#163 Boston University (5-8)

avg: 1101.13  •  sd: 52.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
82 Binghamton Loss 8-9 1336.54 Jan 28th Mid Atlantic Warmup
56 James Madison Loss 4-10 999.64 Jan 28th Mid Atlantic Warmup
83 RIT Loss 7-8 1325.36 Jan 28th Mid Atlantic Warmup
248 Drexel Win 10-6 1240.71 Jan 28th Mid Atlantic Warmup
248 Drexel Win 9-7 1023.89 Jan 29th Mid Atlantic Warmup
162 American Win 10-9 1229.41 Jan 29th Mid Atlantic Warmup
274 DePaul Win 10-2 1212.69 Apr 1st Huck Finn1
151 Arizona State Win 4-3 1271.59 Apr 1st Huck Finn1
94 Saint Louis Loss 2-6 824.8 Apr 1st Huck Finn1
118 Marquette Loss 5-6 1175.78 Apr 1st Huck Finn1
65 Indiana Loss 6-15 965.84 Apr 2nd Huck Finn1
104 Florida State Loss 5-6 1220.01 Apr 2nd Huck Finn1
112 Illinois Loss 3-10 715.98 Apr 2nd Huck Finn1
**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)