#235 Claremont (14-9)

avg: 995.21  •  sd: 79.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
178 Portland Win 10-6 1704.93 Feb 3rd Stanford Open 2024
124 San Jose State Loss 3-11 792.34 Feb 3rd Stanford Open 2024
361 Oregon State-B Win 13-3 1040.58 Feb 3rd Stanford Open 2024
178 Portland Loss 7-11 741.87 Feb 10th DIII Grand Prix
81 Lewis & Clark Loss 7-12 1066.97 Feb 10th DIII Grand Prix
239 Reed Win 13-9 1407.92 Feb 10th DIII Grand Prix
276 Whitworth Win 13-6 1446.13 Feb 10th DIII Grand Prix
291 Pacific Lutheran Win 10-9 873.55 Feb 11th DIII Grand Prix
52 Whitman** Loss 3-13 1156.72 Ignored Feb 11th DIII Grand Prix
173 Xavier Win 9-7 1512.77 Feb 11th DIII Grand Prix
350 Arizona State-B Win 9-7 790.76 Mar 24th Southwest Showdown 2024
255 Cal State-Long Beach Win 11-6 1469.48 Mar 24th Southwest Showdown 2024
211 San Diego State Loss 5-12 480.14 Mar 24th Southwest Showdown 2024
328 Nevada-Reno Loss 7-10 212.33 Mar 24th Southwest Showdown 2024
219 Arizona Loss 3-10 452.48 Mar 30th 2024 Sinvite
133 Arizona State Loss 5-8 921.49 Mar 30th 2024 Sinvite
221 California-B Loss 6-9 630.35 Mar 30th 2024 Sinvite
334 California-Santa Barbara-B Win 15-3 1181.39 Mar 30th 2024 Sinvite
332 California-San Diego-B Win 15-1 1191.08 Mar 31st 2024 Sinvite
298 Southern California-B Win 14-9 1194.01 Mar 31st 2024 Sinvite
- Caltech** Win 15-1 812.26 Ignored Apr 21st Southwest D III Mens Conferences 2024
- San Diego** Win 15-0 425.81 Ignored Apr 21st Southwest D III Mens Conferences 2024
339 Occidental Win 11-5 1158.41 Apr 21st Southwest D III Mens 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)