#101 North Carolina-Asheville (12-6)

avg: 1448.36  •  sd: 67.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
159 Brandeis Win 13-11 1446.04 Mar 1st D III River City Showdown 2025
66 Franciscan Loss 3-13 1023.97 Mar 1st D III River City Showdown 2025
132 Kenyon Win 10-5 1889.66 Mar 1st D III River City Showdown 2025
169 Michigan Tech Win 13-10 1516.75 Mar 1st D III River City Showdown 2025
60 Carleton College-CHOP Win 11-9 1895.58 Mar 2nd D III River City Showdown 2025
66 Franciscan Loss 10-13 1295.83 Mar 2nd D III River City Showdown 2025
81 Rochester Win 12-11 1663.5 Mar 2nd D III River City Showdown 2025
123 Bates Loss 12-13 1219.49 Mar 29th Easterns 2025
143 Davidson Win 13-6 1878.6 Mar 29th Easterns 2025
36 Lewis & Clark Loss 1-13 1197.06 Mar 29th Easterns 2025
170 Messiah Win 13-10 1511.42 Mar 29th Easterns 2025
220 Christopher Newport Win 15-5 1581.36 Mar 30th Easterns 2025
220 Christopher Newport Win 11-1 1581.36 Apr 12th Atlantic Coast D III Mens Conferences 2025
366 High Point** Win 11-2 948.77 Ignored Apr 12th Atlantic Coast D III Mens Conferences 2025
292 Mary Washington** Win 11-2 1322.23 Ignored Apr 12th Atlantic Coast D III Mens Conferences 2025
77 Richmond Loss 7-11 1097.31 Apr 12th Atlantic Coast D III Mens Conferences 2025
143 Davidson Loss 13-14 1153.6 Apr 13th Atlantic Coast D III Mens Conferences 2025
285 Navy** Win 15-2 1350.44 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)