#91 Lewis & Clark (6-7)

avg: 1284.69  •  sd: 55.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
48 Carleton College-Eclipse Loss 7-11 1176.8 Feb 10th DIII Grand Prix
106 Puget Sound Loss 7-8 1026.16 Feb 10th DIII Grand Prix
139 Oregon State Win 8-6 1194.53 Feb 10th DIII Grand Prix
44 Whitman Loss 3-11 1092.34 Feb 10th DIII Grand Prix
60 Colorado College Loss 6-8 1217.93 Feb 11th DIII Grand Prix
45 Portland Loss 4-9 1084.91 Feb 11th DIII Grand Prix
106 Puget Sound Win 9-3 1751.16 Feb 11th DIII Grand Prix
141 Cal State-Long Beach Win 12-0 1470.36 Mar 9th Irvine Open
159 California-Davis-B** Win 13-0 1277.88 Ignored Mar 9th Irvine Open
69 California-San Diego-B Loss 4-5 1297.37 Mar 9th Irvine Open
138 California-B Win 10-2 1496.13 Mar 10th Irvine Open
78 California-Irvine Loss 5-6 1239.93 Mar 10th Irvine Open
182 UCLA-B** Win 13-2 1036.13 Ignored Mar 10th Irvine Open
**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)