#254 Michigan-B (8-8)

avg: 928.81  •  sd: 72.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
86 Cedarville** Loss 2-13 955.52 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
84 Elon** Loss 5-13 976.44 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
214 North Carolina-B Loss 9-10 948.8 Feb 17th Commonwealth Cup Weekend 1 2024
212 West Virginia Loss 4-9 479.5 Feb 18th Commonwealth Cup Weekend 1 2024
395 Chicago-B** Win 11-4 743.1 Ignored Apr 13th Great Lakes Dev Mens Conferences 2024
369 Illinois-B Win 10-4 960.32 Apr 13th Great Lakes Dev Mens Conferences 2024
363 Indiana-B Win 9-8 563.08 Apr 13th Great Lakes Dev Mens Conferences 2024
389 Purdue-C Win 9-7 475.78 Apr 13th Great Lakes Dev Mens Conferences 2024
357 Michigan State-B Win 15-6 1074.64 Apr 14th Great Lakes Dev Mens Conferences 2024
323 Purdue-B Win 13-8 1126.8 Apr 14th Great Lakes Dev Mens Conferences 2024
222 Ball State Loss 10-13 715.52 Apr 27th Great Lakes D I College Mens Regionals 2024
83 Indiana Loss 7-14 998.43 Apr 27th Great Lakes D I College Mens Regionals 2024
37 Michigan** Loss 3-15 1285.75 Ignored Apr 27th Great Lakes D I College Mens Regionals 2024
222 Ball State Win 11-5 1643.66 Apr 28th Great Lakes D I College Mens Regionals 2024
118 Kentucky Loss 7-15 815.92 Apr 28th Great Lakes D I College Mens Regionals 2024
357 Michigan State-B Win 15-6 1074.64 Apr 28th Great Lakes D I 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)