#316 SUNY-Fredonia (5-5)

avg: 273.54  •  sd: 55.66  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
313 Dartmouth-B Loss 10-11 159.25 Mar 16th Free Tournament
331 New Jersey Tech Loss 7-8 38.75 Mar 16th Free Tournament
377 RIT-B Win 11-5 232.37 Mar 16th Free Tournament
166 Villanova** Loss 2-13 358.54 Ignored Mar 16th Free Tournament
313 Dartmouth-B Win 12-8 725.4 Mar 17th Free Tournament
331 New Jersey Tech Win 8-7 288.75 Mar 17th Free Tournament
197 Haverford Loss 5-13 236.18 Mar 30th Layout Pigout 2024
152 West Chester** Loss 1-13 426.57 Ignored Mar 30th Layout Pigout 2024
374 West Chester-B Win 11-6 214.46 Mar 30th Layout Pigout 2024
374 West Chester-B** Win 9-3 267.76 Ignored Mar 30th Layout Pigout 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)