#147 Toronto (7-14)

avg: 1280.11  •  sd: 62.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
15 Davenport** Loss 5-15 1459.59 Ignored Mar 15th Grand Rapids Invite 2025
169 Michigan Tech Loss 7-10 814.34 Mar 15th Grand Rapids Invite 2025
79 Notre Dame Loss 7-10 1182.04 Mar 15th Grand Rapids Invite 2025
141 Pittsburgh-B Loss 5-14 698.32 Mar 15th Grand Rapids Invite 2025
148 Grand Valley Loss 11-13 1050.9 Mar 16th Grand Rapids Invite 2025
266 Wisconsin-Platteville Win 15-7 1411.27 Mar 16th Grand Rapids Invite 2025
302 Western Michigan Win 15-7 1280.56 Mar 16th Grand Rapids Invite 2025
51 Cornell Loss 1-15 1123.89 Apr 12th Western NY D I Mens Conferences 2025
30 Ottawa Loss 12-14 1651.92 Apr 12th Western NY D I Mens Conferences 2025
115 RIT Loss 11-12 1259.22 Apr 12th Western NY D I Mens Conferences 2025
97 SUNY-Buffalo Loss 10-12 1236.51 Apr 12th Western NY D I Mens Conferences 2025
182 Carleton University Win 15-8 1718 Apr 13th Western NY D I Mens Conferences 2025
115 RIT Loss 10-12 1146.1 Apr 13th Western NY D I Mens Conferences 2025
97 SUNY-Buffalo Win 10-8 1737.3 Apr 13th Western NY D I Mens Conferences 2025
325 Central Connecticut State** Win 15-5 1197.71 Ignored Apr 26th Metro East D I College Mens Regionals 2025
51 Cornell Loss 10-15 1270.29 Apr 26th Metro East D I College Mens Regionals 2025
219 Princeton Win 15-6 1609.83 Apr 26th Metro East D I College Mens Regionals 2025
149 Rutgers Win 15-11 1659.83 Apr 26th Metro East D I College Mens Regionals 2025
108 Columbia Loss 7-12 922.94 Apr 27th Metro East D I College Mens Regionals 2025
30 Ottawa Loss 3-15 1272.88 Apr 27th Metro East D I College Mens Regionals 2025
115 RIT Loss 6-9 965.65 Apr 27th Metro East D I College Mens Regionals 2025
**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)