#165 New Mexico (3-3)

avg: 972.98  •  sd: 46.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
86 Arizona State-B-B Loss 7-12 776.56 Jan 25th New Year Fest 2020
318 Arizona-B** Win 13-2 816 Ignored Jan 25th New Year Fest 2020
282 Colorado Mesa University Win 13-6 1044.84 Jan 25th New Year Fest 2020
102 Northern Arizona Loss 10-12 968.73 Jan 25th New Year Fest 2020
228 Colorado State-B Win 13-10 1068.11 Jan 26th New Year Fest 2020
94 Denver Loss 9-11 1006.65 Jan 26th New Year Fest 2020
**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)