Using Win%s to Illustrate the Impact of Being in the Play in Round

Last night's loss at Missouri State was a big back breaker, as it virtually cements us into the Thursday night games in St. Louis next week. Before we dive into the main topic of this post, here were my reactions:

• One of the drivers that allows us to be an efficient offense is Phelps' insistence on taking care of the basketball. Well, 12 turnovers in 60 posessions is pretty high for this team, and rounds out to a TO% of 20% (12 / 60 = 20%). Other than last Saturday's game at Cal State Northridge (TO% = 19.2%), we haven't seen a turnover rate this high since our January 23 win over Wichita State @ the Knapp (the Frank Wiseler 12 assist game).


• We showed against Cal State Northridge that we can be successful when we turn the ball over if we have a strong shooting night and if we get to the line (52.9% shooting, 28 free throw attempts). That wasn't the case last night as we shot fine (48.9%) and didn't get to the line (11 total attempts).


• We're not good enough defensively to bang out wins when we turn the ball over and don't shoot particularly well. I guess that's something we should already know by the last game of the regular season.

Oh well, back to other things. I was curious as to the impact of this loss -- or in other words -- quantifying the impact of watching my pipe dream of a #6 seed float away last night. To do so, I created a model that attempts to use a simplified framework that is similar to how Ken Pomeroy simulates games (Pythagorean Expectation combined with the Log 5 Method). I used the Pomeroy expected win percentages and made no adjustment for venue, under the assumption that St. Louis is a neutral court and would have no impact on either team.

This assumes that the tournament starts today and seeding is as follows:

This could or could not be what ends up occurring, but I'm too impatient to wait. So, using expected win %s and the log 5 method, I simulated our chances of winning the tournament, showing up at the final (i.e. being there on Sunday), and our chances of showing up at the quarterfinal (i.e. being there on Saturday). Using the setup above, I projected them as follows:

• Pr(Alive on Saturday) = 4.6%
• Pr(Alive on Sunday) = 1.8%
• Pr(Missouri Valley Champion) = 0.5%

This is not too out of the question, considering that we would have to win a game against a Valley opponent and then play the best team in the league. Contrast this to the results when I switched Missouri State (currently #6) and Drake (currently #8) in the model:

• Pr(Alive on Saturday) = 28.0%
• Pr(Alive on Sunday) = 7.8%
• Pr(Missouri Valley Champion) = 1.8%

Quite a big difference. You can see that we have not an excellent, but a decent shot of making it to Saturday. This probably isn't a shocker, but it reinforces the fact that your odds dramatically improve when you consider the fact that (a) you play a weaker opponent and (b) you don't have to win a game to get into the tournament.

Nevertheless, 1.8% versus 0.5% is not terribly significant, essentially because you still have a good shot of playing a strong UNI team, or you at least have to beat several strong teams in the way. But it really hurts us, a lot.

Oh well, at least I can say the following:

• Pr(Me Drinking Beer at the Last Day of West End) = 100%