#83 Northwestern (10-9)

avg: 1335.48  •  sd: 66.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
17 Brigham Young Loss 2-15 1275.44 Jan 27th Santa Barbara Invite 2024
43 California-San Diego Loss 9-15 1046.78 Jan 27th Santa Barbara Invite 2024
22 Washington Loss 5-15 1219 Jan 27th Santa Barbara Invite 2024
23 UCLA Loss 5-15 1208.44 Jan 27th Santa Barbara Invite 2024
54 California-Santa Barbara Loss 11-14 1156.3 Jan 28th Santa Barbara Invite 2024
115 Southern California Win 13-8 1680.95 Jan 28th Santa Barbara Invite 2024
176 Saint Louis Win 10-9 1051.24 Mar 2nd Midwest Throwdown 2024
259 Wisconsin-B Win 10-7 950.09 Mar 2nd Midwest Throwdown 2024
124 Macalester Win 11-8 1512.19 Mar 2nd Midwest Throwdown 2024
121 Iowa State Win 10-7 1544.72 Mar 3rd Midwest Throwdown 2024
48 Missouri Loss 8-9 1389.77 Mar 3rd Midwest Throwdown 2024
95 Wisconsin-Eau Claire Loss 6-9 831.46 Mar 3rd Midwest Throwdown 2024
128 Colorado College Win 13-6 1736 Mar 30th Huck Finn 2024
118 Michigan Tech Win 12-9 1518.99 Mar 30th Huck Finn 2024
108 Wisconsin-Milwaukee Win 12-7 1720.21 Mar 30th Huck Finn 2024
117 Vanderbilt Win 9-8 1304.97 Mar 30th Huck Finn 2024
50 Alabama Win 12-10 1739.69 Mar 31st Huck Finn 2024
49 St Olaf Loss 10-12 1265.04 Mar 31st Huck Finn 2024
65 Stanford Loss 7-13 847.4 Mar 31st Huck Finn 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)