#8 Massachusetts (13-3)

avg: 1963.77  •  sd: 64.35  •  top 16/20: 98.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
7 Pittsburgh Win 11-8 2353.07 Feb 3rd Queen City Tune Up 2018 College Open
150 North Carolina-Asheville** Win 11-2 1731.08 Ignored Feb 3rd Queen City Tune Up 2018 College Open
33 Maryland Win 11-9 1933.49 Feb 3rd Queen City Tune Up 2018 College Open
39 Northwestern Win 11-4 2228.7 Feb 3rd Queen City Tune Up 2018 College Open
10 Virginia Tech Loss 8-9 1798.3 Feb 3rd Queen City Tune Up 2018 College Open
97 Alabama Win 12-9 1693.29 Mar 10th Tally Classic XIII
88 Alabama-Huntsville Win 15-10 1841.91 Mar 10th Tally Classic XIII
28 Carnegie Mellon Win 11-10 1843.65 Mar 10th Tally Classic XIII
37 Central Florida Win 13-9 2053.32 Mar 10th Tally Classic XIII
140 Florida Tech** Win 13-5 1767.47 Ignored Mar 10th Tally Classic XIII
52 Harvard Win 15-8 2100.82 Mar 11th Tally Classic XIII
9 Georgia Loss 11-15 1568.12 Mar 11th Tally Classic XIII
12 North Carolina State Loss 9-15 1403.38 Mar 31st Easterns 2018
4 Minnesota Win 15-13 2284.09 Mar 31st Easterns 2018
31 LSU Win 15-6 2299.56 Mar 31st Easterns 2018
42 Connecticut Win 15-8 2160.37 Mar 31st Easterns 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)