#47 Iowa State (10-8)

avg: 1568.24  •  sd: 71.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
74 Washington University Win 12-7 1939 Mar 3rd Midwest Throwdown 2018
49 Marquette Win 15-11 1929.52 Mar 3rd Midwest Throwdown 2018
114 Minnesota-Duluth Win 15-9 1796.55 Mar 3rd Midwest Throwdown 2018
190 Northern Iowa Win 15-8 1540.59 Mar 4th Midwest Throwdown 2018
164 St John's Win 12-5 1673.4 Mar 4th Midwest Throwdown 2018
51 Ohio State Loss 9-11 1288.49 Mar 4th Midwest Throwdown 2018
184 Texas-San Antonio Win 13-4 1584.1 Mar 10th Mens Centex 2018
26 Texas-Dallas Loss 12-13 1604.02 Mar 10th Mens Centex 2018
21 Texas A&M Win 12-11 1947.06 Mar 10th Mens Centex 2018
63 Tulane Loss 8-10 1201.01 Mar 10th Mens Centex 2018
17 Colorado State Loss 7-13 1312.22 Mar 10th Mens Centex 2018
68 Baylor Loss 10-14 1056.12 Mar 11th Mens Centex 2018
114 Minnesota-Duluth Loss 12-13 1156.07 Mar 11th Mens Centex 2018
184 Texas-San Antonio Win 11-5 1584.1 Mar 11th Mens Centex 2018
52 Harvard Win 13-9 1954.58 Mar 31st Huck Finn 2018
11 Emory Loss 8-14 1384.64 Mar 31st Huck Finn 2018
58 Kansas Win 13-8 1997.02 Mar 31st Huck Finn 2018
89 John Brown Loss 11-12 1257.31 Mar 31st Huck Finn 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)