#154 Brigham Young-B (11-7)

avg: 1244.54  •  sd: 61.71  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
187 Arizona Loss 11-13 877.74 Jan 25th New Year Fest 2025
269 Grand Canyon-B Win 13-3 1398.15 Jan 25th New Year Fest 2025
86 Colorado-B Loss 5-13 893.4 Jan 25th New Year Fest 2025
378 Denver-B** Win 13-2 864.1 Ignored Jan 25th New Year Fest 2025
109 San Diego State Loss 8-13 914.63 Jan 25th New Year Fest 2025
89 Colorado College Loss 6-13 885.28 Feb 28th Snow Melt 2025
117 Colorado Mines Loss 9-11 1114.05 Mar 1st Snow Melt 2025
259 Colorado State-B Win 13-9 1258.76 Mar 1st Snow Melt 2025
378 Denver-B** Win 13-1 864.1 Ignored Mar 1st Snow Melt 2025
281 California-B Win 13-3 1361.82 Mar 1st Snow Melt 2025
187 Arizona Win 11-10 1231.58 Mar 29th Sinvite 2025
352 California-San Diego-B** Win 13-1 1020.58 Ignored Mar 29th Sinvite 2025
249 Northern Arizona Win 13-3 1471.65 Mar 29th Sinvite 2025
289 Southern California-B Win 13-2 1339.54 Mar 29th Sinvite 2025
367 Boise State** Win 11-1 948.32 Ignored Apr 19th Big Sky D I Mens Conferences 2025
137 Montana State Win 11-8 1670.94 Apr 19th Big Sky D I Mens Conferences 2025
25 Utah Valley** Loss 1-11 1344 Ignored Apr 19th Big Sky D I Mens Conferences 2025
27 Utah** Loss 4-11 1303.46 Ignored Apr 19th Big Sky D I Mens Conferences 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)