#304 Rhode Island (4-9)

avg: 644.91  •  sd: 69.78  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
319 North Carolina State -B Win 11-10 719.87 Mar 9th Mash Up 2019
270 Delaware-B Loss 7-8 657.13 Mar 9th Mash Up 2019
64 Ohio** Loss 3-13 939.4 Ignored Mar 9th Mash Up 2019
126 New Hampshire** Loss 4-13 675.42 Ignored Mar 9th Mash Up 2019
267 SUNY-Fredonia Loss 9-10 662.71 Mar 10th Mash Up 2019
319 North Carolina State -B Loss 4-13 -5.13 Mar 10th Mash Up 2019
134 Boston University Loss 7-9 965.05 Mar 23rd Spring Awakening 8
281 Skidmore Win 7-6 874.6 Mar 23rd Spring Awakening 8
178 Army Loss 2-5 459.73 Mar 23rd Spring Awakening 8
343 Dickinson Win 8-6 812.79 Mar 23rd Spring Awakening 8
375 Vermont-B Win 11-7 821.64 Mar 24th Spring Awakening 8
96 Bowdoin** Loss 5-13 767.81 Ignored Mar 24th Spring Awakening 8
293 Wentworth Loss 8-10 438.74 Mar 24th Spring Awakening 8
**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)