#122 Michigan Tech (10-3)

avg: 1293.2  •  sd: 78.91  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
281 Ball State** Win 13-4 1178.31 Ignored Mar 25th Old Capitol Open
226 Wisconsin-La Crosse Win 13-5 1404.02 Mar 25th Old Capitol Open
341 Iowa State-B** Win 13-4 770.46 Ignored Mar 25th Old Capitol Open
207 Illinois State Win 12-10 1148.9 Mar 25th Old Capitol Open
145 Carthage Loss 10-13 843.88 Mar 26th Old Capitol Open
188 Macalester Win 10-7 1385.51 Mar 26th Old Capitol Open
93 Iowa Loss 7-12 905.71 Mar 26th Old Capitol Open
297 Michigan State-B** Win 13-2 1084.49 Ignored Apr 1st King of the Hill
214 Wheaton (Illinois) Win 13-5 1485.37 Apr 1st King of the Hill
103 Truman State Loss 12-13 1228.27 Apr 1st King of the Hill
282 Valparaiso** Win 15-5 1174.73 Ignored Apr 2nd King of the Hill
231 Hillsdale Win 15-3 1391.33 Apr 2nd King of the Hill
103 Truman State Win 15-10 1806.88 Apr 2nd King of the Hill
**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)