#67 Lewis & Clark (12-5)

avg: 1297.81  •  sd: 82.91  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Carleton College-Eclipse Loss 4-12 938.86 Feb 8th DIII Grand Prix 2025
73 Colorado College Win 8-6 1560.66 Feb 8th DIII Grand Prix 2025
52 Oregon State Loss 10-12 1234.11 Feb 8th DIII Grand Prix 2025
133 Claremont Win 11-9 1060.43 Feb 9th DIII Grand Prix 2025
48 Whitman Loss 6-13 905.02 Feb 9th DIII Grand Prix 2025
116 Puget Sound Win 10-6 1439.98 Feb 9th DIII Grand Prix 2025
79 Portland Win 11-8 1572.37 Feb 9th DIII Grand Prix 2025
225 Washington-B** Win 11-1 795.47 Ignored Mar 8th PACcon
171 Pacific Lutheran** Win 13-2 1203 Ignored Mar 8th PACcon
79 Portland Win 10-7 1596.42 Mar 8th PACcon
250 Lewis & Clark -B** Win 11-2 561.39 Ignored Mar 9th PACcon
52 Oregon State Loss 10-11 1347.23 Mar 9th PACcon
79 Portland Win 9-7 1486.09 Mar 9th PACcon
171 Pacific Lutheran Win 8-5 1056.6 Apr 12th Northwest D III Womens Conferences 2025
79 Portland Win 10-9 1331.76 Apr 12th Northwest D III Womens Conferences 2025
116 Puget Sound Loss 9-10 818.82 Apr 13th Northwest D III Womens Conferences 2025
79 Portland Win 13-6 1806.76 Apr 13th Northwest D III Womens Conferences 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)