#84 Missouri S&T (16-4)

avg: 1307.9  •  sd: 77.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
289 Olivet Nazarene Win 11-8 777.71 Feb 22nd Music City Tune Up 2020
212 Union (Tennessee) Win 13-5 1404.02 Feb 22nd Music City Tune Up 2020
281 Butler** Win 13-4 1047.86 Ignored Feb 22nd Music City Tune Up 2020
80 Boston College Win 12-11 1445.57 Feb 23rd Music City Tune Up 2020
70 Middle Tennessee State Loss 11-12 1252 Feb 23rd Music City Tune Up 2020
108 Chicago Win 15-10 1636.07 Feb 23rd Music City Tune Up 2020
127 Brandeis Win 9-8 1246.85 Feb 29th FCS D III Tune Up 2020
116 Franciscan Win 13-10 1481.35 Feb 29th FCS D III Tune Up 2020
322 High Point** Win 13-1 788.87 Ignored Feb 29th FCS D III Tune Up 2020
97 Richmond Loss 12-13 1107.71 Feb 29th FCS D III Tune Up 2020
155 North Carolina-Asheville Win 13-9 1415.72 Mar 1st FCS D III Tune Up 2020
170 Shippensburg Loss 11-13 731.5 Mar 1st FCS D III Tune Up 2020
135 Messiah Win 11-10 1191.07 Mar 1st FCS D III Tune Up 2020
235 St. Thomas Win 10-7 1103.59 Mar 7th Midwest Throwdown 2020
221 Michigan-B Win 13-4 1346.86 Mar 7th Midwest Throwdown 2020
350 Coe** Win 13-1 494.43 Ignored Mar 7th Midwest Throwdown 2020
67 Wisconsin-Milwaukee Win 10-9 1516.64 Mar 7th Midwest Throwdown 2020
33 Northwestern Loss 5-9 1136.33 Mar 8th Midwest Throwdown 2020
89 Carleton College-GoP Win 9-4 1878.33 Mar 8th Midwest Throwdown 2020
133 Missouri Win 8-4 1657.48 Mar 8th Midwest Throwdown 2020
**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)