#118 Lewis & Clark (10-7)

avg: 1396.45  •  sd: 68.31  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
5 Oregon** Loss 0-13 2010.52 Ignored Jan 27th Flat Tail Womens Tournament 2018
67 Puget Sound Loss 7-8 1643.81 Jan 27th Flat Tail Womens Tournament 2018
147 Humboldt State Win 8-7 1317.56 Jan 27th Flat Tail Womens Tournament 2018
68 Boise State Loss 3-13 1162.64 Jan 27th Flat Tail Womens Tournament 2018
113 Portland State Win 15-11 1811.42 Jan 28th Flat Tail Womens Tournament 2018
147 Humboldt State Win 14-9 1666.43 Jan 28th Flat Tail Womens Tournament 2018
87 California-Santa Cruz Loss 5-7 1259.61 Feb 10th Stanford Open 2018
105 Chico State Loss 7-9 1200 Feb 10th Stanford Open 2018
73 San Diego State Loss 8-11 1358.12 Feb 10th Stanford Open 2018
249 California-B** Win 13-5 1024.8 Ignored Feb 11th Stanford Open 2018
189 Sonoma State Win 13-6 1539.66 Feb 11th Stanford Open 2018
126 Portland Win 10-7 1703.36 Feb 24th PLU BBQ 2018
113 Portland State Win 11-10 1555.26 Feb 24th PLU BBQ 2018
230 Washington-B** Win 13-1 1244.24 Ignored Feb 24th PLU BBQ 2018
196 Idaho Win 7-5 1254.21 Feb 24th PLU BBQ 2018
183 Western Washington-B Win 15-14 1084.22 Feb 25th PLU BBQ 2018
113 Portland State Loss 7-10 1040.59 Feb 25th PLU BBQ 2018
**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)