#74 Washington University (9-7)

avg: 1418.49  •  sd: 83.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 Southern California Loss 7-13 1076.36 Jan 27th Santa Barbara Invitational 2018
53 UCLA Loss 7-13 976.89 Jan 27th Santa Barbara Invitational 2018
15 Stanford Loss 4-13 1285.63 Jan 27th Santa Barbara Invitational 2018
25 Victoria Loss 9-13 1313.17 Jan 27th Santa Barbara Invitational 2018
146 Nevada-Reno Win 13-12 1274.3 Jan 28th Santa Barbara Invitational 2018
100 Arizona Win 13-10 1663.62 Jan 28th Santa Barbara Invitational 2018
47 Iowa State Loss 7-12 1047.73 Mar 3rd Midwest Throwdown 2018
114 Minnesota-Duluth Win 13-7 1838.6 Mar 3rd Midwest Throwdown 2018
49 Marquette Win 15-6 2148.36 Mar 3rd Midwest Throwdown 2018
114 Minnesota-Duluth Win 11-10 1406.07 Mar 4th Midwest Throwdown 2018
233 Missouri** Win 15-1 1412.4 Ignored Mar 4th Midwest Throwdown 2018
51 Ohio State Loss 3-15 937.69 Mar 4th Midwest Throwdown 2018
69 Carleton College-GoP Win 11-10 1574.46 Mar 4th Midwest Throwdown 2018
45 Illinois State Loss 9-14 1112.29 Mar 31st Huck Finn 2018
133 Case Western Reserve Win 13-10 1503.91 Mar 31st Huck Finn 2018
29 Texas Win 12-11 1836.1 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)