#285 Navy (1-14)

avg: 750.44  •  sd: 63.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
220 Christopher Newport Loss 6-13 381.36 Jan 25th Mid Atlantic Warm Up 2025
91 Vermont-B** Loss 2-13 878.29 Ignored Jan 25th Mid Atlantic Warm Up 2025
166 Pennsylvania Loss 4-13 594.08 Jan 25th Mid Atlantic Warm Up 2025
52 William & Mary** Loss 0-13 1093.54 Ignored Jan 25th Mid Atlantic Warm Up 2025
173 East Carolina Loss 4-15 566.37 Jan 26th Mid Atlantic Warm Up 2025
230 Air Force Loss 10-11 802.9 Mar 1st D III River City Showdown 2025
60 Carleton College-CHOP** Loss 2-13 1046.37 Ignored Mar 1st D III River City Showdown 2025
144 Oberlin Loss 5-13 670.28 Mar 1st D III River City Showdown 2025
159 Brandeis Loss 3-11 617.2 Mar 2nd D III River City Showdown 2025
132 Kenyon Loss 6-13 715.76 Mar 2nd D III River City Showdown 2025
143 Davidson Loss 6-15 678.6 Apr 12th Atlantic Coast D III Mens Conferences 2025
33 Elon** Loss 4-15 1225.66 Ignored Apr 12th Atlantic Coast D III Mens Conferences 2025
277 Salisbury Win 15-10 1223.12 Apr 12th Atlantic Coast D III Mens Conferences 2025
220 Christopher Newport Loss 12-13 856.36 Apr 13th Atlantic Coast D III Mens Conferences 2025
101 North Carolina-Asheville** Loss 2-15 848.36 Ignored Apr 13th Atlantic Coast 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)