#137 Montana State (8-10)

avg: 1305.33  •  sd: 63.89  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
357 Colorado Mesa Win 13-6 1003.64 Mar 1st Snow Melt 2025
391 Colorado Mines-B** Win 13-1 753.99 Ignored Mar 1st Snow Melt 2025
114 Denver Win 8-7 1500.42 Mar 1st Snow Melt 2025
36 Lewis & Clark Loss 5-13 1197.06 Mar 1st Snow Melt 2025
89 Colorado College Loss 10-13 1157.14 Mar 2nd Snow Melt 2025
117 Colorado Mines Loss 10-14 964.56 Mar 2nd Snow Melt 2025
259 Colorado State-B Win 15-10 1293.8 Mar 2nd Snow Melt 2025
367 Boise State** Win 11-1 948.32 Ignored Apr 19th Big Sky D I Mens Conferences 2025
154 Brigham Young-B Loss 8-11 878.93 Apr 19th Big Sky D I Mens Conferences 2025
27 Utah Loss 8-11 1537.85 Apr 19th Big Sky D I Mens Conferences 2025
25 Utah Valley** Loss 4-11 1344 Ignored Apr 19th Big Sky D I Mens Conferences 2025
135 Gonzaga Win 15-14 1433.08 Apr 20th Big Sky D I Mens Conferences 2025
286 Montana Win 13-9 1167.79 Apr 20th Big Sky D I Mens Conferences 2025
27 Utah Loss 4-11 1303.46 Apr 20th Big Sky D I Mens Conferences 2025
10 Washington** Loss 5-15 1507.1 Ignored May 3rd Northwest D I College Mens Regionals 2025
24 Victoria Loss 10-15 1496.41 May 3rd Northwest D I College Mens Regionals 2025
38 Utah State Loss 8-15 1229.06 May 3rd Northwest D I College Mens Regionals 2025
163 Washington-B Win 15-7 1811.9 May 4th Northwest D I College Mens Regionals 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)