#64 Ohio (18-9)

avg: 1539.4  •  sd: 55.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
131 Chicago Win 12-10 1504.62 Feb 9th Queen City Tune Up 2019 Men
24 Auburn Win 12-10 2034.9 Feb 9th Queen City Tune Up 2019 Men
44 Virginia Win 13-10 1999.56 Feb 9th Queen City Tune Up 2019 Men
26 North Carolina-Wilmington Loss 6-10 1284.82 Feb 9th Queen City Tune Up 2019 Men
47 Maryland Loss 12-15 1355.84 Feb 10th Queen City Tune Up 2019 Men
9 Massachusetts Loss 3-15 1465.5 Feb 10th Queen City Tune Up 2019 Men
14 Ohio State Loss 8-15 1427.26 Feb 10th Queen City Tune Up 2019 Men
53 Indiana Loss 7-13 1069.09 Feb 16th Easterns Qualifier 2019
119 Clemson Win 13-7 1841.08 Feb 16th Easterns Qualifier 2019
197 George Mason Win 13-5 1601.39 Feb 16th Easterns Qualifier 2019
81 Georgia Tech Loss 8-12 1006.16 Feb 16th Easterns Qualifier 2019
139 Pennsylvania Win 13-10 1557.81 Feb 17th Easterns Qualifier 2019
126 New Hampshire Loss 13-14 1150.42 Feb 17th Easterns Qualifier 2019
88 Tennessee-Chattanooga Win 15-13 1633.37 Feb 17th Easterns Qualifier 2019
304 Rhode Island** Win 13-3 1244.91 Ignored Mar 9th Mash Up 2019
270 Delaware-B Win 13-6 1382.13 Mar 9th Mash Up 2019
319 North Carolina State -B** Win 13-5 1194.87 Ignored Mar 9th Mash Up 2019
267 SUNY-Fredonia** Win 13-3 1387.71 Ignored Mar 9th Mash Up 2019
126 New Hampshire Win 13-10 1603.56 Mar 10th Mash Up 2019
135 University of Pittsburgh-B Win 10-6 1739.2 Mar 10th Mash Up 2019
38 Purdue Loss 11-13 1478.2 Mar 23rd CWRUL Memorial 2019
160 Vanderbilt Win 13-9 1542.95 Mar 23rd CWRUL Memorial 2019
135 University of Pittsburgh-B Win 13-8 1739.2 Mar 23rd CWRUL Memorial 2019
53 Indiana Loss 8-15 1061.81 Mar 24th CWRUL Memorial 2019
210 Rochester Win 15-8 1518.1 Mar 24th CWRUL Memorial 2019
38 Purdue Win 13-10 2035.18 Mar 24th CWRUL Memorial 2019
148 Michigan-B Win 15-6 1781.95 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)