#294 Knox (10-15)

avg: 737.81  •  sd: 53.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
78 Carleton College-CHOP** Loss 4-13 1004.23 Ignored Mar 2nd Midwest Throwdown 2024
143 Truman State Loss 7-11 870.17 Mar 2nd Midwest Throwdown 2024
51 Missouri** Loss 3-13 1164.88 Ignored Mar 2nd Midwest Throwdown 2024
227 St John's (Minnesota) Loss 10-11 904.82 Mar 2nd Midwest Throwdown 2024
257 Wisconsin-B Loss 9-10 792.85 Mar 3rd Midwest Throwdown 2024
320 Washington University-B Loss 7-8 526.34 Mar 3rd Midwest Throwdown 2024
373 Northwestern-B Win 7-5 660.56 Mar 3rd Midwest Throwdown 2024
351 Bradley Win 12-8 948.58 Mar 23rd Meltdown mini tournament
264 Wheaton (Illinois) Loss 6-13 291.59 Mar 23rd Meltdown mini tournament
333 North Park Win 12-10 820.62 Mar 23rd Meltdown mini tournament
- Wisconsin-Oshkosh** Win 13-5 580.79 Ignored Mar 23rd Meltdown mini tournament
222 Ball State Loss 5-10 469.77 Mar 30th Illinois Invite 2024
369 Illinois-B Win 6-4 725.93 Mar 30th Illinois Invite 2024
323 Purdue-B Win 8-5 1084.24 Mar 30th Illinois Invite 2024
282 Toledo Win 11-2 1418.55 Mar 30th Illinois Invite 2024
264 Wheaton (Illinois) Loss 7-9 612.25 Mar 30th Illinois Invite 2024
222 Ball State Loss 7-14 460.78 Mar 31st Illinois Invite 2024
282 Toledo Win 11-10 943.55 Mar 31st Illinois Invite 2024
351 Bradley Win 15-13 721.61 Apr 13th Illinois D III Mens Conferences 2024
264 Wheaton (Illinois) Loss 8-15 326.78 Apr 13th Illinois D III Mens Conferences 2024
232 Butler Loss 9-12 664.85 Apr 27th Great Lakes D III College Mens Regionals 2024
151 Grace Loss 6-15 711.63 Apr 27th Great Lakes D III College Mens Regionals 2024
220 Hillsdale Loss 7-12 529.79 Apr 27th Great Lakes D III College Mens Regionals 2024
110 Davenport** Loss 3-15 867.57 Ignored Apr 28th Great Lakes D III College Mens Regionals 2024
333 North Park Win 15-9 1097.98 Apr 28th Great Lakes D III College Mens 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)