Grand Prix
286 Reed Win 13-9 840.71 Feb 10th DIII Grand Prix
279 Whitworth Win 13-6 1079.7 Feb 10th DIII Grand Prix
284 Pacific Lutheran Win 10-9 555.87 Feb 11th DIII Grand Prix
59 Whitman** Loss 3-13 836.78 Ignored Feb 11th DIII Grand Prix
163 Xavier Win 9-7 1252.81 Feb 11th DIII Grand Prix
310 Arizona State-B Win 9-7 582.1 Mar 24th Southwest Showdown 2024
227 Cal State-Long Beach Win 11-6 1268.69 Mar 24th Southwest Showdown 2024
212 San Diego State Loss 5-12 170.92 Mar 24th Southwest Showdown 2024
273 Nevada-Reno Loss 7-10 117.29 Mar 24th Southwest Showdown 2024
211 Arizona Loss 3-10 172.8 Mar 30th 2024 Sinvite
110 Arizona State Loss 5-8 738.98 Mar 30th 2024 Sinvite
202 California-B Loss 6-9 407.96 Mar 30th 2024 Sinvite
292 California-Santa Barbara-B Win 15-3 1002.65 Mar 30th 2024 Sinvite
330 California-San Diego-B Win 15-1 771.51 Mar 31st 2024 Sinvite
285 Southern California-B Win 14-9 901.14 Mar 31st 2024 Sinvite
**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)