#51 Purdue (11-9)

avg: 1555.86  •  sd: 71.81  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
8 Brigham Young Loss 7-13 1513.03 Jan 31st Florida Warm Up 2025
62 Tulane Win 10-7 1852.02 Jan 31st Florida Warm Up 2025
40 Wisconsin Loss 6-13 1028.01 Jan 31st Florida Warm Up 2025
28 Pittsburgh Loss 6-13 1164.73 Feb 1st Florida Warm Up 2025
134 South Florida Win 13-5 1712.4 Feb 1st Florida Warm Up 2025
40 Wisconsin Loss 8-13 1131.85 Feb 1st Florida Warm Up 2025
38 Utah State Loss 12-13 1507.62 Feb 2nd Florida Warm Up 2025
103 Texas A&M Loss 5-9 711.6 Feb 2nd Florida Warm Up 2025
64 James Madison Win 13-12 1582.38 Feb 15th Queen City Tune Up 2025
49 North Carolina State Loss 8-13 1068.64 Feb 15th Queen City Tune Up 2025
25 Penn State Loss 9-13 1408.81 Feb 15th Queen City Tune Up 2025
75 Carnegie Mellon Win 10-7 1760.92 Feb 16th Queen City Tune Up 2025
37 North Carolina-Wilmington Win 9-8 1760.07 Feb 16th Queen City Tune Up 2025
135 Mississippi State Win 13-6 1711.28 Mar 29th Huck Finn 2025
141 Northwestern Win 15-8 1637.67 Mar 29th Huck Finn 2025
74 Oklahoma Christian Win 11-9 1627.3 Mar 29th Huck Finn 2025
82 St Olaf Win 13-8 1817.16 Mar 29th Huck Finn 2025
50 Colorado State Win 12-9 1907 Mar 30th Huck Finn 2025
63 Notre Dame Win 11-9 1708.67 Mar 30th Huck Finn 2025
15 Washington University Loss 11-13 1722.79 Mar 30th Huck Finn 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)