#331 New Jersey Tech (4-6)

avg: 163.75  •  sd: 68.82  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
313 Dartmouth-B Loss 7-13 -273.28 Mar 16th Free Tournament
166 Villanova** Loss 5-13 358.54 Ignored Mar 16th Free Tournament
377 RIT-B Win 13-1 232.37 Mar 16th Free Tournament
316 SUNY-Fredonia Win 8-7 398.54 Mar 16th Free Tournament
120 Army** Loss 3-15 560.57 Ignored Mar 17th Free Tournament
377 RIT-B Win 15-1 232.37 Mar 17th Free Tournament
316 SUNY-Fredonia Loss 7-8 148.54 Mar 17th Free Tournament
321 SUNY-Binghamton-B Loss 7-8 130.69 Mar 23rd King of New York 2024
252 Dickinson Loss 6-8 309.94 Mar 24th King of New York 2024
367 Siena Win 8-6 166.87 Mar 24th King of New York 2024
**Blowout Eligible


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)