#20 Tufts (10-12)

avg: 1864.15  •  sd: 54.38  •  top 16/20: 46.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Emory Win 11-10 1633.46 Feb 8th Florida Warm Up 2019
15 Central Florida Loss 11-13 1761.47 Feb 8th Florida Warm Up 2019
73 Temple Win 13-6 2080.87 Feb 8th Florida Warm Up 2019
48 Kennesaw State Loss 9-11 1397.28 Feb 9th Florida Warm Up 2019
7 Carleton College-CUT Win 15-13 2332.82 Feb 9th Florida Warm Up 2019
13 Wisconsin Loss 9-10 1875.97 Feb 9th Florida Warm Up 2019
12 Texas Loss 9-11 1760.7 Feb 9th Florida Warm Up 2019
17 Minnesota Win 15-8 2515.86 Feb 10th Florida Warm Up 2019
27 LSU Loss 11-12 1652.74 Feb 10th Florida Warm Up 2019
25 South Carolina Loss 12-13 1661.69 Mar 9th Classic City Invite 2019
55 Florida State Win 13-8 2107.83 Mar 9th Classic City Invite 2019
11 North Carolina State Loss 10-13 1699.43 Mar 9th Classic City Invite 2019
61 Tennessee Win 13-9 1972.76 Mar 9th Classic City Invite 2019
48 Kennesaw State Win 9-8 1771.49 Mar 10th Classic City Invite 2019
28 Northeastern Win 10-9 1900.83 Mar 10th Classic City Invite 2019
4 Pittsburgh Loss 9-13 1766.36 Mar 30th Easterns 2019 Men
49 Northwestern Loss 12-13 1512.69 Mar 30th Easterns 2019 Men
26 North Carolina-Wilmington Win 13-11 2009.82 Mar 30th Easterns 2019 Men
32 William & Mary Win 13-9 2165.25 Mar 30th Easterns 2019 Men
9 Massachusetts Loss 9-11 1816.29 Mar 31st Easterns 2019 Men
3 Oregon Loss 12-15 1888.49 Mar 31st Easterns 2019 Men
11 North Carolina State Loss 10-12 1789.45 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)