#129 Michigan Tech (14-10)

avg: 1382.05  •  sd: 60.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
206 Embry-Riddle Win 13-10 1427.04 Mar 2nd FCS D III Tune Up 2024
68 Franciscan Loss 10-13 1332.34 Mar 2nd FCS D III Tune Up 2024
168 Kenyon Win 13-6 1852.36 Mar 2nd FCS D III Tune Up 2024
179 North Carolina-Asheville Win 13-7 1757.06 Mar 2nd FCS D III Tune Up 2024
114 Davidson Loss 9-13 1018.71 Mar 3rd FCS D III Tune Up 2024
173 Xavier Win 13-8 1729.59 Mar 3rd FCS D III Tune Up 2024
81 Lewis & Clark Loss 10-13 1259.34 Mar 3rd FCS D III Tune Up 2024
69 Central Florida Win 10-8 1901.2 Mar 30th Huck Finn 2024
61 Chicago Loss 10-11 1569.33 Mar 30th Huck Finn 2024
194 Ohio Win 13-3 1742.62 Mar 30th Huck Finn 2024
106 Northwestern Loss 9-12 1137.54 Mar 30th Huck Finn 2024
53 Colorado State Loss 7-13 1198.52 Mar 31st Huck Finn 2024
82 Mississippi State Loss 11-12 1457.9 Mar 31st Huck Finn 2024
105 Wisconsin-Milwaukee Win 10-9 1609.87 Mar 31st Huck Finn 2024
62 Purdue Loss 5-13 1089.75 Mar 31st Huck Finn 2024
237 Carthage Win 12-8 1430.81 Apr 13th Lake Superior D III Mens Conferences 2024
362 Concordia-Wisconsin Win 13-8 934.28 Apr 13th Lake Superior D III Mens Conferences 2024
270 Wisconsin-Platteville Win 13-10 1188.41 Apr 13th Lake Superior D III Mens Conferences 2024
386 Milwaukee Engineering** Win 13-3 847.21 Ignored Apr 13th Lake Superior D III Mens Conferences 2024
193 Grinnell Win 12-9 1500 Apr 27th North Central D III College Mens Regionals 2024
304 Luther College Win 15-7 1294.97 Apr 27th North Central D III College Mens Regionals 2024
278 St Thomas Win 15-4 1441.12 Apr 27th North Central D III College Mens Regionals 2024
78 Carleton College-CHOP Loss 13-15 1390.05 Apr 28th North Central D III College Mens Regionals 2024
237 Carthage Loss 13-14 864.65 Apr 28th North Central 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)