#108 Wisconsin-Milwaukee (7-6)

avg: 1199.7  •  sd: 71.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
188 John Brown Win 11-6 1409.08 Feb 17th Dust Bowl 2024
145 Southern Illinois-Edwardsville Win 9-8 1185.42 Feb 17th Dust Bowl 2024
317 Washington University-B** Win 13-2 870.22 Ignored Feb 17th Dust Bowl 2024
192 Harding Win 15-7 1451.11 Feb 18th Dust Bowl 2024
47 Oklahoma Christian Loss 7-11 1053.28 Feb 18th Dust Bowl 2024
145 Southern Illinois-Edwardsville Win 12-8 1501.57 Feb 18th Dust Bowl 2024
67 Chicago Loss 8-12 945.87 Mar 30th Huck Finn 2024
128 Colorado College Win 10-6 1632.16 Mar 30th Huck Finn 2024
83 Northwestern Loss 7-12 814.97 Mar 30th Huck Finn 2024
117 Vanderbilt Win 9-8 1304.97 Mar 30th Huck Finn 2024
53 Colorado State Loss 6-7 1345.56 Mar 31st Huck Finn 2024
118 Michigan Tech Loss 9-10 1048.62 Mar 31st Huck Finn 2024
105 Mississippi State Loss 6-8 910.29 Mar 31st Huck Finn 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)