#193 Miami (Florida) (10-6)

avg: 1088.75  •  sd: 66.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 Ave Maria** Loss 4-13 1215.95 Ignored Mar 1st Florida Warm Up 2025 Weekend 2
229 Ave Maria-B Loss 10-11 832.47 Mar 1st Florida Warm Up 2025 Weekend 2
333 Florida-B Win 13-3 1166.66 Mar 1st Florida Warm Up 2025 Weekend 2
333 Florida-B Win 13-3 1166.66 Mar 2nd Florida Warm Up 2025 Weekend 2
397 South Florida-B** Win 13-4 702.31 Ignored Mar 2nd Florida Warm Up 2025 Weekend 2
339 Alabama-B Win 13-9 952.36 Mar 22nd 2025 Annual Magic City Invite
397 South Florida-B** Win 13-2 702.31 Ignored Mar 22nd 2025 Annual Magic City Invite
324 Samford Win 13-6 1197.9 Mar 22nd 2025 Annual Magic City Invite
319 Mississippi Win 13-3 1212.05 Mar 22nd 2025 Annual Magic City Invite
171 Alabama-Birmingham Loss 9-14 718.68 Mar 23rd 2025 Annual Magic City Invite
319 Mississippi Win 15-6 1212.05 Mar 23rd 2025 Annual Magic City Invite
185 Union (Tennessee) Win 15-8 1694.01 Mar 23rd 2025 Annual Magic City Invite
126 Central Florida Loss 10-14 947.78 Apr 12th Florida D I Mens Conferences 2025
286 North Florida Win 14-7 1332.34 Apr 12th Florida D I Mens Conferences 2025
132 Florida State Loss 10-15 873.93 Apr 13th Florida D I Mens Conferences 2025
144 South Florida Loss 10-14 889.83 Apr 13th Florida D I Mens Conferences 2025
**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)