#280 Western Michigan (6-12)

avg: 828.64  •  sd: 65.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
362 Concordia-Wisconsin Win 13-5 1038.12 Mar 16th Grand Rapids College Invite
37 Michigan** Loss 2-13 1285.75 Ignored Mar 16th Grand Rapids College Invite
357 Michigan State-B Win 13-7 1032.17 Mar 16th Grand Rapids College Invite
340 DePaul Win 7-3 1155.29 Mar 17th Grand Rapids College Invite
135 Grand Valley Loss 4-13 771.15 Mar 17th Grand Rapids College Invite
123 Oberlin Loss 5-12 796.72 Mar 17th Grand Rapids College Invite
357 Michigan State-B Win 12-6 1053.95 Mar 22nd Butler Spring Fling
220 Hillsdale Loss 6-9 631.74 Mar 23rd Butler Spring Fling
236 Loyola-Chicago Loss 1-13 390.9 Mar 23rd Butler Spring Fling
173 Xavier Loss 6-13 633.43 Mar 23rd Butler Spring Fling
151 Grace Loss 5-10 737.73 Mar 24th Butler Spring Fling
323 Purdue-B Win 11-6 1177.34 Mar 24th Butler Spring Fling
301 Rose-Hulman Win 11-7 1181.2 Mar 24th Butler Spring Fling
223 Eastern Michigan Loss 6-9 621.42 Apr 13th Michigan D I Mens Conferences 2024
135 Grand Valley Loss 0-13 771.15 Apr 13th Michigan D I Mens Conferences 2024
37 Michigan** Loss 5-13 1285.75 Ignored Apr 13th Michigan D I Mens Conferences 2024
49 Michigan State** Loss 2-6 1178.48 Ignored Apr 13th Michigan D I Mens Conferences 2024
223 Eastern Michigan Loss 9-13 621.42 Apr 14th Michigan D I Mens 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)