#106 Michigan Tech (9-6)

avg: 1188.38  •  sd: 93.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
167 Wheaton (Illinois) Win 9-4 1386.41 Mar 23rd Meltdown mini tournament
167 Wheaton (Illinois) Win 9-5 1315.47 Mar 23rd Meltdown mini tournament
96 Iowa State Loss 6-7 1128.51 Mar 30th Old Capitol Open 2024
83 Kansas Loss 6-8 1042.51 Mar 30th Old Capitol Open 2024
179 Minnesota-B Win 9-3 1227.62 Mar 30th Old Capitol Open 2024
59 Purdue Loss 4-8 951.61 Mar 30th Old Capitol Open 2024
99 Chicago Win 6-2 1819.77 Mar 31st Old Capitol Open 2024
102 Iowa Win 8-5 1665.87 Mar 31st Old Capitol Open 2024
179 Minnesota-B Win 13-1 1227.62 Mar 31st Old Capitol Open 2024
35 St Olaf Loss 6-14 1178.48 Apr 13th North Central D III Womens Conferences 2024
227 Carleton College-C** Win 15-5 782.75 Ignored Apr 13th North Central D III Womens Conferences 2024
128 Grinnell Loss 6-7 914.8 Apr 13th North Central D III Womens Conferences 2024
242 St Thomas** Win 15-1 430.15 Ignored Apr 14th North Central D III Womens Conferences 2024
227 Carleton College-C** Win 10-3 782.75 Ignored Apr 14th North Central D III Womens Conferences 2024
128 Grinnell Loss 11-14 726.47 Apr 14th North Central D III Womens 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)