#176 Brandeis (3-8)

avg: 857.85  •  sd: 78.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
181 Berry Win 13-8 1320.09 Mar 4th FCS D III Tune Up 2023
144 Franciscan Loss 10-11 856 Mar 4th FCS D III Tune Up 2023
96 Whitman Loss 7-13 651.94 Mar 4th FCS D III Tune Up 2023
75 Richmond Loss 4-13 712 Mar 4th FCS D III Tune Up 2023
132 Messiah Loss 7-13 482.66 Mar 5th FCS D III Tune Up 2023
79 Lewis & Clark Loss 6-13 690.5 Mar 5th FCS D III Tune Up 2023
240 Xavier Win 11-6 1090.48 Mar 5th FCS D III Tune Up 2023
236 Haverford Win 12-8 1011.22 Mar 25th Layout Pigout 2023
118 Williams Loss 6-14 493.04 Mar 25th Layout Pigout 2023
116 Kenyon Loss 10-11 986.25 Mar 25th Layout Pigout 2023
91 Oberlin Loss 9-10 1120.71 Mar 26th Layout Pigout 2023
**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)