#239 Reed (8-12)

avg: 989.35  •  sd: 70.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
235 Claremont Loss 9-13 576.64 Feb 10th DIII Grand Prix
291 Pacific Lutheran Loss 8-9 623.55 Feb 10th DIII Grand Prix
178 Portland Loss 8-12 767.61 Feb 10th DIII Grand Prix
276 Whitworth Loss 6-13 246.13 Feb 10th DIII Grand Prix
173 Xavier Loss 8-12 792.28 Feb 11th DIII Grand Prix
81 Lewis & Clark Loss 2-13 987.48 Feb 11th DIII Grand Prix
52 Whitman** Loss 4-13 1156.72 Ignored Feb 11th DIII Grand Prix
178 Portland Loss 8-13 712.61 Mar 2nd PLU Mens BBQ
169 Puget Sound Loss 6-13 652.14 Mar 2nd PLU Mens BBQ
276 Whitworth Win 10-9 971.13 Mar 2nd PLU Mens BBQ
291 Pacific Lutheran Win 12-5 1348.55 Mar 3rd PLU Mens BBQ
155 Washington-B Loss 9-13 881.43 Mar 3rd PLU Mens BBQ
365 Seattle Win 10-5 1005.45 Mar 3rd PLU Mens BBQ
169 Puget Sound Win 11-9 1501.35 Apr 13th Northwest D III Mens Conferences 2024
81 Lewis & Clark Loss 6-11 1040.78 Apr 13th Northwest D III Mens Conferences 2024
335 Willamette Win 11-5 1170.54 Apr 13th Northwest D III Mens Conferences 2024
365 Seattle Win 11-7 898.44 Apr 13th Northwest D III Mens Conferences 2024
291 Pacific Lutheran Win 15-10 1202.15 Apr 14th Northwest D III Mens Conferences 2024
169 Puget Sound Win 15-12 1552.63 Apr 14th Northwest D III Mens Conferences 2024
81 Lewis & Clark Loss 5-15 987.48 Apr 14th Northwest D III Mens Conferences 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)