#142 Boston College (6-6)

avg: 855.31  •  sd: 53.9  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Central Florida** Loss 3-13 1219.58 Ignored Feb 24th Mardi Gras XXXVI college
136 Florida State Loss 5-6 785.04 Feb 24th Mardi Gras XXXVI college
184 Jacksonville State Win 6-2 1022.56 Feb 24th Mardi Gras XXXVI college
185 Tulane Win 9-5 949.81 Feb 24th Mardi Gras XXXVI college
211 LSU** Win 13-0 681.9 Ignored Feb 25th Mardi Gras XXXVI college
89 Trinity Loss 8-10 1031.97 Feb 25th Mardi Gras XXXVI college
103 Lehigh Loss 5-8 716.89 Mar 24th King of New York 2024
103 Lehigh Loss 6-10 674.34 Mar 24th King of New York 2024
210 SUNY-Cortland** Win 8-0 687.63 Ignored Mar 24th King of New York 2024
164 Ithaca Win 8-7 759 Mar 24th King of New York 2024
165 SUNY-Geneseo Win 9-5 1127.35 Mar 24th King of New York 2024
103 Lehigh Loss 5-8 716.89 Mar 24th King of New York 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)