#402 Oregon State-B (3-7)

avg: 203  •  sd: 117.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 Nevada-Reno** Loss 2-13 693.72 Ignored Jan 26th Flat Tail Open 2019 Mens
58 Whitman** Loss 1-13 979.65 Ignored Jan 26th Flat Tail Open 2019 Mens
241 Washington-B Loss 8-13 392.32 Jan 26th Flat Tail Open 2019 Mens
383 Washington-C Win 15-12 604.18 Jan 27th Flat Tail Open 2019 Mens
326 Western Washington University-B Loss 9-15 66.25 Jan 27th Flat Tail Open 2019 Mens
241 Washington-B Loss 7-15 288.48 Jan 27th Flat Tail Open 2019 Mens
162 Washington State** Loss 4-13 509.49 Ignored Mar 2nd 19th Annual PLU BBQ Open
383 Washington-C Loss 5-10 -270.21 Mar 2nd 19th Annual PLU BBQ Open
441 Pacific Lutheran-B Win 11-2 211.59 Mar 2nd 19th Annual PLU BBQ Open
441 Pacific Lutheran-B Win 15-2 211.59 Mar 3rd 19th Annual PLU BBQ Open
**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)