#57 Claremont (4-3)

avg: 1141.86  •  sd: 58.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 Lewis & Clark Win 11-7 1214.3 Feb 8th Stanford Open 2020
126 Cal Poly-SLO-B Win 13-5 1158.87 Feb 8th Stanford Open 2020
86 Puget Sound Win 7-6 1013.6 Feb 8th Stanford Open 2020
22 California-Santa Cruz Loss 8-10 1192.02 Feb 8th Stanford Open 2020
71 Carleton College-GoP Win 5-4 1171.97 Feb 9th Stanford Open 2020
21 Western Washington Loss 8-9 1343.96 Feb 9th Stanford Open 2020
36 Arizona Loss 4-7 823.65 Feb 9th Stanford Open 2020
**Blowout Eligible


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
  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
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)