#243 Haverford (6-5)

avg: 882.39  •  sd: 96.41  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
139 Pennsylvania Loss 5-13 629.67 Mar 24th Hucktastic Spring 2019
- New Jersey Tech** Win 13-0 741.94 Ignored Mar 24th Hucktastic Spring 2019
282 Catholic Win 8-6 1042.44 Mar 24th Hucktastic Spring 2019
115 Villanova Loss 3-10 696.39 Mar 24th Hucktastic Spring 2019
- Franklin & Marshall** Win 13-0 162.63 Ignored Mar 24th Hucktastic Spring 2019
405 Jefferson Win 8-4 754.04 Mar 30th Layout Pigout 2019
96 Bowdoin Loss 3-13 767.81 Mar 30th Layout Pigout 2019
331 Kenyon Win 13-7 1117.51 Mar 30th Layout Pigout 2019
163 SUNY-Geneseo Win 9-7 1385.91 Mar 30th Layout Pigout 2019
107 Franciscan Loss 10-13 997.38 Mar 31st Layout Pigout 2019
163 SUNY-Geneseo Loss 7-13 549.04 Mar 31st Layout Pigout 2019
**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)