#230 Air Force (4-14)

avg: 927.9  •  sd: 50.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
43 Whitman Loss 6-13 1164.88 Feb 8th DIII Grand Prix 2025
319 Pacific Lutheran Win 13-8 1097.3 Feb 8th DIII Grand Prix 2025
295 Reed Win 13-7 1270.84 Feb 8th DIII Grand Prix 2025
199 Occidental Loss 9-13 639.57 Feb 9th DIII Grand Prix 2025
36 Lewis & Clark Loss 8-13 1300.9 Feb 9th DIII Grand Prix 2025
107 Claremont Loss 6-13 828.35 Feb 9th DIII Grand Prix 2025
60 Carleton College-CHOP** Loss 2-13 1046.37 Ignored Mar 1st D III River City Showdown 2025
144 Oberlin Loss 9-13 851.71 Mar 1st D III River City Showdown 2025
285 Navy Win 11-10 875.44 Mar 1st D III River City Showdown 2025
141 Puget Sound Loss 4-13 688.06 Mar 1st D III River City Showdown 2025
241 Xavier Loss 9-11 650.35 Mar 2nd D III River City Showdown 2025
169 Michigan Tech Loss 9-13 770.04 Mar 2nd D III River City Showdown 2025
117 Colorado Mines Loss 9-15 847.78 Apr 12th Rocky Mountain D III Mens Conferences 2025
89 Colorado College Loss 5-15 885.28 Apr 12th Rocky Mountain D III Mens Conferences 2025
117 Colorado Mines Loss 5-13 763.26 Apr 26th South Central D III College Mens Regionals 2025
89 Colorado College Loss 6-14 885.28 Apr 26th South Central D III College Mens Regionals 2025
195 John Brown Loss 12-13 944.63 Apr 27th South Central D III College Mens Regionals 2025
288 Harding Win 13-6 1345.34 Apr 27th South Central D III College Mens Regionals 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)