#149 Davidson (10-8)

avg: 1054.75  •  sd: 67.63  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
255 Wake Forest Win 13-4 1186.48 Feb 15th 2025 Commonwealth Cup Weekend 1
241 Michigan-B Win 13-3 1242.3 Feb 15th 2025 Commonwealth Cup Weekend 1
131 Pittsburgh-B Loss 7-8 996.3 Feb 15th 2025 Commonwealth Cup Weekend 1
45 Elon Loss 9-13 1186.48 Feb 16th 2025 Commonwealth Cup Weekend 1
131 Pittsburgh-B Win 11-8 1486.91 Feb 16th 2025 Commonwealth Cup Weekend 1
78 Richmond Loss 7-13 790.5 Feb 28th D III River City Showdown 2025
125 Puget Sound Loss 7-10 750.94 Mar 1st D III River City Showdown 2025
163 Messiah Win 11-10 1106.6 Mar 1st D III River City Showdown 2025
99 Oberlin Loss 10-13 937.96 Mar 1st D III River City Showdown 2025
143 Michigan Tech Win 11-10 1187.91 Mar 2nd D III River City Showdown 2025
240 Xavier Win 10-9 769.29 Mar 2nd D III River City Showdown 2025
145 Kenyon Win 10-9 1184.11 Mar 2nd D III River City Showdown 2025
89 North Carolina-Asheville Loss 6-13 702.12 Mar 29th Easterns 2025
34 Lewis & Clark** Loss 3-13 1059.3 Ignored Mar 29th Easterns 2025
163 Messiah Loss 8-12 540.44 Mar 29th Easterns 2025
396 Mary Washington** Win 13-2 600 Ignored Mar 29th Easterns 2025
206 Christopher Newport Win 15-8 1343.07 Mar 30th Easterns 2025
171 Dickinson Win 15-6 1553.2 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)