#125 Washington State (14-7)

avg: 1268.19  •  sd: 54.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
253 Oregon State-B Win 15-4 1317.62 Jan 21st Pacific Confrontational Pac Con
137 Portland State Loss 11-15 843 Jan 21st Pacific Confrontational Pac Con
78 Santa Clara Loss 11-15 1093.91 Jan 21st Pacific Confrontational Pac Con
74 Lewis & Clark Loss 8-15 926.12 Jan 22nd Pacific Confrontational Pac Con
241 Humboldt State Win 15-11 1138.4 Jan 22nd Pacific Confrontational Pac Con
216 Boise State Win 11-10 1002.69 Mar 4th Big Sky Brawl1
135 Brigham Young-B Win 13-4 1829.18 Mar 4th Big Sky Brawl1
237 Montana Win 15-5 1365.64 Mar 4th Big Sky Brawl1
216 Boise State Win 13-4 1477.69 Mar 11th Palouse Open 2023
323 Idaho** Win 13-4 897.67 Ignored Mar 11th Palouse Open 2023
237 Montana Win 13-6 1365.64 Mar 11th Palouse Open 2023
303 Whitworth** Win 13-2 1034.61 Ignored Mar 11th Palouse Open 2023
216 Boise State Win 11-6 1424.39 Mar 12th Palouse Open 2023
237 Montana Win 9-7 1044.98 Mar 12th Palouse Open 2023
303 Whitworth** Win 10-2 1034.61 Ignored Mar 12th Palouse Open 2023
53 Utah Loss 8-12 1178.83 Apr 1st Northwest Challenge Mens
17 Washington** Loss 5-15 1390.14 Ignored Apr 1st Northwest Challenge Mens
46 Western Washington Loss 8-10 1425.87 Apr 1st Northwest Challenge Mens
232 Chico State Win 14-8 1318.39 Apr 2nd Northwest Challenge Mens
129 Gonzaga Win 11-9 1505.78 Apr 2nd Northwest Challenge Mens
81 Whitman Loss 8-13 967.96 Apr 2nd Northwest Challenge Mens
**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)