#376 Indiana Wesleyan (2-9)

avg: 353.42  •  sd: 83.71  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
276 North Park Loss 6-9 351.07 Mar 9th D III Midwestern Invite 2019
70 St Olaf** Loss 3-11 900.54 Ignored Mar 9th D III Midwestern Invite 2019
121 Puget Sound** Loss 3-8 681.02 Ignored Mar 9th D III Midwestern Invite 2019
104 Portland** Loss 0-13 739.16 Ignored Mar 10th D III Midwestern Invite 2019
215 Butler Loss 3-11 328.33 Mar 23rd Indy Invite College 2019
411 Eastern Illinois Win 8-7 284.97 Mar 23rd Indy Invite College 2019
215 Butler Loss 3-13 328.33 Mar 30th Black Penguins Classic 2019
258 Olivet Nazarene Loss 7-9 550.71 Mar 30th Black Penguins Classic 2019
386 Southern Indiana Win 8-6 587.98 Mar 30th Black Penguins Classic 2019
128 Saint Louis** Loss 1-9 672.58 Ignored Mar 31st Black Penguins Classic 2019
302 Rose-Hulman Loss 5-11 52.23 Mar 31st Black Penguins Classic 2019
**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)