#46 Iowa State (6-5)

avg: 1659.23  •  sd: 99.71  •  top 16/20: 1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 Oklahoma Win 13-8 1948.13 Mar 16th Centex 2019 Men
82 Texas State Win 13-8 1938.81 Mar 16th Centex 2019 Men
76 Utah Loss 9-13 1055.16 Mar 16th Centex 2019 Men
29 Texas-Dallas Win 14-11 2085.24 Mar 16th Centex 2019 Men
12 Texas Loss 14-15 1884.9 Mar 17th Centex 2019 Men
40 Dartmouth Loss 12-15 1385.98 Mar 17th Centex 2019 Men
106 Illinois State Win 11-5 1927.34 Mar 30th Huck Finn XXIII
38 Purdue Loss 7-9 1427.71 Mar 31st Huck Finn XXIII
106 Illinois State Win 7-3 1927.34 Mar 31st Huck Finn XXIII
92 John Brown Win 6-4 1743.29 Mar 31st Huck Finn XXIII
68 Cincinnati Loss 7-11 1048.48 Mar 31st Huck Finn XXIII
**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)