#8 Pittsburgh (15-7)

avg: 2155.18  •  sd: 58.01  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
2 Brigham Young Loss 12-13 2193.3 Feb 3rd Florida Warm Up 2023
79 Texas A&M** Win 13-5 2073.68 Ignored Feb 3rd Florida Warm Up 2023
147 Connecticut Win 12-7 1682.99 Feb 4th Florida Warm Up 2023
39 Florida Win 11-6 2288.11 Feb 4th Florida Warm Up 2023
19 Georgia Loss 12-13 1825.85 Feb 4th Florida Warm Up 2023
21 Northeastern Win 13-6 2507.17 Feb 4th Florida Warm Up 2023
23 Wisconsin Win 13-8 2390.68 Feb 5th Florida Warm Up 2023
26 Georgia Tech Win 13-8 2364.49 Feb 5th Florida Warm Up 2023
12 Minnesota Win 13-9 2489.48 Mar 4th Smoky Mountain Invite
107 Tennessee Win 13-6 1941.72 Mar 4th Smoky Mountain Invite
15 UCLA Loss 9-11 1779.08 Mar 4th Smoky Mountain Invite
21 Northeastern Win 11-9 2156.38 Mar 4th Smoky Mountain Invite
14 Carleton College Loss 13-15 1835.69 Mar 5th Smoky Mountain Invite
4 Texas Loss 8-15 1649.94 Mar 5th Smoky Mountain Invite
6 Colorado Loss 11-13 1968.73 Mar 5th Smoky Mountain Invite
72 Auburn Win 13-6 2097.8 Apr 1st Easterns 2023
3 Massachusetts Win 12-11 2436.41 Apr 1st Easterns 2023
18 California Win 12-10 2199.69 Apr 1st Easterns 2023
20 North Carolina State Win 12-6 2524.71 Apr 1st Easterns 2023
12 Minnesota Win 15-11 2452.08 Apr 2nd Easterns 2023
5 Vermont Loss 12-14 1989.11 Apr 2nd Easterns 2023
7 Cal Poly-SLO Win 13-12 2300.35 Apr 2nd Easterns 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)