#123 Wisconsin-Milwaukee (9-8)

avg: 1146.99  •  sd: 63.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 California** Loss 5-13 1369.23 Ignored Mar 8th Stanford Invite 2025 Mens
33 California-Santa Barbara Loss 5-13 1060.93 Mar 8th Stanford Invite 2025 Mens
22 Western Washington** Loss 4-13 1249.96 Ignored Mar 8th Stanford Invite 2025 Mens
118 British Columbia -B Win 13-10 1514.15 Mar 9th Stanford Invite 2025 Mens
141 Northwestern Loss 12-13 947.86 Mar 9th Stanford Invite 2025 Mens
249 Cedarville Win 13-10 940.94 Mar 15th Grand Rapids Invite 2025
60 Michigan State Loss 6-11 939.65 Mar 15th Grand Rapids Invite 2025
164 Ohio Win 13-9 1395.43 Mar 15th Grand Rapids Invite 2025
11 Davenport Loss 9-15 1465.47 Mar 16th Grand Rapids Invite 2025
143 Michigan Tech Win 14-11 1376.25 Mar 16th Grand Rapids Invite 2025
63 Notre Dame Loss 6-11 912.77 Mar 16th Grand Rapids Invite 2025
155 Grinnell Win 13-8 1516.05 Mar 29th Old Capitol Open 2025
265 St John's (Minnesota) Win 13-7 1110.52 Mar 29th Old Capitol Open 2025
281 Wisconsin-Platteville Win 12-10 688.99 Mar 29th Old Capitol Open 2025
117 Colorado Mines Win 7-5 1517.4 Mar 30th Old Capitol Open 2025
107 Iowa Loss 8-11 856.75 Mar 30th Old Capitol Open 2025
178 Minnesota-B Win 12-11 1045.73 Mar 30th Old Capitol Open 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)