#5 Oregon (24-4)

avg: 2193.64  •  sd: 76.02  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
22 Western Washington Win 15-9 2365.44 Jan 25th Pac Con 2025
121 Simon Fraser** Win 15-4 1770.02 Ignored Jan 25th Pac Con 2025
10 Oregon State Win 15-14 2106.68 Jan 25th Pac Con 2025
175 Cal Poly-Humboldt** Win 15-1 1536.36 Ignored Jan 26th Pac Con 2025
341 Portland State** Win 15-1 747.16 Ignored Jan 26th Pac Con 2025
10 Oregon State Win 15-10 2435.28 Jan 26th Pac Con 2025
33 California-Santa Barbara Win 11-6 2207.62 Feb 15th Presidents Day Invite 2025
18 Northeastern Win 10-9 2020.7 Feb 15th Presidents Day Invite 2025
173 California-Davis** Win 13-5 1545.35 Ignored Feb 16th Presidents Day Invite 2025
9 California-Santa Cruz Win 13-9 2439.74 Feb 16th Presidents Day Invite 2025
18 Northeastern Win 13-10 2223.84 Feb 16th Presidents Day Invite 2025
26 Utah Win 13-11 2024.42 Feb 16th Presidents Day Invite 2025
2 Colorado Win 13-8 2729.04 Feb 17th Presidents Day Invite 2025
10 Oregon State Win 13-11 2210.52 Feb 17th Presidents Day Invite 2025
21 Georgia Tech Win 15-9 2370.27 Mar 1st Smoky Mountain Invite 2025
3 North Carolina Loss 10-13 1877.98 Mar 1st Smoky Mountain Invite 2025
18 Northeastern Win 12-9 2241.06 Mar 1st Smoky Mountain Invite 2025
28 Pittsburgh Win 13-10 2092.87 Mar 1st Smoky Mountain Invite 2025
4 Carleton College Loss 12-15 1902.81 Mar 2nd Smoky Mountain Invite 2025
20 Vermont Win 15-14 1983.08 Mar 2nd Smoky Mountain Invite 2025
10 Oregon State Win 15-12 2282.17 Mar 2nd Smoky Mountain Invite 2025
16 Brown Win 13-10 2247.87 Mar 29th Easterns 2025
2 Colorado Loss 10-13 1904.74 Mar 29th Easterns 2025
19 Georgia Win 10-9 2017.49 Mar 29th Easterns 2025
64 James Madison Win 13-7 2014.91 Mar 29th Easterns 2025
4 Carleton College Win 15-12 2503.8 Mar 30th Easterns 2025
3 North Carolina Win 15-9 2721.6 Mar 30th Easterns 2025
1 Massachusetts Loss 11-15 1877.94 Mar 30th Easterns 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)