#40 Dartmouth (11-9)

avg: 1686.47  •  sd: 69.58  •  top 16/20: 0.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
5 Cal Poly-SLO Loss 8-13 1648.3 Jan 26th Santa Barbara Invite 2019
100 California-Santa Cruz Win 13-7 1916.3 Jan 26th Santa Barbara Invite 2019
51 Western Washington Loss 8-12 1188.61 Jan 26th Santa Barbara Invite 2019
29 Texas-Dallas Loss 6-13 1171.91 Jan 26th Santa Barbara Invite 2019
74 Arizona Win 13-7 2036.62 Jan 27th Santa Barbara Invite 2019
56 California-San Diego Win 13-10 1920.91 Jan 27th Santa Barbara Invite 2019
94 Appalachian State Loss 9-10 1247.43 Feb 9th Queen City Tune Up 2019 Men
14 Ohio State Loss 3-13 1392.06 Feb 9th Queen City Tune Up 2019 Men
1 North Carolina Loss 7-13 1674.39 Feb 9th Queen City Tune Up 2019 Men
61 Tennessee Win 12-9 1899.56 Feb 9th Queen City Tune Up 2019 Men
94 Appalachian State Win 15-13 1586.61 Feb 10th Queen City Tune Up 2019 Men
57 Carnegie Mellon Win 13-9 2005.94 Feb 10th Queen City Tune Up 2019 Men
44 Virginia Loss 9-15 1155.93 Feb 10th Queen City Tune Up 2019 Men
98 Kansas Win 13-9 1781.75 Mar 16th Centex 2019 Men
152 Arkansas Win 13-4 1753.2 Mar 16th Centex 2019 Men
37 Illinois Win 15-13 1934.57 Mar 16th Centex 2019 Men
67 Oklahoma State Win 13-4 2133.96 Mar 16th Centex 2019 Men
46 Iowa State Win 15-12 1959.73 Mar 17th Centex 2019 Men
8 Colorado Loss 10-14 1696.74 Mar 17th Centex 2019 Men
19 Colorado State Loss 8-15 1334.74 Mar 17th Centex 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)