#63 Notre Dame (10-10)

avg: 1459.46  •  sd: 75.18  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Auburn Loss 8-13 932.23 Feb 22nd Easterns Qualifier 2025
64 James Madison Loss 11-13 1228.54 Feb 22nd Easterns Qualifier 2025
128 SUNY-Binghamton Win 13-10 1456.21 Feb 22nd Easterns Qualifier 2025
27 South Carolina Loss 6-13 1179.6 Feb 22nd Easterns Qualifier 2025
49 North Carolina State Loss 14-15 1439.8 Feb 23rd Easterns Qualifier 2025
84 Ohio State Loss 8-15 754.86 Feb 23rd Easterns Qualifier 2025
87 Temple Loss 13-15 1096.74 Feb 23rd Easterns Qualifier 2025
11 Davenport Loss 6-13 1380.95 Mar 15th Grand Rapids Invite 2025
143 Michigan Tech Win 14-6 1662.91 Mar 15th Grand Rapids Invite 2025
190 Toronto Win 10-7 1249.29 Mar 15th Grand Rapids Invite 2025
60 Michigan State Loss 11-12 1361.35 Mar 16th Grand Rapids Invite 2025
221 Wisconsin-B Win 13-7 1268.24 Mar 16th Grand Rapids Invite 2025
123 Wisconsin-Milwaukee Win 11-6 1693.69 Mar 16th Grand Rapids Invite 2025
104 Alabama Win 14-7 1821.85 Mar 29th Huck Finn 2025
44 Emory Win 14-10 2006.9 Mar 29th Huck Finn 2025
77 Iowa State Win 15-9 1865.51 Mar 29th Huck Finn 2025
194 Saint Louis** Win 15-6 1452 Ignored Mar 29th Huck Finn 2025
35 Chicago Win 12-8 2095.49 Mar 30th Huck Finn 2025
11 Davenport Loss 7-15 1380.95 Mar 30th Huck Finn 2025
51 Purdue Loss 9-11 1306.65 Mar 30th Huck Finn 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)