#154 Harvard (3-17)

avg: 1023.19  •  sd: 72.61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
70 Case Western Reserve Win 14-10 1765.41 Feb 10th Queen City Tune Up 2024
16 Penn State** Loss 2-15 1321.23 Ignored Feb 10th Queen City Tune Up 2024
61 William & Mary Win 13-10 1760.15 Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Loss 6-15 1018.26 Feb 10th Queen City Tune Up 2024
34 Ohio State Loss 8-11 1276.26 Feb 11th Queen City Tune Up 2024
92 Tennessee Loss 7-8 1142.11 Feb 11th Queen City Tune Up 2024
27 Georgia Tech** Loss 5-13 1140.14 Ignored Feb 24th Easterns Qualifier 2024
36 North Carolina-Charlotte Loss 2-13 1018.26 Feb 24th Easterns Qualifier 2024
126 Lehigh Loss 9-10 1020.39 Feb 24th Easterns Qualifier 2024
66 Virginia Loss 5-13 794.17 Feb 24th Easterns Qualifier 2024
106 Notre Dame Loss 9-12 864.95 Feb 25th Easterns Qualifier 2024
68 James Madison Loss 7-12 856.38 Feb 25th Easterns Qualifier 2024
158 Kennesaw State Loss 11-12 885.72 Feb 25th Easterns Qualifier 2024
167 Columbia Win 13-5 1558.25 Mar 30th East Coast Invite 2024
101 Cornell Loss 10-11 1099.57 Mar 30th East Coast Invite 2024
123 Pennsylvania Loss 8-10 884.82 Mar 30th East Coast Invite 2024
146 Yale Loss 8-13 563.89 Mar 30th East Coast Invite 2024
98 Dartmouth Loss 7-10 855.97 Mar 31st East Coast Invite 2024
169 Rutgers Loss 9-11 702.43 Mar 31st East Coast Invite 2024
150 Navy Loss 9-11 785.08 Mar 31st East Coast Invite 2024
**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)