#146 Brigham Young-B (5-2)

avg: 967.32  •  sd: 105.55  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
54 Utah Valley Loss 10-13 1045.85 Jan 28th New Year Fest 2023
216 Arizona Win 13-9 993.58 Jan 28th New Year Fest 2023
291 Denver-B** Win 13-0 642.9 Ignored Jan 28th New Year Fest 2023
161 Colorado-B Win 12-10 1141.74 Jan 28th New Year Fest 2023
212 Boise State Win 15-6 1206.15 Mar 4th Big Sky Brawl1
229 Montana Win 15-5 1093.99 Mar 4th Big Sky Brawl1
138 Washington State Loss 4-13 392.64 Mar 4th Big Sky Brawl1
**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)