#96 Dayton (9-4)

avg: 1233.66  •  sd: 59.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
137 Ball State Loss 9-10 923.2 Feb 22nd Music City Tune Up 2020
70 Middle Tennessee State Loss 8-9 1252 Feb 22nd Music City Tune Up 2020
243 Toledo Win 13-4 1270.36 Feb 22nd Music City Tune Up 2020
212 Union (Tennessee) Win 11-7 1270.92 Feb 22nd Music City Tune Up 2020
137 Ball State Win 13-4 1648.2 Feb 23rd Music City Tune Up 2020
80 Boston College Loss 8-12 879.42 Feb 23rd Music City Tune Up 2020
101 Vanderbilt Loss 11-13 980.84 Feb 23rd Music City Tune Up 2020
247 Towson Win 10-4 1255.11 Mar 7th Mash Up 2020
218 RIT Win 13-5 1354.25 Mar 7th Mash Up 2020
151 George Washington Win 11-6 1553.29 Mar 7th Mash Up 2020
239 Rhode Island Win 13-9 1111.6 Mar 8th Mash Up 2020
147 Wesleyan Win 13-11 1237.06 Mar 8th Mash Up 2020
151 George Washington Win 9-7 1285.93 Mar 8th Mash Up 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)