#16 Penn State (19-9)

avg: 1921.23  •  sd: 39.85  •  top 16/20: 96.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
125 Davidson Win 15-7 1746.57 Jan 27th Carolina Kickoff 2024
72 Georgetown Win 15-9 1881.18 Jan 27th Carolina Kickoff 2024
13 North Carolina State Loss 12-14 1725.64 Jan 27th Carolina Kickoff 2024
38 Duke Win 12-9 1936.12 Jan 28th Carolina Kickoff 2024
1 North Carolina Loss 12-15 1988.07 Jan 28th Carolina Kickoff 2024
28 North Carolina-Wilmington Win 14-9 2208.37 Jan 28th Carolina Kickoff 2024
70 Case Western Reserve Win 15-9 1882.19 Feb 10th Queen City Tune Up 2024
154 Harvard** Win 15-2 1623.19 Ignored Feb 10th Queen City Tune Up 2024
36 North Carolina-Charlotte Win 15-8 2183.07 Feb 10th Queen City Tune Up 2024
61 William & Mary Win 14-11 1745.34 Feb 10th Queen City Tune Up 2024
1 North Carolina Loss 9-15 1773.08 Feb 11th Queen City Tune Up 2024
13 North Carolina State Win 15-13 2160.78 Feb 11th Queen City Tune Up 2024
28 North Carolina-Wilmington Win 12-8 2175.66 Feb 11th Queen City Tune Up 2024
50 Alabama Win 13-6 2101.57 Feb 24th Easterns Qualifier 2024
38 Duke Win 13-6 2190.76 Feb 24th Easterns Qualifier 2024
76 Purdue Win 13-5 1957.22 Feb 24th Easterns Qualifier 2024
52 Virginia Tech Win 13-5 2075.52 Feb 24th Easterns Qualifier 2024
57 Auburn Win 13-8 1943.35 Feb 25th Easterns Qualifier 2024
28 North Carolina-Wilmington Loss 11-12 1609.5 Feb 25th Easterns Qualifier 2024
34 Ohio State Win 12-11 1766.87 Feb 25th Easterns Qualifier 2024
58 Maryland Win 13-8 1939.12 Feb 25th Easterns Qualifier 2024
12 Alabama-Huntsville Loss 10-13 1665.53 Mar 30th Easterns 2024
9 Brown Loss 11-13 1796.23 Mar 30th Easterns 2024
5 Cal Poly-SLO Loss 7-13 1617.18 Mar 30th Easterns 2024
36 North Carolina-Charlotte Win 13-4 2218.26 Mar 30th Easterns 2024
13 North Carolina State Loss 13-15 1732.42 Mar 31st Easterns 2024
28 North Carolina-Wilmington Win 14-7 2317.39 Mar 31st Easterns 2024
21 Tufts Loss 11-12 1703.7 Mar 31st Easterns 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)