#89 Iowa (4-8)

avg: 1570.48  •  sd: 69.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
134 Tennessee Win 10-4 1876.04 Jan 13th Florida Winter Classic 2018
54 Florida State Loss 3-10 1256.06 Jan 13th Florida Winter Classic 2018
207 Miami** Win 14-2 1402.34 Ignored Jan 13th Florida Winter Classic 2018
62 Central Florida Loss 4-6 1433.27 Jan 14th Florida Winter Classic 2018
48 Georgia Loss 7-13 1398.05 Jan 14th Florida Winter Classic 2018
141 North Georgia Win 9-3 1842.65 Jan 14th Florida Winter Classic 2018
79 Chicago Loss 11-13 1424.54 Mar 3rd Midwest Throwdown 2018
57 Kansas Loss 5-10 1262.64 Mar 3rd Midwest Throwdown 2018
28 Washington University Loss 5-11 1513.89 Mar 3rd Midwest Throwdown 2018
37 Northwestern Loss 6-11 1481.48 Mar 4th Midwest Throwdown 2018
143 Truman State Win 9-5 1759.88 Mar 4th Midwest Throwdown 2018
22 Minnesota Loss 11-13 1999.99 Mar 4th Midwest Throwdown 2018
**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)