#171 NYU (5-7)

avg: 735.1  •  sd: 94.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
99 MIT Loss 3-10 527.48 Mar 9th No Sleep Till Brooklyn
44 Brown** Loss 1-12 954.41 Ignored Mar 9th No Sleep Till Brooklyn
183 Marist Win 9-8 751.16 Mar 9th No Sleep Till Brooklyn
47 Williams Loss 4-7 1030.25 Mar 9th No Sleep Till Brooklyn
233 SUNY Cortland Win 8-3 878.81 Mar 10th No Sleep Till Brooklyn
127 SUNY-Stony Brook Loss 3-9 407.41 Mar 10th No Sleep Till Brooklyn
236 SUNY-Buffalo Win 11-5 863.68 Mar 30th Towsontown Throwdown 2019
145 American Loss 6-11 345.08 Mar 30th Towsontown Throwdown 2019
121 Towson Loss 4-10 424.96 Mar 30th Towsontown Throwdown 2019
283 Rhode Island** Win 13-0 132.25 Ignored Mar 30th Towsontown Throwdown 2019
131 Rutgers Loss 7-8 865.5 Mar 31st Towsontown Throwdown 2019
165 Temple Win 9-5 1306.86 Mar 31st Towsontown Throwdown 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)