#151 Grace (17-5)

avg: 1311.63  •  sd: 56.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
110 Davenport Loss 4-13 867.57 Mar 16th Grand Rapids College Invite
340 DePaul Win 11-5 1155.29 Mar 16th Grand Rapids College Invite
257 Wisconsin-B Win 13-5 1517.85 Mar 16th Grand Rapids College Invite
145 Southern Illinois-Edwardsville Win 8-7 1456.75 Mar 17th Grand Rapids College Invite
135 Grand Valley Loss 2-13 771.15 Mar 17th Grand Rapids College Invite
37 Michigan Loss 10-12 1647.62 Mar 17th Grand Rapids College Invite
222 Ball State Win 13-7 1601.19 Mar 23rd Butler Spring Fling
363 Indiana-B** Win 13-1 1038.08 Ignored Mar 23rd Butler Spring Fling
285 Penn State-Behrend Win 13-6 1400.45 Mar 23rd Butler Spring Fling
232 Butler Win 13-7 1567.75 Mar 24th Butler Spring Fling
301 Rose-Hulman Win 12-6 1293.62 Mar 24th Butler Spring Fling
280 Western Michigan Win 10-5 1402.54 Mar 24th Butler Spring Fling
301 Rose-Hulman Win 15-5 1314.31 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
273 Valparaiso Win 15-5 1453.78 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
220 Hillsdale Win 15-7 1650.3 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
349 Kalamazoo Win 15-7 1124.43 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
110 Davenport Loss 8-15 902.76 Apr 14th Eastern Great Lakes D III Mens Conferences 2024
301 Rose-Hulman Win 15-7 1314.31 Apr 14th Eastern Great Lakes D III Mens Conferences 2024
232 Butler Win 12-10 1248.34 Apr 27th Great Lakes D III College Mens Regionals 2024
294 Knox Win 15-6 1337.81 Apr 27th Great Lakes D III College Mens Regionals 2024
220 Hillsdale Win 14-7 1633.19 Apr 27th Great Lakes D III College Mens Regionals 2024
220 Hillsdale Loss 12-13 925.3 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)