#33 Bates (16-0)

avg: 1706.49  •  sd: 120.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Colby Win 12-7 1778.13 Mar 2nd Bates First Annual First Big Dance
150 Bowdoin** Win 12-5 1467.72 Ignored Mar 2nd Bates First Annual First Big Dance
- Bates B** Win 15-0 600 Ignored Mar 2nd Bates First Annual First Big Dance
233 SUNY Cortland** Win 13-2 878.81 Ignored Mar 9th No Sleep Till Brooklyn
159 SUNY-Albany Win 11-5 1424.44 Mar 9th No Sleep Till Brooklyn
127 SUNY-Stony Brook** Win 13-1 1607.41 Ignored Mar 9th No Sleep Till Brooklyn
130 Connecticut** Win 13-2 1592.45 Ignored Mar 9th No Sleep Till Brooklyn
44 Brown Win 11-3 2154.41 Mar 10th No Sleep Till Brooklyn
47 Williams Win 7-4 2022.57 Mar 10th No Sleep Till Brooklyn
238 Worcester Polytechnic** Win 11-1 838.69 Ignored Mar 23rd Live Free and Sky 2019
124 Brandeis Win 9-6 1434.49 Mar 23rd Live Free and Sky 2019
170 Vermont-B Win 4-2 1231.76 Mar 23rd Live Free and Sky 2019
63 New Hampshire Win 7-5 1743.01 Mar 23rd Live Free and Sky 2019
221 Bentley** Win 13-2 986.51 Ignored Mar 24th Live Free and Sky 2019
124 Brandeis** Win 13-2 1615.92 Ignored Mar 24th Live Free and Sky 2019
76 Rensselaer Polytech Win 10-6 1782.03 Mar 24th Live Free and Sky 2019
**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)