#14 Whitman (6-7)

avg: 2386.14  •  sd: 99.39  •  top 16/20: 91.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
10 Pittsburgh Loss 10-13 2153.61 Mar 3rd Stanford Invite 2018
5 Oregon Loss 7-13 2052.99 Mar 3rd Stanford Invite 2018
9 Colorado Loss 6-11 1953 Mar 3rd Stanford Invite 2018
61 California-Davis Win 13-5 2417.92 Mar 4th Stanford Invite 2018
26 California Win 13-6 2733.23 Mar 4th Stanford Invite 2018
43 Southern California Loss 7-8 1865.28 Mar 4th Stanford Invite 2018
20 Washington Loss 8-11 1874.63 Mar 23rd NW Challenge 2018
1 Dartmouth Loss 2-15 2297.25 Mar 23rd NW Challenge 2018
21 Michigan Win 13-6 2838.43 Mar 24th NW Challenge 2018
5 Oregon Loss 11-14 2297.18 Mar 24th NW Challenge 2018
19 Vermont Win 12-6 2842.79 Mar 24th NW Challenge 2018
26 California Win 15-7 2733.23 Mar 25th NW Challenge 2018
12 Carleton College Win 15-12 2722.46 Mar 25th NW Challenge 2018
**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)