#17 Minnesota (12-10)

avg: 1951.05  •  sd: 57.34  •  top 16/20: 94.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
55 Florida State Win 13-10 1939.82 Feb 8th Florida Warm Up 2019
2 Brown Loss 7-13 1671.63 Feb 8th Florida Warm Up 2019
12 Texas Loss 10-12 1771.78 Feb 8th Florida Warm Up 2019
83 Rutgers Win 10-9 1557.97 Feb 9th Florida Warm Up 2019
43 Harvard Win 10-8 1934.94 Feb 9th Florida Warm Up 2019
22 Georgia Loss 9-11 1585.29 Feb 9th Florida Warm Up 2019
28 Northeastern Win 13-9 2194.4 Feb 9th Florida Warm Up 2019
20 Tufts Loss 8-15 1299.34 Feb 10th Florida Warm Up 2019
43 Harvard Win 11-7 2139.17 Feb 10th Florida Warm Up 2019
5 Cal Poly-SLO Loss 7-10 1754.79 Mar 2nd Stanford Invite 2019
8 Colorado Loss 9-10 1970.44 Mar 2nd Stanford Invite 2019
12 Texas Loss 12-13 1884.9 Mar 2nd Stanford Invite 2019
50 Stanford Win 13-6 2232.74 Mar 3rd Stanford Invite 2019
10 Washington Loss 9-10 1919.51 Mar 3rd Stanford Invite 2019
49 Northwestern Win 10-6 2133.85 Mar 3rd Stanford Invite 2019
44 Virginia Win 13-9 2089.98 Mar 30th Easterns 2019 Men
3 Oregon Loss 5-13 1588.99 Mar 30th Easterns 2019 Men
9 Massachusetts Loss 11-13 1836.66 Mar 30th Easterns 2019 Men
45 California-Santa Barbara Win 13-7 2220.79 Mar 30th Easterns 2019 Men
32 William & Mary Win 15-8 2311.49 Mar 31st Easterns 2019 Men
22 Georgia Win 12-9 2179.86 Mar 31st Easterns 2019 Men
45 California-Santa Barbara Win 15-7 2263.25 Mar 31st Easterns 2019 Men
**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)