#253 St Thomas (9-14)

avg: 862.99  •  sd: 64.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
176 Northern Iowa Loss 8-11 805.22 Mar 22nd Meltdown 2025
338 Rose-Hulman Win 11-6 1089.46 Mar 22nd Meltdown 2025
208 Wisconsin-Whitewater Loss 8-9 909.01 Mar 22nd Meltdown 2025
204 Winona State Loss 9-10 920.92 Mar 22nd Meltdown 2025
308 Illinois-Chicago Loss 6-7 521.69 Mar 23rd Meltdown 2025
273 Minnesota State-Mankato Loss 3-7 203.78 Mar 23rd Meltdown 2025
110 Iowa Loss 8-13 927.59 Mar 29th Old Capitol Open 2025
402 Wisconsin-Milwaukee-B** Win 13-5 617.81 Ignored Mar 29th Old Capitol Open 2025
309 Washington University-B Win 11-10 765.96 Mar 29th Old Capitol Open 2025
274 St John's (Minnesota) Loss 7-10 399.2 Mar 30th Old Capitol Open 2025
266 Wisconsin-Platteville Loss 5-13 211.27 Mar 30th Old Capitol Open 2025
288 Carleton College-C Win 8-7 864.46 Apr 12th Northwoods D III Mens Conferences 2025
72 St Olaf** Loss 6-15 1006.39 Ignored Apr 12th Northwoods D III Mens Conferences 2025
150 Macalester Loss 4-11 677.34 Apr 12th Northwoods D III Mens Conferences 2025
357 Bethel Win 14-9 867.18 Apr 13th Northwoods D III Mens Conferences 2025
288 Carleton College-C Win 15-11 1120.63 Apr 13th Northwoods D III Mens Conferences 2025
204 Winona State Win 14-10 1444.62 Apr 13th Northwoods D III Mens Conferences 2025
169 Michigan Tech Loss 10-13 875.86 Apr 26th North Central D III College Mens Regionals 2025
72 St Olaf** Loss 3-15 1006.39 Ignored Apr 26th North Central D III College Mens Regionals 2025
266 Wisconsin-Platteville Win 13-8 1307.43 Apr 26th North Central D III College Mens Regionals 2025
169 Michigan Tech Loss 6-15 604 Apr 27th North Central D III College Mens Regionals 2025
150 Macalester Loss 10-13 949.19 Apr 27th North Central D III College Mens Regionals 2025
301 Luther Win 15-8 1245.42 Apr 27th North Central D III College Mens Regionals 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)