#145 Dayton (12-7)

avg: 1189.68  •  sd: 67.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Johns Hopkins Loss 11-13 1502.33 Feb 16th Easterns Qualifier 2019
101 Connecticut Loss 9-12 1010.87 Feb 16th Easterns Qualifier 2019
44 Virginia Loss 6-13 1071.41 Feb 16th Easterns Qualifier 2019
88 Tennessee-Chattanooga Loss 4-12 819.19 Feb 16th Easterns Qualifier 2019
155 Elon Loss 14-15 1024.58 Feb 17th Easterns Qualifier 2019
165 Georgia Southern Win 14-12 1312.86 Feb 17th Easterns Qualifier 2019
197 George Mason Win 15-11 1382.56 Feb 17th Easterns Qualifier 2019
345 American** Win 13-4 1101.81 Ignored Mar 2nd Huckin in the Hills VI
380 Case Western Reserve-B** Win 13-3 930.75 Ignored Mar 2nd Huckin in the Hills VI
432 SUNY-Buffalo-B** Win 13-0 503.29 Ignored Mar 2nd Huckin in the Hills VI
356 West Virginia** Win 13-5 1048.77 Ignored Mar 2nd Huckin in the Hills VI
269 Ball State Win 13-6 1385.46 Mar 23rd CWRUL Memorial 2019
358 Trine** Win 13-5 1047.9 Ignored Mar 23rd CWRUL Memorial 2019
303 Ohio Northern Win 13-4 1252.2 Mar 23rd CWRUL Memorial 2019
247 Xavier Win 13-7 1432.28 Mar 23rd CWRUL Memorial 2019
210 Rochester Win 14-8 1489.33 Mar 24th CWRUL Memorial 2019
132 Kentucky Loss 11-12 1126.16 Mar 24th CWRUL Memorial 2019
174 Cedarville Win 10-9 1192.46 Mar 24th CWRUL Memorial 2019
183 Oberlin Loss 7-11 575.06 Mar 24th CWRUL Memorial 2019
**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)