#148 Michigan-B (15-6)

avg: 1181.95  •  sd: 63.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
226 Miami (Ohio) Win 13-7 1473.98 Mar 9th Boogienights 2019
172 Miami-Upper Loss 11-13 845.42 Mar 9th Boogienights 2019
204 SUNY-Buffalo Win 13-8 1467.96 Mar 9th Boogienights 2019
172 Miami-Upper Win 9-7 1353.6 Mar 10th Boogienights 2019
368 Cleveland State** Win 15-5 987.5 Ignored Mar 10th Boogienights 2019
68 Cincinnati Loss 9-15 999.89 Mar 10th Boogienights 2019
380 Case Western Reserve-B Win 13-6 930.75 Mar 23rd CWRUL Memorial 2019
320 Ohio State-B Win 12-7 1113.05 Mar 23rd CWRUL Memorial 2019
348 Western Michigan** Win 13-0 1087.57 Ignored Mar 23rd CWRUL Memorial 2019
174 Cedarville Win 13-10 1395.6 Mar 23rd CWRUL Memorial 2019
171 RIT Win 15-14 1206.65 Mar 24th CWRUL Memorial 2019
64 Ohio Loss 6-15 939.4 Mar 24th CWRUL Memorial 2019
132 Kentucky Win 11-8 1616.77 Mar 24th CWRUL Memorial 2019
154 Syracuse Win 15-7 1750.57 Mar 24th CWRUL Memorial 2019
237 Loyola-Chicago Win 12-11 1024.86 Mar 30th Illinois Invite 8
224 DePaul Win 10-9 1041.56 Mar 30th Illinois Invite 8
97 Grand Valley State Loss 6-8 1063.31 Mar 31st Illinois Invite 8
235 Northern Iowa Win 8-6 1206.17 Mar 31st Illinois Invite 8
205 Wisconsin-B Win 11-4 1570.59 Mar 31st Illinois Invite 8
112 Wisconsin-Whitewater Loss 3-11 706.21 Mar 31st Illinois Invite 8
124 Wisconsin-Milwaukee Loss 3-7 678.72 Mar 31st Illinois Invite 8
**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)