#200 Truman State (7-17)

avg: 433.99  •  sd: 62.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
191 Colorado Mines Win 7-6 595.45 Feb 22nd Dust Bowl 2025
256 John Brown Win 8-2 434.14 Feb 22nd Dust Bowl 2025
111 Kansas Loss 3-10 398.48 Feb 22nd Dust Bowl 2025
186 Missouri State Loss 5-7 167.03 Feb 23rd Dust Bowl 2025
192 St Olaf-B Win 5-4 595.42 Feb 23rd Dust Bowl 2025
92 Iowa State** Loss 3-9 511.73 Ignored Mar 1st Midwest Throwdown 2025
128 Winona State Loss 3-6 297.91 Mar 1st Midwest Throwdown 2025
245 Wisconsin-B Win 5-3 445 Mar 1st Midwest Throwdown 2025
43 Washington University** Loss 5-13 960.35 Ignored Mar 1st Midwest Throwdown 2025
66 Illinois Loss 5-11 708.53 Mar 2nd Midwest Throwdown 2025
128 Winona State Loss 2-10 244.6 Mar 2nd Midwest Throwdown 2025
122 Cincinnati Loss 2-13 311.56 Mar 29th Corny Classic College 2025
144 Knox Win 5-4 850.69 Mar 29th Corny Classic College 2025
246 Notre Dame-B Loss 5-6 -104.01 Mar 29th Corny Classic College 2025
254 Purdue-B Win 8-1 485.98 Mar 29th Corny Classic College 2025
66 Illinois Loss 4-8 743.72 Mar 30th Corny Classic College 2025
120 Loyola-Chicago Loss 9-10 803.79 Mar 30th Corny Classic College 2025
180 Michigan-B Loss 8-9 417.67 Mar 30th Corny Classic College 2025
73 Colorado College** Loss 5-14 660.16 Ignored Apr 12th South Central D III Womens Conferences 2025
251 Colorado College-B Win 11-8 319.52 Apr 12th South Central D III Womens Conferences 2025
88 Rice** Loss 3-11 527.4 Ignored Apr 12th South Central D III Womens Conferences 2025
135 Trinity Loss 8-12 361.8 Apr 12th South Central D III Womens Conferences 2025
73 Colorado College** Loss 2-15 660.16 Ignored Apr 13th South Central D III Womens Conferences 2025
135 Trinity Loss 2-7 202.96 Apr 13th South Central D III Womens 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)