#374 Maine-Farmington (2-5)

avg: 285.41  •  sd: 75.27  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
213 Jefferson Loss 6-13 305.64 Feb 24th Bring the Huckus 9
313 Ursinus Loss 8-12 90.05 Feb 24th Bring the Huckus 9
372 Temple-B Win 11-9 542.14 Feb 24th Bring the Huckus 9
- Saint Rose Loss 4-13 203.8 Feb 24th Bring the Huckus 9
119 Bates** Loss 0-13 664.63 Ignored Mar 11th Great Falls Invitational 2018
- Bates-B Win 13-8 285.41 Mar 11th Great Falls Invitational 2018
172 Colby** Loss 4-13 435.77 Ignored Mar 11th Great Falls Invitational 2018
**Blowout Eligible

FAQ

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)