#111 Michigan State (14-6)

avg: 1058.24  •  sd: 65.49  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
249 Alabama-Birmingham** Win 10-3 752.77 Ignored Feb 16th First Annual Jillz Jamboree
210 Cedarville Win 9-8 609.08 Feb 16th First Annual Jillz Jamboree
151 Kentucky Win 8-7 987.08 Feb 16th First Annual Jillz Jamboree
151 Kentucky Win 13-10 1190.22 Feb 17th First Annual Jillz Jamboree
140 Cincinnati Win 11-9 1162.05 Feb 17th First Annual Jillz Jamboree
98 Mississippi State Win 15-4 1728.15 Feb 17th First Annual Jillz Jamboree
85 Dayton Win 9-8 1368.31 Mar 23rd CWRUL Memorial 2019
94 Carnegie Mellon Loss 3-9 584.72 Mar 23rd CWRUL Memorial 2019
75 Purdue Loss 6-8 987.59 Mar 23rd CWRUL Memorial 2019
160 DePaul Win 12-11 939.05 Mar 23rd CWRUL Memorial 2019
81 Ohio Loss 7-9 988.97 Mar 24th CWRUL Memorial 2019
79 Ball State Win 9-8 1398.29 Mar 24th CWRUL Memorial 2019
58 Penn State Loss 8-12 1009.89 Mar 24th CWRUL Memorial 2019
172 Northern Iowa Loss 8-9 599.75 Mar 30th Old Capitol Open 2019
89 Iowa State Loss 8-9 1098.1 Mar 30th Old Capitol Open 2019
193 Drake Win 8-7 698.3 Mar 30th Old Capitol Open 2019
149 Luther Win 8-7 994.27 Mar 30th Old Capitol Open 2019
177 Wisconsin-La Crosse Win 10-6 1165.16 Mar 31st Old Capitol Open 2019
175 Kansas Win 11-7 1184.14 Mar 31st Old Capitol Open 2019
162 Nebraska Win 14-7 1379.63 Mar 31st Old Capitol Open 2019
**Blowout Eligible

FAQ

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)