I would love to see some bright grad student do a thesis on Purdue football injuries.

[Tommaker]

Look back over the last 20-25 years or so. The data has got to there, they could categorize it, chart the who/what/when/where, compare it all to training regimes and diet, track the growth of size/speed in players with injury and reoccurrence, weight lifting programs, machines used, look at player condition/history prior to their college career, graph offenses and defenses by team and type versus when injuries occurred, field and weather conditions at the time, the whole shmear. Then compare that data to year over year and against the D1 mean. It's a lot of work, but hey, that's what PhD is supposed to be about, right?

 

[BCfanatic2020]

Look back over the last 20-25 years or so. The data has got to there, they could categorize it, chart the who/what/when/where, compare it all to training regimes and diet, track the growth of size/speed in players with injury and reoccurrence, weight lifting programs, machines used, look at player condition/history prior to their college career, graph offenses and defenses by team and type versus when injuries occurred, field and weather conditions at the time, the whole shmear. Then compare that data to year over year and against the D1 mean. It's a lot of work, but hey, that's what PhD is supposed to be about, right?

me too

 

[NVboiler]

The key is to put all your data in an easily accessible database such as Excel, and then you can easily manipulate the data to get meaningful correlations. Being a scientist and having stored all of my analytical data for other reasons, it was amazing to see all of the correlations I found when things weren't expected to correlate at all, and then often with a correlation coefficient of 1.00000. This was a part time project for me I dreamed up when I was idle at work, and it paid enormous dividends. I say, do the same with our football data. You could even hire someone to compile all the data and play around with it and perhaps come up with some highly significant results. But the key to such a project is having ALL data (even some not thought significant) in an easily accessible manipulatable data base.

 

[purduepat1969]

The key is to put all your data in an easily accessible database such as Excel, and then you can easily manipulate the data to get meaningful correlations. Being a scientist and having stored all of my analytical data for other reasons, it was amazing to see all of the correlations I found when things weren't expected to correlate at all, and then often with a correlation coefficient of 1.00000. This was a part time project for me I dreamed up when I was idle at work, and it paid enormous dividends. I say, do the same with our football data. You could even hire someone to compile all the data and play around with it and perhaps come up with some highly significant results. But the key to such a project is having ALL data (even some not thought significant) in an easily accessible manipulatable data base.

Agree with you. But Excel would be the last software I'd try to utilize. You'd want to use a statistical analysis software like Tableau.

 

[TopSecretBoiler]

The key is to put all your data in an easily accessible database such as Excel, and then you can easily manipulate the data to get meaningful correlations. Being a scientist and having stored all of my analytical data for other reasons, it was amazing to see all of the correlations I found when things weren't expected to correlate at all, and then often with a correlation coefficient of 1.00000. This was a part time project for me I dreamed up when I was idle at work, and it paid enormous dividends. I say, do the same with our football data. You could even hire someone to compile all the data and play around with it and perhaps come up with some highly significant results. But the key to such a project is having ALL data (even some not thought significant) in an easily accessible manipulatable data base.

excel is not a database. yikes. this sort of data is almost impossible to work with. It usually requires a team of people to collect and verify every situation. Then there's no control group, so you would end up looking at averages against other similar cases. then you would basically need all data from all major D1 programs for comparison to see how Purdue is an outlier. We run into this stuff a lot on the human performance front. It's difficult to tease out what is relevant or not for a given scenario. maybe nutrition was bad, maybe they got no sleep, maybe they were already slightly injured and didn't tell anyone. it goes on and on... correlation means nothing, you need causal relationships that you can change behavior against. Tough sledding in a dataset as complicated as injuries in D1 football. You might be able to go after one thing like concussions or ACLs but teasing out what Purdue specifically does differently is a nightmare task.

 

[old 23]

I think what you would find out is that Purdue is no better, or no worse than everybody else. It just always seems that way to us because they are the teams we follow.
Last year in football might have been an exception.

 

[Wolegib]

I believe you should also compare our injuries to before and after we built that awesome and expensive training facility! It appears our injuries have increased since we built that facility!

 

[njm8845]

The key is to put all your data in an easily accessible database such as Excel, and then you can easily manipulate the data to get meaningful correlations. Being a scientist and having stored all of my analytical data for other reasons, it was amazing to see all of the correlations I found when things weren't expected to correlate at all, and then often with a correlation coefficient of 1.00000. This was a part time project for me I dreamed up when I was idle at work, and it paid enormous dividends. I say, do the same with our football data. You could even hire someone to compile all the data and play around with it and perhaps come up with some highly significant results. But the key to such a project is having ALL data (even some not thought significant) in an easily accessible manipulatable data base.

excel isn’t a database. Access is.

I’m extremely skeptical that you got correlations of “1.00000”, and if you did, I would assume that you did something wrong.

tableau isn’t statistical analysis. It’s data visualization.

to answer the question, I don’t see why Purdue would have any more injuries than any place else. But it does impact us more because we don’t have the depth that a top athletic school has.