#43 Whitman (11-4)

avg: 1764.88  •  sd: 81.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
230 Air Force Win 13-6 1527.9 Feb 8th DIII Grand Prix 2025
117 Colorado Mines Win 12-7 1883.77 Feb 8th DIII Grand Prix 2025
199 Occidental Win 13-10 1386.28 Feb 8th DIII Grand Prix 2025
89 Colorado College Loss 9-11 1236.07 Feb 9th DIII Grand Prix 2025
36 Lewis & Clark Win 13-11 2025.9 Feb 9th DIII Grand Prix 2025
141 Puget Sound Win 13-9 1706.62 Feb 9th DIII Grand Prix 2025
108 British Columbia -B Win 13-10 1747.15 Mar 8th Stanford Invite 2025 Mens
6 Cal Poly-SLO Loss 11-13 2008.07 Mar 8th Stanford Invite 2025 Mens
41 California-San Diego Win 13-9 2190.83 Mar 8th Stanford Invite 2025 Mens
45 Virginia Tech Loss 9-13 1334.01 Mar 8th Stanford Invite 2025 Mens
32 California-Santa Barbara Loss 10-13 1508.03 Mar 9th Stanford Invite 2025 Mens
131 Santa Clara Win 13-6 1922.07 Mar 9th Stanford Invite 2025 Mens
141 Puget Sound Win 14-7 1870.95 Apr 19th Northwest D III Mens Conferences 2025
317 Willamette** Win 15-3 1206.53 Ignored Apr 19th Northwest D III Mens Conferences 2025
36 Lewis & Clark Win 12-10 2035.18 Apr 20th Northwest D III Mens Conferences 2025
**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)