#209 Christopher Newport (14-11)

avg: 1081.48  •  sd: 56.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
173 Xavier Loss 8-13 737.27 Mar 2nd FCS D III Tune Up 2024
206 Embry-Riddle Win 13-12 1223.9 Mar 2nd FCS D III Tune Up 2024
52 Whitman Loss 6-13 1156.72 Mar 2nd FCS D III Tune Up 2024
68 Franciscan Loss 7-13 1102.95 Mar 2nd FCS D III Tune Up 2024
274 Air Force Win 13-8 1346.67 Mar 3rd FCS D III Tune Up 2024
88 Berry Loss 9-13 1133.11 Mar 3rd FCS D III Tune Up 2024
198 Messiah Loss 11-13 900.15 Mar 3rd FCS D III Tune Up 2024
336 Virginia-B Win 9-7 849.27 Mar 23rd Fishbowl
394 William & Mary-B** Win 13-3 753.15 Ignored Mar 23rd Fishbowl
393 George Washington-B Win 10-5 731.72 Mar 23rd Fishbowl
262 Virginia Tech-B Win 9-8 1024.28 Mar 23rd Fishbowl
342 James Madison-B Win 13-7 1112.4 Mar 24th Fishbowl
262 Virginia Tech-B Win 10-8 1161.94 Mar 24th Fishbowl
226 American Loss 7-15 433.12 Mar 30th Atlantic Coast Open 2024
217 George Washington Loss 6-15 458.5 Mar 30th Atlantic Coast Open 2024
97 Lehigh Loss 9-15 1010.86 Mar 30th Atlantic Coast Open 2024
244 Dickinson Win 15-14 1104.09 Mar 30th Atlantic Coast Open 2024
307 Mary Washington Win 13-6 1283.16 Mar 31st Atlantic Coast Open 2024
262 Virginia Tech-B Win 13-8 1395.44 Mar 31st Atlantic Coast Open 2024
176 Navy Win 11-10 1337.43 Apr 20th Atlantic Coast D III Mens Conferences 2024
84 Elon Loss 5-14 976.44 Apr 20th Atlantic Coast D III Mens Conferences 2024
179 North Carolina-Asheville Win 13-10 1527.67 Apr 20th Atlantic Coast D III Mens Conferences 2024
256 Salisbury Win 15-6 1519.79 Apr 21st Atlantic Coast D III Mens Conferences 2024
65 Richmond Loss 6-15 1074.99 Apr 21st Atlantic Coast D III Mens Conferences 2024
84 Elon Loss 8-13 1080.28 Apr 21st Atlantic Coast D III Mens Conferences 2024
**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)