#180 Brigham Young-B (6-13)

avg: 1194.94  •  sd: 69.38  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
219 Arizona Loss 7-9 773.15 Jan 27th New Year Fest 40
156 Denver Loss 6-13 696.21 Jan 27th New Year Fest 40
137 Kansas Loss 6-10 869.19 Jan 27th New Year Fest 40
100 Colorado-B Loss 4-13 913.43 Jan 27th New Year Fest 40
119 Colorado College Loss 8-15 843.35 Mar 1st Snow Melt 2024
100 Colorado-B Win 12-10 1751.55 Mar 2nd Snow Melt 2024
149 Montana State Loss 6-13 715.09 Mar 2nd Snow Melt 2024
101 Colorado Mines Loss 8-10 1249.42 Mar 2nd Snow Melt 2024
371 Boise State** Win 10-0 956.76 Ignored Mar 9th Big Sky Brawl 2024
288 Montana Win 10-4 1380.85 Mar 9th Big Sky Brawl 2024
58 Utah Valley Loss 4-9 1137.54 Mar 9th Big Sky Brawl 2024
328 Nevada-Reno Win 13-0 1202 Mar 9th Big Sky Brawl 2024
29 Utah Loss 7-13 1389.53 Mar 29th Utah Valley Rally
29 Utah Loss 7-13 1389.53 Mar 30th Utah Valley Rally
32 Utah State** Loss 0-13 1308.9 Ignored Mar 30th Utah Valley Rally
32 Utah State** Loss 5-15 1308.9 Ignored Apr 13th Big Sky D I Mens Conferences 2024
58 Utah Valley Loss 5-15 1137.54 Apr 13th Big Sky D I Mens Conferences 2024
321 Idaho Win 15-7 1240.22 Apr 13th Big Sky D I Mens Conferences 2024
160 Washington State Win 13-9 1702.48 Apr 13th Big Sky D I Mens Conferences 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)