#135 Claremont (4-11)

avg: 978.82  •  sd: 118.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
72 Santa Clara Win 6-5 1521.79 Feb 3rd Stanford Open 2024
85 California-San Diego-B Loss 5-6 1214.27 Feb 3rd Stanford Open 2024
37 Carleton College-Eclipse** Loss 3-13 1149.41 Ignored Feb 3rd Stanford Open 2024
186 UCLA-B Win 8-2 1185.49 Feb 3rd Stanford Open 2024
5 Stanford** Loss 2-15 1919.88 Ignored Feb 17th Presidents Day Invite 2024
29 UCLA** Loss 3-13 1253.18 Ignored Feb 17th Presidents Day Invite 2024
25 Utah** Loss 4-15 1286.02 Ignored Feb 17th Presidents Day Invite 2024
30 Cal Poly-SLO Loss 6-12 1262.6 Feb 18th Presidents Day Invite 2024
28 California** Loss 3-15 1256.51 Ignored Feb 18th Presidents Day Invite 2024
115 Denver Win 9-6 1538.18 Feb 18th Presidents Day Invite 2024
165 Cal State-Long Beach Loss 3-10 199.57 Mar 30th Claremont Classic 2024
131 Occidental Loss 0-8 414.86 Mar 30th Claremont Classic 2024
131 Occidental Loss 8-9 889.86 Apr 13th Southwest D III Womens Conferences 2024
131 Occidental Win 9-6 1433.43 Apr 13th Southwest D III Womens Conferences 2024
131 Occidental Loss 5-7 686.72 Apr 13th Southwest D III Womens 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)