#45 Elon (20-5)

avg: 1605.05  •  sd: 46.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
88 Georgetown Win 13-10 1636.81 Feb 1st Carolina Kickoff mens 2025
32 Virginia Loss 12-13 1554.87 Feb 1st Carolina Kickoff mens 2025
21 Georgia Tech Loss 9-13 1436.23 Feb 1st Carolina Kickoff mens 2025
71 Case Western Reserve Loss 10-13 1076.92 Feb 2nd Carolina Kickoff mens 2025
84 Ohio State Win 13-8 1815.83 Feb 2nd Carolina Kickoff mens 2025
88 Georgetown Win 14-11 1622.01 Feb 2nd Carolina Kickoff mens 2025
124 Denver Win 11-1 1746.36 Feb 15th 2025 Commonwealth Cup Weekend 1
256 Illinois-B** Win 11-2 1180.53 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
164 Ohio** Win 10-4 1576.87 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
187 North Carolina-B** Win 11-0 1464.39 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
149 Davidson Win 13-9 1473.32 Feb 16th 2025 Commonwealth Cup Weekend 1
124 Denver Win 9-4 1746.36 Feb 16th 2025 Commonwealth Cup Weekend 1
166 Brandeis** Win 13-4 1572.11 Ignored Mar 1st D III River City Showdown 2025
240 Xavier** Win 13-4 1244.29 Ignored Mar 1st D III River City Showdown 2025
80 Rochester Win 13-5 1940.49 Mar 1st D III River City Showdown 2025
125 Puget Sound Win 13-3 1740.61 Mar 2nd D III River City Showdown 2025
70 Franciscan Loss 9-10 1285.69 Mar 2nd D III River City Showdown 2025
80 Rochester Win 12-8 1781.64 Mar 2nd D III River City Showdown 2025
144 Bates Win 13-5 1659.56 Mar 29th Easterns 2025
171 Dickinson** Win 13-1 1553.2 Ignored Mar 29th Easterns 2025
68 Wesleyan Loss 11-13 1202.47 Mar 29th Easterns 2025
163 Messiah Win 13-6 1581.6 Mar 29th Easterns 2025
46 Middlebury Win 14-10 1996.5 Mar 30th Easterns 2025
73 Williams Win 13-12 1510.1 Mar 30th Easterns 2025
68 Wesleyan Win 15-13 1645.49 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)