#49 Carleton College-Eclipse (8-2)

avg: 1331.2  •  sd: 98.54  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
88 Claremont Win 13-3 1475.69 Feb 3rd Stanford Open 2024
74 California-San Diego-B Win 7-6 1159.78 Feb 3rd Stanford Open 2024
126 Cal Poly-SLO-B** Win 13-4 895.56 Ignored Feb 3rd Stanford Open 2024
83 Lewis & Clark Win 11-7 1393.36 Feb 10th DIII Grand Prix
45 Portland Loss 5-8 923.69 Feb 10th DIII Grand Prix
59 Colorado College Loss 8-10 956.33 Feb 10th DIII Grand Prix
92 Puget Sound Win 12-4 1447.19 Feb 10th DIII Grand Prix
40 Whitman Win 10-7 1878.5 Feb 11th DIII Grand Prix
112 Oregon State** Win 10-2 1198.27 Ignored Feb 11th DIII Grand Prix
59 Colorado College Win 6-5 1344 Feb 11th DIII Grand Prix
**Blowout Eligible

FAQ

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)