#53 Indiana (19-2)

avg: 1626.62  •  sd: 96.13  •  top 16/20: 0.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
81 Georgia Tech Win 12-9 1792.68 Feb 16th Easterns Qualifier 2019
197 George Mason** Win 13-5 1601.39 Ignored Feb 16th Easterns Qualifier 2019
64 Ohio Win 13-7 2096.93 Feb 16th Easterns Qualifier 2019
119 Clemson Win 9-5 1812.61 Feb 16th Easterns Qualifier 2019
44 Virginia Loss 13-14 1546.41 Feb 17th Easterns Qualifier 2019
102 Georgetown Loss 11-15 970.02 Feb 17th Easterns Qualifier 2019
81 Georgia Tech Win 11-10 1572.32 Feb 17th Easterns Qualifier 2019
87 Case Western Reserve Win 13-11 1651.4 Mar 23rd CWRUL Memorial 2019
171 RIT Win 13-7 1639.18 Mar 23rd CWRUL Memorial 2019
132 Kentucky Win 11-5 1851.16 Mar 23rd CWRUL Memorial 2019
247 Xavier Win 15-7 1474.74 Mar 24th CWRUL Memorial 2019
154 Syracuse Win 13-9 1569.14 Mar 24th CWRUL Memorial 2019
87 Case Western Reserve Win 15-9 1938.04 Mar 24th CWRUL Memorial 2019
64 Ohio Win 15-8 2104.21 Mar 24th CWRUL Memorial 2019
313 Drake** Win 12-3 1213.8 Ignored Mar 30th Old Capitol Open 2019
419 Northwestern-St. Paul** Win 13-2 677.48 Ignored Mar 30th Old Capitol Open 2019
321 Carleton Hot Karls** Win 13-2 1189.49 Ignored Mar 30th Old Capitol Open 2019
433 Chicago-B** Win 15-1 421.23 Ignored Mar 30th Old Capitol Open 2019
264 St John's Win 15-7 1408.62 Mar 31st Old Capitol Open 2019
194 Kansas State Win 14-12 1228.57 Mar 31st Old Capitol Open 2019
105 Iowa Win 13-12 1462.36 Mar 31st Old Capitol Open 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)