#58 Whitman (13-9)

avg: 1579.65  •  sd: 68.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
402 Oregon State-B** Win 13-1 803 Ignored Jan 26th Flat Tail Open 2019 Mens
116 Nevada-Reno Win 13-7 1851.25 Jan 26th Flat Tail Open 2019 Mens
99 Lewis & Clark Win 15-5 1958.77 Jan 26th Flat Tail Open 2019 Mens
241 Washington-B Win 13-7 1446.01 Jan 26th Flat Tail Open 2019 Mens
3 Oregon Loss 8-15 1624.18 Jan 27th Flat Tail Open 2019 Mens
192 Gonzaga Win 15-7 1622.54 Jan 27th Flat Tail Open 2019 Mens
357 San Jose State** Win 13-0 1048.54 Ignored Feb 9th Stanford Open 2019
90 Santa Clara Win 10-8 1649.52 Feb 9th Stanford Open 2019
184 California-B Win 13-2 1632.51 Feb 9th Stanford Open 2019
367 Texas-B** Win 13-5 991.24 Ignored Feb 9th Stanford Open 2019
116 Nevada-Reno Win 8-3 1893.72 Feb 10th Stanford Open 2019
41 Las Positas Win 7-6 1803.54 Feb 10th Stanford Open 2019
21 California Loss 5-6 1718.46 Feb 10th Stanford Open 2019
180 Humboldt State Win 9-5 1587.49 Feb 10th Stanford Open 2019
6 Brigham Young Loss 9-13 1716.17 Mar 23rd 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 7-13 1586.93 Mar 23rd 2019 NW Challenge Mens Tier 1
30 Victoria Loss 11-13 1537.06 Mar 23rd 2019 NW Challenge Mens Tier 1
42 British Columbia Loss 9-13 1255.04 Mar 24th 2019 NW Challenge Mens Tier 1
51 Western Washington Loss 6-13 1029.76 Mar 24th 2019 NW Challenge Mens Tier 1
50 Stanford Loss 10-13 1304.6 Mar 25th 2019 NW Challenge Mens Tier 1
59 Oregon State Win 13-10 1890.33 Mar 25th 2019 NW Challenge Mens Tier 1
10 Washington Loss 2-9 1444.51 Mar 25th 2019 NW Challenge Mens Tier 1
**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)