#73 Richmond (12-8)

avg: 1364.26  •  sd: 42.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
224 American Win 11-5 1331.75 Jan 27th Mid Atlantic Warm Up
142 Boston University Loss 8-9 943.71 Jan 27th Mid Atlantic Warm Up
68 James Madison Loss 10-12 1138.77 Jan 27th Mid Atlantic Warm Up
116 Liberty Win 12-7 1703.47 Jan 27th Mid Atlantic Warm Up
70 Case Western Reserve Loss 12-14 1145.76 Jan 28th Mid Atlantic Warm Up
98 Dartmouth Win 12-11 1370.64 Jan 28th Mid Atlantic Warm Up
61 William & Mary Win 14-13 1557.01 Jan 28th Mid Atlantic Warm Up
119 Berry Win 13-11 1401.16 Mar 2nd FCS D III Tune Up 2024
51 Franciscan Loss 11-12 1371.28 Mar 2nd FCS D III Tune Up 2024
217 Kenyon** Win 13-3 1358.95 Ignored Mar 2nd FCS D III Tune Up 2024
122 Oberlin Win 12-11 1277.55 Mar 2nd FCS D III Tune Up 2024
59 Whitman Loss 11-13 1207.94 Mar 3rd FCS D III Tune Up 2024
80 Lewis & Clark Win 13-12 1464.69 Mar 3rd FCS D III Tune Up 2024
137 Union (Tennessee) Win 13-6 1690.5 Mar 3rd FCS D III Tune Up 2024
84 Appalachian State Loss 12-14 1105.85 Mar 30th Atlantic Coast Open 2024
38 Duke Loss 11-12 1465.76 Mar 30th Atlantic Coast Open 2024
97 Florida State Win 13-11 1476.61 Mar 30th Atlantic Coast Open 2024
87 Tennessee-Chattanooga Win 13-12 1434.98 Mar 30th Atlantic Coast Open 2024
85 Carnegie Mellon Loss 13-14 1193.33 Mar 31st Atlantic Coast Open 2024
126 Lehigh Win 15-11 1526.56 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)