#105 Michigan Tech (7-4)

avg: 959.82  •  sd: 54.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
153 Loyola-Chicago Win 7-6 710.59 Mar 19th Meltdown College
68 Winona State Loss 7-8 1141.92 Mar 19th Meltdown College
174 Wheaton (Illinois) Win 5-2 994.94 Mar 19th Meltdown College
- Missouri Win 9-6 1278.6 Mar 19th Meltdown College
- St Benedict Win 9-4 917.2 Mar 25th Old Capitol Open
142 Macalester Win 10-7 1063.12 Mar 25th Old Capitol Open
188 Wisconsin-B Win 10-7 627.44 Mar 25th Old Capitol Open
27 Minnesota** Loss 3-12 1085.25 Ignored Mar 25th Old Capitol Open
104 Iowa Loss 6-7 841.86 Mar 26th Old Capitol Open
72 Iowa State Loss 7-9 951.15 Mar 26th Old Capitol Open
153 Loyola-Chicago Win 10-6 1081.75 Mar 26th Old Capitol Open
**Blowout Eligible


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
  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
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)