#140 Minnesota-B (11-5)

avg: 1078.04  •  sd: 118.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
78 Carleton College-CHOP Win 12-11 1473.94 Feb 10th Ugly Dome 2024
124 Macalester Loss 10-12 908.46 Feb 10th Ugly Dome 2024
49 St Olaf Loss 10-12 1265.04 Feb 10th Ugly Dome 2024
95 Wisconsin-Eau Claire Win 13-5 1850.03 Feb 10th Ugly Dome 2024
299 Minnesota-C** Win 13-4 957.54 Ignored Feb 12th Ugly Dome 2024
337 St Thomas Win 11-8 482.3 Mar 21st Minneapolis Makeup
- Bethel** Win 11-3 600 Ignored Mar 23rd Minneapolis Makeup
124 Macalester Loss 6-7 1021.58 Mar 23rd Minneapolis Makeup
299 Minnesota-C** Win 11-1 957.54 Ignored Mar 28th Minneapolis Makeup
69 Colorado Mines Loss 8-11 1003.79 Mar 30th Old Capitol Open 2024
383 Wisconsin-Milwaukee-B** Win 13-0 -142.26 Ignored Mar 30th Old Capitol Open 2024
370 Northwestern-B** Win 13-3 394.7 Ignored Mar 30th Old Capitol Open 2024
145 Southern Illinois-Edwardsville Loss 10-13 732.27 Mar 30th Old Capitol Open 2024
265 Eastern Michigan Win 13-4 1133.93 Mar 31st Old Capitol Open 2024
229 Northern Iowa Win 12-2 1315.79 Mar 31st Old Capitol Open 2024
180 Wisconsin-La Crosse Win 8-7 1023.16 Mar 31st Old Capitol Open 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)