#126 Lehigh (8-17)

avg: 1145.39  •  sd: 41.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
78 Carleton College-CHOP Loss 11-15 967.78 Jan 27th Carolina Kickoff 2024
38 Duke Loss 10-15 1137.15 Jan 27th Carolina Kickoff 2024
36 North Carolina-Charlotte Loss 7-15 1018.26 Jan 27th Carolina Kickoff 2024
91 Indiana Loss 10-11 1145.81 Jan 28th Carolina Kickoff 2024
113 Syracuse Loss 9-13 770.27 Feb 5th New Jersey Warmup
167 Columbia Win 11-8 1323.86 Feb 10th New Jersey Warmup
196 NYU Win 10-6 1337.35 Feb 10th New Jersey Warmup
60 Temple Loss 7-13 877.49 Feb 10th New Jersey Warmup
107 Princeton Loss 12-13 1083.6 Feb 11th New Jersey Warmup
169 Rutgers Win 14-11 1264.98 Feb 11th New Jersey Warmup
60 Temple Loss 12-15 1134.53 Feb 11th New Jersey Warmup
154 Harvard Win 10-9 1148.19 Feb 24th Easterns Qualifier 2024
36 North Carolina-Charlotte Loss 8-13 1122.1 Feb 24th Easterns Qualifier 2024
106 Notre Dame Loss 8-13 714.16 Feb 24th Easterns Qualifier 2024
66 Virginia Loss 8-11 1028.56 Feb 24th Easterns Qualifier 2024
27 Georgia Tech Loss 8-13 1243.98 Feb 25th Easterns Qualifier 2024
68 James Madison Loss 9-10 1251.89 Feb 25th Easterns Qualifier 2024
169 Rutgers Win 15-7 1551.64 Feb 25th Easterns Qualifier 2024
58 Maryland Loss 10-15 989.36 Feb 25th Easterns Qualifier 2024
231 Christopher Newport Win 15-9 1228.09 Mar 30th Atlantic Coast Open 2024
56 Emory Loss 10-14 1048.87 Mar 30th Atlantic Coast Open 2024
206 George Washington Win 15-5 1403.3 Mar 30th Atlantic Coast Open 2024
52 Virginia Tech Win 15-14 1600.52 Mar 30th Atlantic Coast Open 2024
84 Appalachian State Loss 12-15 1026.31 Mar 31st Atlantic Coast Open 2024
73 Richmond Loss 11-15 983.09 Mar 31st Atlantic Coast Open 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)