#37 Washington University (10-6)

avg: 1670.56  •  sd: 60.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
90 Colorado State Win 12-5 1817.13 Jan 26th Santa Barbara Invite 2019
39 California-Davis Win 8-7 1718.28 Jan 26th Santa Barbara Invite 2019
4 California-Santa Barbara Loss 9-13 1861.92 Jan 26th Santa Barbara Invite 2019
19 UCLA Loss 7-13 1408.33 Jan 26th Santa Barbara Invite 2019
39 California-Davis Loss 8-10 1330.61 Jan 27th Santa Barbara Invite 2019
42 Chicago Loss 10-12 1326.37 Jan 27th Santa Barbara Invite 2019
141 Iowa** Win 13-1 1506.31 Ignored Mar 2nd Midwest Throwdown 2019
89 Iowa State Win 11-1 1823.1 Mar 2nd Midwest Throwdown 2019
75 Purdue Win 10-4 1888.08 Mar 2nd Midwest Throwdown 2019
72 Texas-Dallas Win 10-8 1589.07 Mar 2nd Midwest Throwdown 2019
29 Northwestern Loss 4-9 1167.62 Mar 23rd Womens College Centex 2019
34 Colorado College Win 11-8 2069.39 Mar 23rd Womens College Centex 2019
51 Florida State Win 8-7 1633.35 Mar 23rd Womens College Centex 2019
29 Northwestern Win 10-9 1892.62 Mar 24th Womens College Centex 2019
40 Michigan Win 8-7 1694.43 Mar 24th Womens College Centex 2019
22 Tufts Loss 11-14 1621.27 Mar 24th Womens College Centex 2019
**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)