#386 Southern Indiana (1-10)

avg: 287.49  •  sd: 93.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
105 Iowa** Loss 3-13 737.36 Ignored Mar 16th Shamrock Showdown 2019
196 Middle Tennessee State Loss 8-11 637.53 Mar 16th Shamrock Showdown 2019
269 Ball State Loss 3-13 185.46 Mar 16th Shamrock Showdown 2019
226 Miami (Ohio) Loss 7-12 395.94 Mar 16th Shamrock Showdown 2019
105 Iowa** Loss 4-15 737.36 Ignored Mar 17th Shamrock Showdown 2019
336 Arkansas State Win 13-12 666.03 Mar 17th Shamrock Showdown 2019
376 Indiana Wesleyan Loss 6-8 52.93 Mar 30th Black Penguins Classic 2019
258 Olivet Nazarene Loss 8-11 464.44 Mar 30th Black Penguins Classic 2019
215 Butler** Loss 4-13 328.33 Mar 30th Black Penguins Classic 2019
360 Illinois-Chicago Loss 5-11 -168.8 Mar 31st Black Penguins Classic 2019
411 Eastern Illinois Loss 11-12 34.97 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)