#94 Arizona State (12-7)

avg: 1135.58  •  sd: 67.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
139 Arizona Win 13-8 1255.51 Jan 25th New Year Fest 2025
78 Grand Canyon Loss 7-8 1167.78 Jan 25th New Year Fest 2025
145 Colorado-B Win 11-5 1305.34 Jan 25th New Year Fest 2025
98 Northern Arizona Loss 6-12 546.69 Jan 25th New Year Fest 2025
139 Arizona Win 8-5 1212.95 Jan 26th New Year Fest 2025
145 Colorado-B Win 11-3 1305.34 Jan 26th New Year Fest 2025
48 California-Irvine Loss 3-11 1076.72 Feb 1st Presidents Day Qualifiers 2025
13 Cal Poly-SLO** Loss 2-11 1670.8 Ignored Feb 1st Presidents Day Qualifiers 2025
195 California-San Diego-C** Win 13-1 637.15 Ignored Feb 1st Presidents Day Qualifiers 2025
37 California Loss 4-8 1187.55 Feb 1st Presidents Day Qualifiers 2025
176 Cal State-Long Beach** Win 12-1 925.28 Ignored Feb 2nd Presidents Day Qualifiers 2025
195 California-San Diego-C** Win 13-1 637.15 Ignored Feb 2nd Presidents Day Qualifiers 2025
162 UCLA-B** Win 12-5 1134.3 Ignored Feb 2nd Presidents Day Qualifiers 2025
145 Colorado-B Win 13-8 1201.5 Mar 1st Snow Melt 2025
157 Colorado Mines Win 15-3 1201.14 Mar 1st Snow Melt 2025
52 Colorado College Loss 5-12 1001.63 Mar 1st Snow Melt 2025
139 Arizona Win 15-3 1359.35 Mar 2nd Snow Melt 2025
77 Denver Loss 9-12 955.77 Mar 2nd Snow Melt 2025
204 Colorado College-B** Win 13-2 600 Ignored Mar 2nd Snow Melt 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)