#362 Concordia-Wisconsin (5-17)

avg: 438.12  •  sd: 78.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
357 Michigan State-B Win 13-6 1074.64 Mar 16th Grand Rapids College Invite
37 Michigan** Loss 2-13 1285.75 Ignored Mar 16th Grand Rapids College Invite
280 Western Michigan Loss 5-13 228.64 Mar 16th Grand Rapids College Invite
316 Calvin University Win 13-10 988.55 Mar 17th Grand Rapids College Invite
257 Wisconsin-B Loss 5-13 317.85 Mar 17th Grand Rapids College Invite
282 Toledo Loss 4-13 218.55 Mar 17th Grand Rapids College Invite
237 Carthage Loss 7-13 432.12 Mar 24th Meltdown mini tournament
270 Wisconsin-Platteville Loss 0-13 260.27 Mar 24th Meltdown mini tournament
414 Wisconsin-Milwaukee-B Win 13-7 92.76 Mar 24th Meltdown mini tournament
270 Wisconsin-Platteville Loss 6-13 260.27 Mar 25th Meltdown mini tournament
237 Carthage Loss 8-13 493.49 Apr 13th Lake Superior D III Mens Conferences 2024
386 Milwaukee Engineering Win 13-11 476.05 Apr 13th Lake Superior D III Mens Conferences 2024
270 Wisconsin-Platteville Loss 3-13 260.27 Apr 13th Lake Superior D III Mens Conferences 2024
129 Michigan Tech Loss 8-13 885.89 Apr 13th Lake Superior D III Mens Conferences 2024
270 Wisconsin-Platteville Loss 9-15 344.79 Apr 14th Lake Superior D III Mens Conferences 2024
386 Milwaukee Engineering Win 15-9 762.69 Apr 14th Lake Superior D III Mens Conferences 2024
237 Carthage Loss 4-15 389.65 Apr 27th North Central D III College Mens Regionals 2024
45 St Olaf** Loss 5-15 1211.34 Ignored Apr 27th North Central D III College Mens Regionals 2024
134 Macalester** Loss 4-15 772.12 Ignored Apr 27th North Central D III College Mens Regionals 2024
270 Wisconsin-Platteville Loss 12-15 559.78 Apr 28th North Central D III College Mens Regionals 2024
278 St Thomas Loss 5-11 241.12 Apr 28th North Central D III College Mens Regionals 2024
304 Luther College Loss 6-15 94.97 Apr 28th North Central D III College Mens Regionals 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)