#143 Michigan Tech (7-13)

avg: 1062.91  •  sd: 43.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
70 Franciscan Loss 8-12 969.54 Mar 1st D III River City Showdown 2025
145 Kenyon Loss 7-9 779.77 Mar 1st D III River City Showdown 2025
89 North Carolina-Asheville Loss 10-13 973.98 Mar 1st D III River City Showdown 2025
213 Air Force Win 13-9 1165.43 Mar 2nd D III River City Showdown 2025
166 Brandeis Win 11-7 1439 Mar 2nd D III River City Showdown 2025
149 Davidson Loss 10-11 929.75 Mar 2nd D III River City Showdown 2025
11 Davenport Loss 6-13 1380.95 Mar 15th Grand Rapids Invite 2025
212 Eastern Michigan Win 10-6 1246.49 Mar 15th Grand Rapids Invite 2025
190 Toronto Win 10-7 1249.29 Mar 15th Grand Rapids Invite 2025
63 Notre Dame Loss 6-14 859.46 Mar 15th Grand Rapids Invite 2025
249 Cedarville Win 15-6 1212.8 Mar 16th Grand Rapids Invite 2025
123 Wisconsin-Milwaukee Loss 11-14 833.65 Mar 16th Grand Rapids Invite 2025
131 Pittsburgh-B Loss 9-10 996.3 Mar 16th Grand Rapids Invite 2025
59 Asbury Loss 8-13 998.08 Mar 29th Corny Classic College 2025
303 Ball State Win 12-6 944.1 Mar 29th Corny Classic College 2025
133 Lipscomb Loss 9-10 989.86 Mar 29th Corny Classic College 2025
164 Ohio Loss 8-9 851.87 Mar 29th Corny Classic College 2025
59 Asbury Loss 6-8 1193.74 Mar 30th Corny Classic College 2025
164 Ohio Win 10-7 1366.53 Mar 30th Corny Classic College 2025
158 Vanderbilt Loss 5-6 877.81 Mar 30th Corny Classic College 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)