#76 Purdue (9-11)

avg: 1357.22  •  sd: 57.64  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
119 Berry Win 11-9 1421.53 Feb 10th Golden Triangle Invitational
105 Mississippi State Win 11-9 1459.99 Feb 10th Golden Triangle Invitational
87 Tennessee-Chattanooga Win 11-6 1856.68 Feb 10th Golden Triangle Invitational
57 Auburn Loss 11-12 1322.19 Feb 11th Golden Triangle Invitational
158 Kennesaw State Win 13-1 1610.72 Feb 11th Golden Triangle Invitational
38 Duke Loss 5-13 990.76 Feb 24th Easterns Qualifier 2024
16 Penn State Loss 5-13 1321.23 Feb 24th Easterns Qualifier 2024
58 Maryland Loss 9-12 1097.6 Feb 24th Easterns Qualifier 2024
52 Virginia Tech Loss 11-12 1350.52 Feb 24th Easterns Qualifier 2024
50 Alabama Win 11-10 1626.57 Feb 25th Easterns Qualifier 2024
68 James Madison Win 15-10 1830.5 Feb 25th Easterns Qualifier 2024
158 Kennesaw State Win 12-9 1356.08 Feb 25th Easterns Qualifier 2024
58 Maryland Loss 10-13 1114.82 Feb 25th Easterns Qualifier 2024
50 Alabama Loss 8-12 1060.41 Mar 30th Huck Finn 2024
19 Washington University Loss 6-12 1285.86 Mar 30th Huck Finn 2024
49 St Olaf Loss 10-11 1378.16 Mar 30th Huck Finn 2024
66 Virginia Loss 10-11 1269.17 Mar 30th Huck Finn 2024
53 Colorado State Loss 7-12 950.04 Mar 31st Huck Finn 2024
118 Michigan Tech Win 13-5 1773.62 Mar 31st Huck Finn 2024
105 Mississippi State Win 10-9 1335.79 Mar 31st Huck Finn 2024
**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)