#158 Grinnell (10-7)

avg: 1220.19  •  sd: 71.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
288 Harding Win 9-5 1274.4 Feb 22nd Dust Bowl 2025
278 Texas Tech Win 11-2 1368.35 Feb 22nd Dust Bowl 2025
160 Kansas Win 10-8 1479.86 Feb 22nd Dust Bowl 2025
171 Northern Iowa Win 9-8 1304.26 Feb 23rd Dust Bowl 2025
87 Missouri S&T Loss 4-9 891.08 Feb 23rd Dust Bowl 2025
133 North Texas Loss 8-9 1188 Feb 23rd Dust Bowl 2025
276 St John's (Minnesota) Win 13-9 1191.1 Mar 29th Old Capitol Open 2025
145 Wisconsin-Milwaukee Loss 8-13 773.76 Mar 29th Old Capitol Open 2025
177 Minnesota-Duluth Win 13-7 1712.88 Mar 29th Old Capitol Open 2025
117 Colorado Mines Loss 8-12 922.1 Mar 30th Old Capitol Open 2025
171 Northern Iowa Win 11-3 1779.26 Mar 30th Old Capitol Open 2025
303 Luther Win 12-10 904.08 Apr 12th West Plains D III Mens Conferences 2025
60 Carleton College-CHOP Loss 7-15 1046.37 Apr 26th North Central D III College Mens Regionals 2025
151 Macalester Loss 10-11 1136.32 Apr 26th North Central D III College Mens Regionals 2025
303 Luther Win 14-9 1139.83 Apr 26th North Central D III College Mens Regionals 2025
60 Carleton College-CHOP Loss 6-13 1046.37 Apr 27th North Central D III College Mens Regionals 2025
169 Michigan Tech Win 15-9 1704.09 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)