#385 Carthage (1-11)

avg: 203.76  •  sd: 116.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
312 Illinois-Chicago Loss 7-10 241.27 Mar 22nd Meltdown 2025
72 St Olaf** Loss 0-13 989.88 Ignored Mar 22nd Meltdown 2025
132 Kenyon** Loss 3-13 715.76 Ignored Mar 22nd Meltdown 2025
273 Minnesota State-Mankato Loss 10-11 663.71 Mar 22nd Meltdown 2025
340 Rose-Hulman Loss 2-9 -70.56 Mar 23rd Meltdown 2025
297 Loyola-Chicago Loss 1-10 101.18 Mar 23rd Meltdown 2025
419 Iowa State-B** Win 13-5 87.4 Ignored Mar 29th Old Capitol Open 2025
271 Wisconsin-Platteville Loss 3-13 195.62 Mar 29th Old Capitol Open 2025
320 Minnesota-C Loss 4-13 -3.7 Mar 29th Old Capitol Open 2025
399 Iowa-B Loss 4-9 -521.51 Mar 30th Old Capitol Open 2025
271 Wisconsin-Platteville Loss 7-10 405.96 Apr 12th Lake Superior D III Mens Conferences 2025
169 Michigan Tech Loss 6-13 588.6 Apr 12th Lake Superior D III Mens Conferences 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)