#76 Pacific Lutheran (9-8)

avg: 1692.71  •  sd: 96.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
65 Utah Loss 8-9 1656.92 Feb 10th Stanford Open 2018
18 Brigham Young Loss 9-11 2037.3 Feb 10th Stanford Open 2018
67 Puget Sound Loss 6-7 1643.81 Feb 10th Stanford Open 2018
103 Claremont Loss 4-8 926.4 Feb 10th Stanford Open 2018
124 Carleton College-Eclipse Win 13-3 1952.52 Feb 11th Stanford Open 2018
103 Claremont Win 11-8 1856.82 Feb 11th Stanford Open 2018
183 Western Washington-B** Win 13-2 1559.22 Ignored Feb 24th PLU BBQ 2018
101 Oregon State Loss 7-9 1226.63 Feb 24th PLU BBQ 2018
256 Seattle** Win 13-0 913.39 Ignored Feb 24th PLU BBQ 2018
101 Oregon State Loss 9-12 1160.61 Feb 25th PLU BBQ 2018
126 Portland Win 10-6 1809.86 Feb 25th PLU BBQ 2018
136 Cal State-Long Beach Win 15-5 1871.41 Mar 24th NW Challenge 2018
196 Idaho** Win 15-3 1526.06 Ignored Mar 24th NW Challenge 2018
67 Puget Sound Loss 8-10 1506.14 Mar 24th NW Challenge 2018
103 Claremont Win 15-6 2091.21 Mar 25th NW Challenge 2018
72 Simon Fraser University Win 15-8 2293.81 Mar 25th NW Challenge 2018
67 Puget Sound Loss 7-11 1301.92 Mar 25th NW Challenge 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)