#401 Marquette-B (1-9)

avg: 41.72  •  sd: 104.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
261 Drake** Loss 4-15 147.36 Ignored Mar 3rd Midwest Throwdown 2018
96 Missouri State** Loss 2-15 750.5 Ignored Mar 3rd Midwest Throwdown 2018
259 Northern Illinois** Loss 1-15 152.4 Ignored Mar 3rd Midwest Throwdown 2018
306 Carleton College-Hot Karls Loss 4-15 -39.64 Mar 4th Midwest Throwdown 2018
392 Drury Loss 11-15 -236.31 Mar 4th Midwest Throwdown 2018
257 Knox Loss 6-8 455.13 Mar 24th Meltdown 2018
157 St Olaf Loss 4-9 505.34 Mar 24th Meltdown 2018
420 Wisconsin-C Win 6-5 -47.92 Mar 24th Meltdown 2018
339 Northern Michigan Loss 4-11 -188.89 Mar 24th Meltdown 2018
346 Illinois-Chicago Loss 5-9 -140.42 Mar 25th Meltdown 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)