#122 Claremont (9-10)

avg: 1150.79  •  sd: 64.58  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
35 Carleton College-Eclipse** Loss 2-12 1351.89 Ignored Feb 8th DIII Grand Prix 2025
44 Whitman Loss 5-11 1230.92 Feb 8th DIII Grand Prix 2025
65 Portland Loss 4-11 1026.91 Feb 8th DIII Grand Prix 2025
61 Lewis & Clark Loss 9-11 1407.23 Feb 9th DIII Grand Prix 2025
121 Puget Sound Win 10-8 1434.39 Feb 9th DIII Grand Prix 2025
72 Colorado College Loss 2-13 956.22 Feb 9th DIII Grand Prix 2025
54 Oregon State Loss 5-13 1115.45 Feb 9th DIII Grand Prix 2025
63 California-Irvine Loss 4-9 1042.75 Mar 2nd Claremont Classic 2025
104 California-San Diego-B Loss 5-7 938.71 Mar 2nd Claremont Classic 2025
220 California-San Diego-C Win 8-4 949.28 Mar 2nd Claremont Classic 2025
243 Southern California-B** Win 13-0 384.6 Ignored Mar 2nd Claremont Classic 2025
194 Cal State-Long Beach Win 7-1 1195.6 Mar 8th Gnomageddon
63 California-Irvine Loss 4-10 1042.75 Mar 8th Gnomageddon
104 California-San Diego-B Loss 3-8 666.85 Mar 8th Gnomageddon
220 California-San Diego-C** Win 7-1 984.47 Ignored Mar 8th Gnomageddon
220 California-San Diego-C** Win 10-2 984.47 Ignored Mar 9th Gnomageddon
199 California-Santa Barbara-B Win 6-4 937.91 Mar 9th Gnomageddon
171 Nevada-Reno Win 13-2 1404.15 Mar 29th Sinvite 2025
154 Occidental Win 13-6 1552.23 Mar 29th Sinvite 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)