#338 Rose-Hulman (5-19)

avg: 542.77  •  sd: 71.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
268 Michigan State-B Loss 6-8 507.62 Mar 15th Grand Rapids Invite 2025
402 Wisconsin-Milwaukee-B Win 14-6 617.81 Mar 15th Grand Rapids Invite 2025
204 Winona State Loss 8-15 481.11 Mar 15th Grand Rapids Invite 2025
392 Wisconsin-C Win 15-7 740.91 Mar 16th Grand Rapids Invite 2025
301 Luther Loss 8-11 315.01 Mar 16th Grand Rapids Invite 2025
301 Luther Loss 4-8 115.81 Mar 16th Grand Rapids Invite 2025
176 Northern Iowa Loss 5-11 570.83 Mar 22nd Meltdown 2025
253 St Thomas Loss 6-11 316.3 Mar 22nd Meltdown 2025
208 Wisconsin-Whitewater Loss 4-12 434.01 Mar 22nd Meltdown 2025
204 Winona State Loss 6-11 499.23 Mar 22nd Meltdown 2025
384 Carthage Win 9-2 819.74 Mar 23rd Meltdown 2025
273 Minnesota State-Mankato Loss 6-9 385.21 Mar 23rd Meltdown 2025
15 Davenport** Loss 4-15 1459.59 Ignored Apr 12th Eastern Great Lakes D III Mens Conferences 2025
242 Grace Loss 4-15 309.7 Apr 12th Eastern Great Lakes D III Mens Conferences 2025
240 Kalamazoo Loss 8-12 476.05 Apr 12th Eastern Great Lakes D III Mens Conferences 2025
242 Grace Win 10-9 1034.7 Apr 13th Eastern Great Lakes D III Mens Conferences 2025
242 Grace Loss 8-11 544.09 Apr 13th Eastern Great Lakes D III Mens Conferences 2025
240 Kalamazoo Loss 6-12 337.9 Apr 13th Eastern Great Lakes D III Mens Conferences 2025
15 Davenport** Loss 5-15 1459.59 Ignored Apr 26th Great Lakes D III Mens Regionals 2025
242 Grace Loss 7-14 326.81 Apr 26th Great Lakes D III Mens Regionals 2025
114 Hillsdale** Loss 4-15 789.29 Ignored Apr 26th Great Lakes D III Mens Regionals 2025
297 Knox Win 14-10 1109.64 Apr 26th Great Lakes D III Mens Regionals 2025
129 Asbury Loss 8-15 775.66 Apr 27th Great Lakes D III Mens Regionals 2025
240 Kalamazoo Loss 8-13 421.05 Apr 27th Great Lakes D III Mens Regionals 2025
**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)