#180 Humboldt State (4-7)

avg: 1058.43  •  sd: 95.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Oregon State Loss 8-13 1066.03 Jan 26th Flat Tail Open 2019 Mens
312 Portland State Win 13-9 1032.53 Jan 26th Flat Tail Open 2019 Mens
99 Lewis & Clark Loss 8-13 862.61 Jan 26th Flat Tail Open 2019 Mens
383 Washington-C** Win 13-3 903.69 Ignored Jan 26th Flat Tail Open 2019 Mens
446 Lewis & Clark-B** Win 15-2 600 Ignored Jan 27th Flat Tail Open 2019 Mens
241 Washington-B Loss 13-15 674.3 Jan 27th Flat Tail Open 2019 Mens
133 Utah State Win 13-5 1845.27 Feb 9th Stanford Open 2019
168 Whitworth Loss 8-9 962.23 Feb 9th Stanford Open 2019
34 UCLA** Loss 4-13 1128.73 Ignored Feb 9th Stanford Open 2019
78 Carleton College-GoP Loss 3-8 857.72 Feb 10th Stanford Open 2019
58 Whitman Loss 5-9 1050.59 Feb 10th Stanford Open 2019
**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)