#323 Kansas State (2-9)

avg: 269.54  •  sd: 98.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
309 Washington University-B Loss 7-12 -202.53 Feb 22nd Dust Bowl 2025
74 Oklahoma Christian** Loss 0-13 778.09 Ignored Feb 22nd Dust Bowl 2025
201 Northern Iowa Loss 8-13 314.96 Feb 22nd Dust Bowl 2025
363 Dallas Win 11-7 445.14 Feb 23rd Dust Bowl 2025
268 Harding Win 10-7 925.63 Feb 23rd Dust Bowl 2025
261 Colorado State-B Loss 8-14 28.57 Mar 29th Free State Classic 2025
225 John Brown Loss 4-15 99.24 Mar 29th Free State Classic 2025
169 Kansas** Loss 3-15 360.46 Ignored Mar 29th Free State Classic 2025
168 Truman State Loss 11-15 583.45 Mar 29th Free State Classic 2025
225 John Brown Loss 6-15 99.24 Mar 30th Free State Classic 2025
188 Oklahoma State Loss 7-15 263.82 Mar 30th Free State Classic 2025
**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)