#76 Bates (9-3)

avg: 1372.17  •  sd: 118.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
93 Wesleyan Loss 6-9 853.9 Mar 2nd No Sleep till Brooklyn 2024
85 Wellesley Loss 2-12 713.8 Mar 2nd No Sleep till Brooklyn 2024
193 Columbia-B Win 6-3 845.96 Mar 2nd No Sleep till Brooklyn 2024
193 Columbia-B** Win 14-2 899.26 Ignored Mar 3rd No Sleep till Brooklyn 2024
176 SUNY-Stony Brook** Win 9-2 1105.03 Ignored Mar 3rd No Sleep till Brooklyn 2024
117 Boston University Win 8-6 1327.34 Mar 30th Northeast Classic 2024
135 NYU Win 10-4 1510.84 Mar 30th Northeast Classic 2024
54 Haverford/Bryn Mawr Win 7-5 1889.59 Mar 30th Northeast Classic 2024
146 RIT Win 11-3 1443.66 Mar 31st Northeast Classic 2024
85 Wellesley Win 10-6 1809.96 Mar 31st Northeast Classic 2024
93 Wesleyan Win 8-4 1837.27 Mar 31st Northeast Classic 2024
54 Haverford/Bryn Mawr Loss 8-11 1195.84 Mar 31st Northeast Classic 2024
**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)