#254 Purdue-B (1-17)

avg: -109.71  •  sd: 107.35  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
214 Dayton Loss 3-11 -330.13 Mar 1st Huckleberry Flick 2025
198 Indiana Loss 1-7 -143.92 Mar 1st Huckleberry Flick 2025
236 Miami (Ohio) Loss 2-8 -490.01 Mar 1st Huckleberry Flick 2025
155 Xavier** Loss 1-10 77.89 Ignored Mar 1st Huckleberry Flick 2025
214 Dayton Loss 4-8 -294.94 Mar 2nd Huckleberry Flick 2025
236 Miami (Ohio) Loss 6-9 -308.57 Mar 2nd Huckleberry Flick 2025
124 Cincinnati** Loss 2-7 311.96 Ignored Mar 29th Corny Classic College 2025
146 Knox Loss 4-8 161.6 Mar 29th Corny Classic College 2025
244 Notre Dame-B Win 7-5 364.2 Mar 29th Corny Classic College 2025
200 Truman State Loss 1-8 -163.06 Mar 29th Corny Classic College 2025
214 Dayton Loss 5-5 269.87 Mar 30th Corny Classic College 2025
223 North Park Loss 5-6 88.92 Mar 30th Corny Classic College 2025
135 Grand Valley** Loss 1-13 215.26 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
110 Michigan State** Loss 1-12 405.94 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
181 Michigan-B** Loss 2-12 -63.26 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
17 Notre Dame** Loss 0-13 1369.03 Ignored Apr 12th Eastern Great Lakes D I Womens Conferences 2025
198 Indiana Loss 1-10 -143.92 Apr 13th Eastern Great Lakes D I Womens Conferences 2025
244 Notre Dame-B Loss 1-4 -563.94 Apr 13th Eastern Great Lakes D I Womens Conferences 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)