#48 Kennesaw State (9-13)

avg: 1646.49  •  sd: 63.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
36 Alabama Loss 8-10 1460.47 Jan 26th T Town Throwdown
185 Alabama-Birmingham Win 13-8 1528.12 Jan 26th T Town Throwdown
106 Illinois State Loss 9-11 1078.13 Jan 26th T Town Throwdown
24 Auburn Loss 2-13 1196.78 Jan 26th T Town Throwdown
36 Alabama Win 14-12 1944.09 Jan 27th T Town Throwdown
27 LSU Loss 14-15 1652.74 Jan 27th T Town Throwdown
37 Illinois Loss 3-9 1120.39 Jan 27th T Town Throwdown
98 Kansas Win 12-10 1601.31 Feb 8th Florida Warm Up 2019
29 Texas-Dallas Loss 9-13 1353.34 Feb 8th Florida Warm Up 2019
49 Northwestern Win 13-8 2133.85 Feb 8th Florida Warm Up 2019
73 Temple Win 11-6 2027.57 Feb 9th Florida Warm Up 2019
20 Tufts Win 11-9 2113.35 Feb 9th Florida Warm Up 2019
28 Northeastern Loss 9-12 1430.47 Feb 9th Florida Warm Up 2019
43 Harvard Loss 11-12 1547.28 Feb 9th Florida Warm Up 2019
25 South Carolina Win 13-12 1911.69 Feb 10th Florida Warm Up 2019
7 Carleton College-CUT Loss 4-15 1518.64 Feb 10th Florida Warm Up 2019
4 Pittsburgh Loss 9-13 1766.36 Mar 9th Classic City Invite 2019
55 Florida State Loss 11-13 1382.83 Mar 9th Classic City Invite 2019
9 Massachusetts Loss 10-13 1737.36 Mar 9th Classic City Invite 2019
61 Tennessee Win 13-11 1783.03 Mar 9th Classic City Invite 2019
20 Tufts Loss 8-9 1739.15 Mar 10th Classic City Invite 2019
81 Georgia Tech Win 13-9 1865.88 Mar 10th Classic City Invite 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)