#22 Minnesota (9-5)

avg: 2228.83  •  sd: 105.57  •  top 16/20: 41.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 Western Washington Win 11-10 2469.39 Feb 17th Presidents Day Invitational Tournament 2018
17 California-Santa Barbara Loss 7-11 1853.37 Feb 17th Presidents Day Invitational Tournament 2018
11 Texas Win 11-10 2596.75 Feb 17th Presidents Day Invitational Tournament 2018
61 California-Davis Loss 7-9 1538.58 Feb 17th Presidents Day Invitational Tournament 2018
37 Northwestern Win 12-7 2548.69 Feb 18th Presidents Day Invitational Tournament 2018
2 California-San Diego Loss 6-9 2314.25 Feb 18th Presidents Day Invitational Tournament 2018
26 California Loss 8-12 1692.08 Feb 18th Presidents Day Invitational Tournament 2018
16 Western Washington Win 11-7 2811.28 Feb 19th Presidents Day Invitational Tournament 2018
43 Southern California Win 8-3 2590.28 Feb 19th Presidents Day Invitational Tournament 2018
42 Wisconsin Win 13-9 2422.26 Mar 3rd Midwest Throwdown 2018
112 Illinois** Win 14-1 2031.05 Ignored Mar 3rd Midwest Throwdown 2018
28 Washington University Win 12-8 2555.04 Mar 4th Midwest Throwdown 2018
44 Colorado State Loss 10-11 1856.6 Mar 4th Midwest Throwdown 2018
89 Iowa Win 13-11 1799.32 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)