One of These Things Is Not Like the Other

[neepster]

A brief comparison of season winning % (college only) of recent 'great' coaches vs. recent Nebraska coaches (not including this year).

 

See if you can spot the reason Riley should have been a red flag... Note the coaches in the connecting letters report... the ones with the A are statistically the same... the ones with the B are statistically different...

 

[gobiggergoredder]

Nice minitab work. It doesn't tell us anything that wasn't already known. He actually seems to perform above his mean.

[beorach]

There are so many inequalities to consider between those coaches, though.

 

I was trying to find a list with just active coaches but this is the best I found quickly:

 

http://www.sports-reference.com/cfb/leaders/win-loss-pct-coach-career.html

[Enhance]

The challenging part of these statistics is the variables are not the same. These numbers compare Riley's time at one of the historically worst programs in the country to coaches who spent time at bad programs and great ones.

 

You're also comparing Riley to four of the greatest coaches in modern football history, two of whom are vying for consideration as the greatest football coach of all time.

 

The numbers certainly are telling, but, we already know a lot of this. I'm not saying if Riley had been a coach at a place like Alabama he would've been overly successful. But, there is a bit of a difference, to me at least, comparing a guy who spent 14 years at OSU to a coach who spent time at Florida and Ohio State. And, as I mentioned earlier, the latter coach is one of the best of all time.

[It'sNotAFakeID]

Unbelievable that we let the next Urban Meyer go, you guys, that standard deviation of .02 win % is safe and infuriating.

[gobiggergoredder]

There are so many inequalities to consider between those coaches, though.

 

I was trying to find a list with just active coaches but this is the best I found quickly:

 

http://www.sports-reference.com/cfb/leaders/win-loss-pct-coach-career.html

This is exactly why it is only one part of Six Sigma problem solving. Dude is ignoring the other 75% of the methodology.

 

This is literally meaningless.

[neepster]

 

There are so many inequalities to consider between those coaches, though.

 

I was trying to find a list with just active coaches but this is the best I found quickly:

 

http://www.sports-reference.com/cfb/leaders/win-loss-pct-coach-career.html

This is exactly why it is only one part of Six Sigma problem solving. Dude is ignoring the other 75% of the methodology.

 

This is literally meaningless.

 

 

It's totally incorrect to say this is literally meaningless. This is data. You can argue the data is comparing apples and oranges or that the data is wrong, but you cannot say it is meaningless. This data clearly says that Riley is statistically worse over his 14 years of coaching (in winning %) than many 'tier 1' type school coaches. I can add some additional data points (like Stoops, etc) and expand the data set but clearly based on his past performance you have to question why he was hired. Heck, even Pelini was very likely better statistically.

 

I am not doing a Six Sigma Analysis of the football coach. It wouldn't make any sense anyway. So please stop throwing out terms and analyses you clearly don't understand.

[gobiggergoredder]

 

 

There are so many inequalities to consider between those coaches, though.

 

I was trying to find a list with just active coaches but this is the best I found quickly:

 

http://www.sports-reference.com/cfb/leaders/win-loss-pct-coach-career.html

This is exactly why it is only one part of Six Sigma problem solving. Dude is ignoring the other 75% of the methodology.

 

This is literally meaningless.

 

 

It's totally incorrect to say this is literally meaningless. This is data. You can argue the data is comparing apples and oranges or that the data is wrong, but you cannot say it is meaningless. This data clearly says that Riley is statistically worse over his 14 years of coaching (in winning %) than many 'tier 1' type school coaches. I can add some additional data points (like Stoops, etc) and expand the data set but clearly based on his past performance you have to question why he was hired. Heck, even Pelini was very likely better statistically.

 

I am not doing a Six Sigma Analysis of the football coach. It wouldn't make any sense anyway. So please stop throwing out terms and analyses you clearly don't understand.

 

I know a little bit about this. I don't use Six Sigma much because it's not practical for RCA(which is what I do for a living). I'm glad you learned to use minicab in your engineering class. You showed his winning percentage is lower than good (Except 408) coaches. Anyone could do that on their cell phone.

 

You also cherry picked. You compared him the most successful coaches at the biggest programs. Where is Brady Hoke, Al Golden, Tyrone Whittingham, Derek Dooley or Dan Hawkins.

 

What you should really do is now develop a hypothesis as to why. Are there any contributing factors that may lead to this? It could be anything (coach, Budget, Years at School, age of coach, Recruiting, Size of town, tenure of staff, new starters, injuries, returning starters, graduation rate.....blah blah). Based upon your brainstorming you could select 3-6 factors that you think are significant contributing factors and run a fractional factorial experiment and it would tell you statistically what leads to a lower winning percentage.

 

 

Or you could just do what you did. It is a lot easier to do that.

[NebraskaHarry]

 

 

 

There are so many inequalities to consider between those coaches, though.

 

I was trying to find a list with just active coaches but this is the best I found quickly:

 

http://www.sports-reference.com/cfb/leaders/win-loss-pct-coach-career.html

This is exactly why it is only one part of Six Sigma problem solving. Dude is ignoring the other 75% of the methodology.

 

This is literally meaningless.

 

 

It's totally incorrect to say this is literally meaningless. This is data. You can argue the data is comparing apples and oranges or that the data is wrong, but you cannot say it is meaningless. This data clearly says that Riley is statistically worse over his 14 years of coaching (in winning %) than many 'tier 1' type school coaches. I can add some additional data points (like Stoops, etc) and expand the data set but clearly based on his past performance you have to question why he was hired. Heck, even Pelini was very likely better statistically.

 

I am not doing a Six Sigma Analysis of the football coach. It wouldn't make any sense anyway. So please stop throwing out terms and analyses you clearly don't understand.

 

I know a little bit about this. I don't use Six Sigma much because it's not practical for RCA(which is what I do for a living). I'm glad you learned to use minicab in your engineering class. You showed his winning percentage is lower than good (Except 408) coaches. Anyone could do that on their cell phone.

 

You also cherry picked. You compared him the most successful coaches at the biggest programs. Where is Brady Hoke, Al Golden, Tyrone Whittingham, Derek Dooley or Dan Hawkins.

 

What you should really do is now develop a hypothesis as to why. Are there any contributing factors that may lead to this? It could be anything (coach, Budget, Years at School, age of coach, Recruiting, Size of town, tenure of staff, new starters, injuries, returning starters, graduation rate.....blah blah). Based upon your brainstorming you could select 3-6 factors that you think are significant contributing factors and run a fractional factorial experiment and it would tell you statistically what leads to a lower winning percentage.

 

 

Or you could just do what you did. It is a lot easier to do that.

 

All fired...

[gobiggergoredder]

 

All fired...

 

 

 

And their horrific winning percentages at P5 schools would likely change range that our young scientist is using.

[NebraskaHarry]

 

 

All fired...

 

 

 

And their horrific winning percentages at P5 schools would likely change range that our young scientist is using.

 

Well lets be honest. "Our young scientist" didn't have to run a statistical analysis to determine that Riley is a bad coach and he fits more into the likes of Brady Hoke, Al Golden, Tyrone Whittingham, Derek Dooley or Dan Hawkins.

[gobiggergoredder]

 

 

 

All fired...

 

 

 

And their horrific winning percentages at P5 schools would likely change range that our young scientist is using.

 

Well lets be honest. "Our young scientist" didn't have to run a statistical analysis to determine that Riley is a bad coach and he fits more into the likes of Brady Hoke, Al Golden, Tyrone Whittingham, Derek Dooley or Dan Hawkins.

I'm not here to say whether or not he is a bad football coach. I think it's way too early to tell, but I understand the disappointment.

 

This thread is no different than global warming. Here's a graph that says the global temp has gone up 2.745645 degrees, so people are definitely ruining the planet. Don't consider any other factors. Riley has .500 winning percentage so he's terrible. It's lazy and people hide behind 'science'.

[MichiganDad3]

Thanks neepster. It is interesting to look at data analysis. Of course their are other influencing variables, bu the analysis is still interesting. Bo's 9 win consistence really sticks out when viewed in this perspective.