#94 Saint Louis (12-6)

avg: 1424.8  •  sd: 64.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 Colorado College Loss 8-11 1369.74 Mar 4th Midwest Throwdown 2023
334 Northwestern-B** Win 13-0 837.28 Ignored Mar 4th Midwest Throwdown 2023
279 Wisconsin-Platteville** Win 13-3 1185.54 Ignored Mar 4th Midwest Throwdown 2023
54 Northwestern Loss 8-11 1250.58 Mar 5th Midwest Throwdown 2023
68 Wisconsin-Milwaukee Loss 10-11 1424.93 Mar 5th Midwest Throwdown 2023
183 Minnesota-B Win 12-7 1531.47 Mar 25th Old Capitol Open
93 Iowa Loss 6-13 826.29 Mar 25th Old Capitol Open
64 St. Olaf Loss 7-10 1178.33 Mar 25th Old Capitol Open
188 Macalester Win 8-6 1296.45 Mar 26th Old Capitol Open
93 Iowa Win 10-9 1551.29 Mar 26th Old Capitol Open
199 Nebraska Win 13-8 1441.28 Mar 26th Old Capitol Open
163 Boston University Win 6-2 1701.13 Apr 1st Huck Finn1
151 Arizona State Win 7-3 1746.59 Apr 1st Huck Finn1
274 DePaul Win 5-2 1212.69 Apr 1st Huck Finn1
131 Georgia State Win 8-4 1807.39 Apr 1st Huck Finn1
35 Missouri Loss 9-14 1312.96 Apr 2nd Huck Finn1
98 Kentucky Win 11-10 1541.81 Apr 2nd Huck Finn1
68 Wisconsin-Milwaukee Win 9-8 1674.93 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)