#124 Indiana (10-10)

avg: 1226.26  •  sd: 75.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
113 Lehigh Win 13-8 1780.24 Feb 17th Easterns Qualifier 2018
224 Georgia Southern Win 13-5 1461.24 Feb 17th Easterns Qualifier 2018
34 William & Mary Win 13-9 2066.77 Feb 17th Easterns Qualifier 2018
102 Richmond Win 12-11 1451.88 Feb 17th Easterns Qualifier 2018
64 North Carolina-Charlotte Loss 9-13 1043.94 Feb 17th Easterns Qualifier 2018
48 Dartmouth Win 15-14 1690.43 Feb 18th Easterns Qualifier 2018
33 Maryland Loss 6-15 1084.28 Feb 18th Easterns Qualifier 2018
34 William & Mary Loss 9-11 1398.99 Feb 18th Easterns Qualifier 2018
97 Alabama Loss 8-15 783.12 Mar 10th Tally Classic XIII
224 Georgia Southern Win 13-10 1189.38 Mar 10th Tally Classic XIII
88 Alabama-Huntsville Loss 9-13 969.74 Mar 10th Tally Classic XIII
168 South Florida Win 12-11 1188.99 Mar 10th Tally Classic XIII
16 North Carolina-Wilmington Loss 8-13 1388.35 Mar 10th Tally Classic XIII
120 Mississippi State Loss 11-15 880.13 Mar 11th Tally Classic XIII
136 Ohio Win 13-12 1299.77 Mar 24th Indy Invite College Men 2018
259 Northern Illinois Win 13-4 1352.4 Mar 24th Indy Invite College Men 2018
73 Michigan State Loss 9-11 1170.34 Mar 24th Indy Invite College Men 2018
181 Ball State Win 7-6 1135.76 Mar 25th Indy Invite College Men 2018
95 Purdue Loss 4-10 752.93 Mar 25th Indy Invite College Men 2018
122 Tennessee Loss 7-11 788.65 Mar 25th Indy Invite College Men 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)