#115 Michigan State (7-4)

avg: 1310.55  •  sd: 85.18  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
297 Michigan State-B** Win 11-3 1084.49 Ignored Mar 19th Meltdown College
212 Grand Valley Win 11-5 1490.63 Mar 19th Meltdown College
236 Eastern Michigan Win 13-4 1366.83 Mar 19th Meltdown College
198 Western Michigan Win 10-7 1337.82 Mar 19th Meltdown College
64 St. Olaf Loss 3-10 967.94 Apr 1st Huck Finn1
92 Missouri S&T Loss 4-6 1064.16 Apr 1st Huck Finn1
112 Illinois Loss 8-9 1190.95 Apr 1st Huck Finn1
98 Kentucky Loss 6-11 870.06 Apr 1st Huck Finn1
274 DePaul** Win 14-6 1212.64 Ignored Apr 2nd Huck Finn1
207 Illinois State Win 12-5 1510.77 Apr 2nd Huck Finn1
116 John Brown Win 15-5 1905.93 Apr 2nd Huck Finn1
**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)