#158 Case Western Reserve (2-16)

avg: 834.5  •  sd: 89.13  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
61 Florida Loss 6-9 1074.87 Feb 10th Queen City Tune Up 2024
8 Tufts** Loss 0-15 1755.7 Ignored Feb 10th Queen City Tune Up 2024
40 Minnesota** Loss 1-15 1113.8 Ignored Feb 10th Queen City Tune Up 2024
21 Northeastern** Loss 2-15 1359.88 Ignored Feb 10th Queen City Tune Up 2024
24 Ohio State** Loss 5-15 1293.87 Ignored Feb 11th Queen City Tune Up 2024
104 Appalachian State Loss 2-6 596.7 Mar 23rd Needle in a Ho Stack 2024
120 Charleston Win 7-6 1207.15 Mar 23rd Needle in a Ho Stack 2024
249 Emory-B** Win 13-0 600 Ignored Mar 24th Needle in a Ho Stack 2024
74 Davidson Loss 3-10 775.12 Mar 24th Needle in a Ho Stack 2024
89 Virginia Tech Loss 1-10 706.92 Mar 24th Needle in a Ho Stack 2024
49 Ohio Loss 3-6 1060.01 Apr 20th Ohio D I Womens Conferences 2024
24 Ohio State** Loss 0-9 1293.87 Ignored Apr 20th Ohio D I Womens Conferences 2024
80 Cincinnati Loss 1-11 754.51 Apr 20th Ohio D I Womens Conferences 2024
49 Ohio** Loss 3-11 1006.71 Ignored Apr 27th Ohio Valley D I College Womens Regionals 2024
108 West Chester Loss 2-11 579.47 Apr 27th Ohio Valley D I College Womens Regionals 2024
64 Penn State Loss 6-10 980.82 Apr 27th Ohio Valley D I College Womens Regionals 2024
16 Pennsylvania** Loss 3-13 1499.13 Ignored Apr 27th Ohio Valley D I College Womens Regionals 2024
80 Cincinnati Loss 5-15 754.51 Apr 28th Ohio Valley D I College Womens Regionals 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)