#95 Iowa (7-5)

avg: 1252.41  •  sd: 62.57  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
160 Knox Win 13-4 1274.39 Mar 2nd Midwest Throwdown 2024
148 Missouri State Win 9-2 1426.22 Mar 2nd Midwest Throwdown 2024
119 Wisconsin-Eau Claire Win 7-4 1510.53 Mar 2nd Midwest Throwdown 2024
92 Saint Louis Loss 4-6 906.87 Mar 3rd Midwest Throwdown 2024
84 Iowa State Win 9-8 1448.81 Mar 3rd Midwest Throwdown 2024
51 Macalester Loss 5-7 1280.78 Mar 3rd Midwest Throwdown 2024
28 St Olaf** Loss 2-9 1321.34 Ignored Mar 30th Old Capitol Open 2024
100 Davenport Loss 7-9 914.63 Mar 30th Old Capitol Open 2024
145 Grinnell Win 6-3 1390.78 Mar 30th Old Capitol Open 2024
140 Wisconsin-Milwaukee Win 9-3 1485.46 Mar 30th Old Capitol Open 2024
140 Wisconsin-Milwaukee Win 9-6 1304.02 Mar 31st Old Capitol Open 2024
77 Michigan Tech Loss 5-8 916.83 Mar 31st Old Capitol Open 2024
**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)