#70 Dartmouth (13-12)

avg: 1618.5  •  sd: 68.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
39 Cincinnati Loss 10-11 1686.62 Jan 25th Mid Atlantic Warm Up 2025
154 Johns Hopkins Win 13-3 1856.34 Jan 25th Mid Atlantic Warm Up 2025
140 George Mason Win 13-4 1904.92 Jan 25th Mid Atlantic Warm Up 2025
88 Carnegie Mellon Loss 13-14 1381.57 Jan 26th Mid Atlantic Warm Up 2025
115 RIT Win 15-4 1984.22 Jan 26th Mid Atlantic Warm Up 2025
78 Richmond Loss 10-11 1457.46 Jan 26th Mid Atlantic Warm Up 2025
111 Vermont-B Win 13-10 1746.46 Jan 26th Mid Atlantic Warm Up 2025
106 Appalachian State Win 13-6 2049.49 Feb 22nd Easterns Qualifier 2025
56 Indiana Loss 11-12 1553.9 Feb 22nd Easterns Qualifier 2025
62 North Carolina State Loss 9-12 1305.96 Feb 22nd Easterns Qualifier 2025
53 William & Mary Loss 8-13 1214.14 Feb 22nd Easterns Qualifier 2025
66 Auburn Win 15-10 2094.05 Feb 23rd Easterns Qualifier 2025
63 Duke Loss 14-15 1523.79 Feb 23rd Easterns Qualifier 2025
64 Georgetown Win 15-7 2245.71 Feb 23rd Easterns Qualifier 2025
19 Tufts Loss 5-13 1430.53 Mar 15th Mens Centex 2025
36 Middlebury Loss 7-13 1261.44 Mar 15th Mens Centex 2025
44 Wisconsin Loss 7-8 1652.22 Mar 15th Mens Centex 2025
58 Illinois Loss 6-8 1372.45 Mar 15th Mens Centex 2025
120 Arizona State Win 15-12 1666.41 Mar 16th Mens Centex 2025
36 Middlebury Win 15-14 1943.97 Mar 16th Mens Centex 2025
50 Tulane Win 9-8 1854.12 Mar 16th Mens Centex 2025
95 Bowdoin Loss 10-12 1242.05 Apr 12th North New England D III Mens Conferences 2025
223 Colby Win 11-7 1449.62 Apr 12th North New England D III Mens Conferences 2025
399 Middlebury-B** Win 15-0 686 Ignored Apr 12th North New England D III Mens Conferences 2025
133 Bates Win 9-8 1450.44 Apr 13th North New England 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)