#207 Miami (3-8)

avg: 802.34  •  sd: 94.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
134 Tennessee Loss 4-12 676.04 Jan 13th Florida Winter Classic 2018
54 Florida State** Loss 0-15 1256.06 Ignored Jan 13th Florida Winter Classic 2018
89 Iowa** Loss 2-14 970.48 Ignored Jan 13th Florida Winter Classic 2018
174 Florida-B Loss 4-9 437.55 Jan 14th Florida Winter Classic 2018
180 South Florida Loss 3-8 400.57 Jan 14th Florida Winter Classic 2018
205 Trinity Win 8-6 1119.47 Mar 24th Womens Centex 2018
95 Texas State** Loss 4-13 933.28 Ignored Mar 24th Womens Centex 2018
144 Texas-Dallas Loss 3-13 627.04 Mar 24th Womens Centex 2018
165 Baylor Loss 6-12 502.31 Mar 24th Womens Centex 2018
247 North Texas Win 10-7 862.06 Mar 25th Womens Centex 2018
208 Rice Win 13-7 1353.84 Mar 25th Womens Centex 2018
**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)