#87 Michigan Tech (12-7)

avg: 1133.92  •  sd: 63.49  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
96 Arkansas Win 9-8 1216.69 Mar 1st Midwest Throwdown 2025
66 Illinois Win 7-5 1636.67 Mar 1st Midwest Throwdown 2025
144 Knox Win 12-2 1325.69 Mar 1st Midwest Throwdown 2025
209 Vanderbilt** Win 13-1 880.82 Ignored Mar 1st Midwest Throwdown 2025
92 Iowa State Loss 8-11 746.13 Mar 2nd Midwest Throwdown 2025
28 Missouri Loss 5-11 1131.99 Mar 2nd Midwest Throwdown 2025
117 Northwestern Loss 4-10 339.88 Mar 2nd Midwest Throwdown 2025
131 Grand Valley Win 9-0 1414.79 Mar 15th Davenport Spring Skirmish
110 Michigan State Win 7-6 1131.03 Mar 15th Davenport Spring Skirmish
199 Oberlin** Win 6-2 1040.83 Ignored Mar 15th Davenport Spring Skirmish
58 Davenport Loss 6-8 1102.63 Mar 16th Davenport Spring Skirmish
58 Davenport Loss 4-7 906.97 Mar 16th Davenport Spring Skirmish
110 Michigan State Win 6-5 1131.03 Mar 16th Davenport Spring Skirmish
46 Carleton College-Eclipse Loss 3-15 938.86 Apr 12th North Central D III Womens Conferences 2025
173 Grinnell Win 15-7 1192.63 Apr 12th North Central D III Womens Conferences 2025
192 St Olaf-B** Win 15-0 1070.42 Ignored Apr 12th North Central D III Womens Conferences 2025
46 Carleton College-Eclipse Loss 13-15 1324.68 Apr 13th North Central D III Womens Conferences 2025
173 Grinnell Win 11-7 1059.52 Apr 13th North Central D III Womens Conferences 2025
128 Winona State Win 14-7 1427.49 Apr 13th North Central D III Womens Conferences 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)