#224 Butler (4-9)

avg: 220.15  •  sd: 95.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
140 Indiana Loss 6-10 432.65 Mar 2nd Huckleberry Flick
209 Miami (Ohio) Loss 9-11 100.12 Mar 2nd Huckleberry Flick
203 Oberlin Loss 5-11 -173.12 Mar 2nd Huckleberry Flick
243 Dayton Win 11-4 405.14 Mar 2nd Huckleberry Flick
80 Cincinnati** Loss 0-15 754.51 Ignored Mar 3rd Huckleberry Flick
243 Dayton Win 9-7 84.47 Mar 3rd Huckleberry Flick
159 Kenyon Loss 5-10 251.86 Mar 3rd Huckleberry Flick
247 North Park** Win 8-2 -636.93 Ignored Apr 13th Great Lakes D III Womens Conferences 2024
167 Wheaton (Illinois) Loss 1-8 186.41 Apr 13th Great Lakes D III Womens Conferences 2024
100 Davenport** Loss 3-10 619.44 Ignored Apr 13th Great Lakes D III Womens Conferences 2024
246 Kalamazoo** Win 9-2 -36.93 Ignored Apr 13th Great Lakes D III Womens Conferences 2024
182 Knox Loss 7-9 329.76 Apr 14th Great Lakes D III Womens Conferences 2024
182 Knox Loss 6-8 308.61 Apr 14th Great Lakes D III Womens Conferences 2024
**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)