#142 Davidson (14-10)

avg: 1291.44  •  sd: 55.57  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
239 Wake Forest Win 13-4 1519.66 Feb 15th 2025 Commonwealth Cup Weekend 1
227 Michigan-B Win 13-3 1561.98 Feb 15th 2025 Commonwealth Cup Weekend 1
141 Pittsburgh-B Loss 7-8 1173.32 Feb 15th 2025 Commonwealth Cup Weekend 1
33 Elon Loss 9-13 1424.09 Feb 16th 2025 Commonwealth Cup Weekend 1
141 Pittsburgh-B Win 11-8 1663.93 Feb 16th 2025 Commonwealth Cup Weekend 1
78 Richmond Loss 7-13 1024.93 Feb 28th D III River City Showdown 2025
139 Puget Sound Loss 7-10 915.65 Mar 1st D III River City Showdown 2025
170 Messiah Win 11-10 1320.06 Mar 1st D III River City Showdown 2025
145 Oberlin Loss 10-13 956.65 Mar 1st D III River City Showdown 2025
169 Michigan Tech Win 11-10 1329 Mar 2nd D III River City Showdown 2025
241 Xavier Win 10-9 1038.28 Mar 2nd D III River City Showdown 2025
131 Kenyon Win 10-9 1454.6 Mar 2nd D III River City Showdown 2025
102 North Carolina-Asheville Loss 6-13 859.86 Mar 29th Easterns 2025
40 Lewis & Clark Loss 3-13 1207.59 Mar 29th Easterns 2025
170 Messiah Loss 8-12 753.91 Mar 29th Easterns 2025
290 Mary Washington Win 13-2 1334.64 Mar 29th Easterns 2025
221 Christopher Newport Win 15-8 1560.12 Mar 30th Easterns 2025
179 Dickinson Win 15-6 1766.25 Mar 30th Easterns 2025
282 Navy Win 15-6 1363.93 Apr 12th Atlantic Coast D III Mens Conferences 2025
277 Salisbury Win 15-8 1344.39 Apr 12th Atlantic Coast D III Mens Conferences 2025
33 Elon Loss 6-13 1242.66 Apr 12th Atlantic Coast D III Mens Conferences 2025
221 Christopher Newport Win 15-7 1595.31 Apr 13th Atlantic Coast D III Mens Conferences 2025
102 North Carolina-Asheville Win 14-13 1584.86 Apr 13th Atlantic Coast D III Mens Conferences 2025
78 Richmond Loss 7-15 982.46 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)