#72 Iowa State (15-4)

avg: 1230.49  •  sd: 68.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
52 Arkansas Win 9-8 1535.94 Feb 25th Dust Bowl 2023
164 Colorado Mines Win 10-5 1061.13 Feb 25th Dust Bowl 2023
208 Oklahoma** Win 11-2 588.97 Ignored Feb 25th Dust Bowl 2023
52 Arkansas Loss 5-7 1082.8 Feb 26th Dust Bowl 2023
172 Missouri State** Win 10-3 1002.17 Ignored Feb 26th Dust Bowl 2023
66 Kansas Win 9-8 1393.16 Feb 26th Dust Bowl 2023
52 Arkansas Loss 9-10 1285.94 Mar 4th Midwest Throwdown 2023
152 Grinnell** Win 11-4 1195.43 Ignored Mar 4th Midwest Throwdown 2023
142 Macalester Win 11-6 1220.15 Mar 4th Midwest Throwdown 2023
193 Minnesota-Duluth** Win 11-2 795.81 Ignored Mar 4th Midwest Throwdown 2023
104 Iowa Win 9-7 1246.2 Mar 5th Midwest Throwdown 2023
66 Kansas Win 10-9 1393.16 Mar 5th Midwest Throwdown 2023
68 Winona State Loss 3-8 666.92 Mar 5th Midwest Throwdown 2023
124 Saint Louis Win 9-4 1419.4 Mar 25th Old Capitol Open
153 Loyola-Chicago Win 8-5 1039.19 Mar 25th Old Capitol Open
180 Wisconsin-La Crosse Win 9-4 934.74 Mar 25th Old Capitol Open
27 Minnesota Loss 4-9 1085.25 Mar 25th Old Capitol Open
105 Michigan Tech Win 9-7 1239.16 Mar 26th Old Capitol Open
68 Winona State Win 6-3 1813.62 Mar 26th Old Capitol Open
**Blowout Eligible

FAQ

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
  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
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)