#258 DePaul (4-11)

avg: 840.77  •  sd: 53.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
298 Knox Win 12-9 1041.92 Mar 1st Midwest Throwdown 2025
104 Marquette Loss 4-13 837.96 Mar 1st Midwest Throwdown 2025
136 Wisconsin-Eau Claire Loss 5-10 732.4 Mar 1st Midwest Throwdown 2025
95 Southern Illinois-Edwardsville** Loss 4-13 870.96 Ignored Mar 2nd Midwest Throwdown 2025
223 Wisconsin-B Win 9-7 1255.4 Mar 2nd Midwest Throwdown 2025
136 Wisconsin-Eau Claire Loss 1-13 706.29 Mar 2nd Midwest Throwdown 2025
117 Colorado Mines Loss 4-13 763.26 Mar 29th Old Capitol Open 2025
399 Iowa-B** Win 14-4 678.49 Ignored Mar 29th Old Capitol Open 2025
303 Luther Win 13-12 790.96 Mar 29th Old Capitol Open 2025
177 Minnesota-Duluth Loss 10-11 1030.35 Mar 30th Old Capitol Open 2025
49 Chicago Loss 7-13 1171.79 Apr 12th Illinois D I Mens Conferences 2025
122 Northwestern Loss 6-9 926.22 Apr 12th Illinois D I Mens Conferences 2025
297 Loyola-Chicago Loss 9-11 451.97 Apr 12th Illinois D I Mens Conferences 2025
236 Illinois State Loss 8-9 783.05 Apr 13th Illinois D I Mens Conferences 2025
297 Loyola-Chicago Loss 9-10 576.18 Apr 13th Illinois D I 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)