#163 Messiah (7-12)

avg: 981.6  •  sd: 72.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
149 Davidson Loss 10-11 929.75 Mar 1st D III River City Showdown 2025
125 Puget Sound Loss 9-13 722.04 Mar 1st D III River City Showdown 2025
78 Richmond Loss 7-13 790.5 Mar 1st D III River City Showdown 2025
166 Brandeis Win 11-10 1097.11 Mar 2nd D III River City Showdown 2025
145 Kenyon Loss 11-12 934.11 Mar 2nd D III River City Showdown 2025
240 Xavier Win 13-6 1244.29 Mar 2nd D III River City Showdown 2025
388 American-B** Win 15-6 179.27 Ignored Mar 22nd Atlantic Coast Open 2025
96 Appalachian State Loss 8-14 738.39 Mar 22nd Atlantic Coast Open 2025
139 Florida State Loss 11-15 702.52 Mar 22nd Atlantic Coast Open 2025
115 Vermont-B Loss 7-9 915.38 Mar 22nd Atlantic Coast Open 2025
180 American Loss 13-14 780.74 Mar 23rd Atlantic Coast Open 2025
139 Florida State Loss 8-13 587.53 Mar 23rd Atlantic Coast Open 2025
170 Massachusetts -B Win 15-13 1174.04 Mar 23rd Atlantic Coast Open 2025
149 Davidson Win 12-8 1495.91 Mar 29th Easterns 2025
45 Elon Loss 6-13 1005.05 Mar 29th Easterns 2025
34 Lewis & Clark** Loss 5-13 1059.3 Ignored Mar 29th Easterns 2025
89 North Carolina-Asheville Loss 10-13 973.98 Mar 29th Easterns 2025
171 Dickinson Win 15-8 1518.01 Mar 30th Easterns 2025
396 Mary Washington** Win 15-6 600 Ignored 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)