#37 McGill (11-9)

avg: 1818.23  •  sd: 76.89  •  top 16/20: 0.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Alabama-Huntsville Win 12-11 1754.91 Jan 31st Florida Warm Up 2025
45 Virginia Tech Loss 9-10 1650.6 Jan 31st Florida Warm Up 2025
27 Minnesota Loss 4-13 1291.78 Jan 31st Florida Warm Up 2025
17 Brown Loss 12-13 1930.26 Feb 1st Florida Warm Up 2025
132 Florida State Win 13-1 1927.53 Feb 1st Florida Warm Up 2025
90 Texas A&M Win 13-10 1824.58 Feb 1st Florida Warm Up 2025
50 Tulane Loss 8-10 1466.45 Feb 2nd Florida Warm Up 2025
32 Utah State Loss 9-10 1727.16 Feb 2nd Florida Warm Up 2025
63 Duke Win 13-8 2144.95 Feb 22nd Easterns Qualifier 2025
196 Kennesaw State Win 12-8 1523.81 Feb 22nd Easterns Qualifier 2025
77 Ohio State Win 10-4 2188.07 Feb 22nd Easterns Qualifier 2025
28 Virginia Win 12-10 2120.2 Feb 22nd Easterns Qualifier 2025
56 Indiana Win 15-7 2278.9 Feb 23rd Easterns Qualifier 2025
48 North Carolina-Wilmington Loss 9-11 1502.74 Feb 23rd Easterns Qualifier 2025
35 South Carolina Loss 11-14 1520.87 Feb 23rd Easterns Qualifier 2025
1 Massachusetts Loss 6-10 1920.52 Apr 12th Greater New England D I Mens Conferences 2025
134 Maine Win 14-2 1924.57 Apr 12th Greater New England D I Mens Conferences 2025
17 Brown Loss 11-12 1930.26 Apr 13th Greater New England D I Mens Conferences 2025
152 Rhode Island Win 12-8 1709.58 Apr 13th Greater New England D I Mens Conferences 2025
385 New Hampshire** Win 15-3 802.59 Ignored Apr 13th Greater New England D I 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)