#54 Northwestern (10-10)

avg: 1616.19  •  sd: 80.11  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
9 Oregon Loss 3-15 1537.14 Jan 28th Santa Barbara Invitational 2023
18 California Win 12-10 2199.69 Jan 28th Santa Barbara Invitational 2023
57 Stanford Win 13-9 2000.81 Jan 28th Santa Barbara Invitational 2023
46 Western Washington Loss 10-15 1234.93 Jan 28th Santa Barbara Invitational 2023
16 British Columbia Loss 5-11 1392.55 Jan 29th Santa Barbara Invitational 2023
50 Case Western Reserve Loss 8-12 1198.86 Jan 29th Santa Barbara Invitational 2023
29 Utah State Loss 12-13 1713.27 Jan 29th Santa Barbara Invitational 2023
92 Missouri S&T Win 11-9 1679.02 Mar 4th Midwest Throwdown 2023
207 Illinois State** Win 13-1 1510.86 Ignored Mar 4th Midwest Throwdown 2023
325 Washington University-B** Win 13-2 886.48 Ignored Mar 4th Midwest Throwdown 2023
94 Saint Louis Win 11-8 1790.41 Mar 5th Midwest Throwdown 2023
22 Washington University Loss 4-11 1305.32 Mar 5th Midwest Throwdown 2023
64 St. Olaf Loss 8-12 1126.84 Mar 5th Midwest Throwdown 2023
60 Middlebury Win 13-9 1996.59 Mar 18th Centex 2023
79 Texas A&M Win 13-8 1969.84 Mar 18th Centex 2023
79 Texas A&M Loss 10-13 1145.54 Mar 18th Centex 2023
6 Colorado Loss 6-13 1597.57 Mar 18th Centex 2023
40 Colorado College Win 15-13 1949.52 Mar 19th Centex 2023
47 Colorado State Win 15-13 1861.4 Mar 19th Centex 2023
39 Florida Loss 7-11 1274.52 Mar 19th Centex 2023
**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)