#182 Knox (9-9)

avg: 609.1  •  sd: 95.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Iowa** Loss 4-13 612.27 Ignored Mar 2nd Midwest Throwdown 2024
127 Wisconsin-Eau Claire Loss 2-9 441.07 Mar 2nd Midwest Throwdown 2024
177 Missouri State Loss 6-7 557.97 Mar 2nd Midwest Throwdown 2024
231 Wisconsin-B Win 7-6 265.11 Mar 3rd Midwest Throwdown 2024
127 Wisconsin-Eau Claire Loss 4-8 476.26 Mar 3rd Midwest Throwdown 2024
129 Illinois Loss 7-8 912.5 Mar 30th Illinois Invite 2024
237 Northwestern-B Win 13-0 621.81 Mar 30th Illinois Invite 2024
190 Michigan-B Win 9-2 1153.14 Mar 30th Illinois Invite 2024
195 Washington University-B Win 7-2 1111.38 Mar 30th Illinois Invite 2024
129 Illinois Loss 5-12 437.5 Mar 31st Illinois Invite 2024
190 Michigan-B Win 9-8 678.14 Mar 31st Illinois Invite 2024
100 Davenport** Loss 1-9 619.44 Ignored Apr 13th Great Lakes D III Womens Conferences 2024
246 Kalamazoo** Win 9-1 -36.93 Ignored Apr 13th Great Lakes D III Womens Conferences 2024
247 North Park** Win 8-2 -636.93 Ignored Apr 13th Great Lakes D III Womens Conferences 2024
167 Wheaton (Illinois) Loss 4-8 221.6 Apr 13th Great Lakes D III Womens Conferences 2024
224 Butler Win 9-7 499.49 Apr 14th Great Lakes D III Womens Conferences 2024
224 Butler Win 8-6 520.64 Apr 14th Great Lakes D III Womens Conferences 2024
100 Davenport** Loss 3-12 619.44 Ignored Apr 14th Great Lakes D III Womens Conferences 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)