#42 Purdue (8-4)

avg: 1700.79  •  sd: 109.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
31 Brown Win 10-8 2136.86 Feb 24th Commonwealth Cup Weekend 2 2024
88 Virginia Tech Win 11-4 1897.26 Feb 24th Commonwealth Cup Weekend 2 2024
35 Ohio Loss 9-11 1548.93 Feb 24th Commonwealth Cup Weekend 2 2024
49 Georgia Tech Loss 8-10 1369.54 Feb 25th Commonwealth Cup Weekend 2 2024
94 Duke Win 11-8 1633.25 Feb 25th Commonwealth Cup Weekend 2 2024
61 Penn State Loss 5-7 1175.35 Feb 25th Commonwealth Cup Weekend 2 2024
84 Iowa State Win 11-2 1923.81 Mar 30th Old Capitol Open 2024
77 Michigan Tech Win 8-4 1935.24 Mar 30th Old Capitol Open 2024
195 Minnesota-B** Win 13-1 870.18 Ignored Mar 30th Old Capitol Open 2024
51 Macalester Win 7-6 1733.92 Mar 31st Old Capitol Open 2024
34 Minnesota Loss 0-8 1218.79 Mar 31st Old Capitol Open 2024
82 Northwestern Win 12-1 1932.46 Mar 31st Old Capitol Open 2024
**Blowout Eligible

FAQ

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
  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
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)