The correlation between OBP and run scoring is a certain number.  You're arguing that the correlation between Ks and run scoring is essentially a certain number, which is essentially the same number as the OBP to runs number.

So in your view, the correlation is just as strong as it is to OBP. Doesn't that sound off?

I don't think he's arguing anything about OBP. 

Posted by tecwrg on 3/4/2015 7:39:00 AM (view original):
When you're dealing with large volumes of discrete data points, you sometimes have to group and summarize the data in order to look for trends, trends that you would not be able to easily see (or see at all) when looking at the discrete data points.

When BL looks at his 600 data points, he sees nothing.  Yet there is a trend that's seen when the data is grouped and summarized, not just by the eyeball test (looking at the numbers themselves), but by statistical correlation.

Or are you denying that over the recent past that strikeouts have been steadily going up while scoring has been steadily going down?

No, grouping it together doesn't help you see a trend. Each team's Ks and run scoring are independent of other teams Ks and run scoring. By grouping them together, you miss that.

For example, if you looked at the team by team numbers for a two team league you might see this:

2014 Team A - 700 runs, 900 strikeouts
2013 Team A - 800 runs, 900 strikeouts
2012 Team A - 850 runs, 900 strikeouts

2014 Team B - 750 runs, 1200 strikeouts
2013 Team B - 750 runs, 1000 strikeouts
2012 Team B - 750 runs, 900 strikeouts

Grouping those teams together would give the appearance of a strong correlation when there clearly isn't one. If strikeout totals impact run scoring, we'd HAVE to see it in a team level. Otherwise, it isn't happening.

Posted by bad_luck on 3/4/2015 9:32:00 AM (view original):

Posted by tecwrg on 3/4/2015 7:39:00 AM (view original):
When you're dealing with large volumes of discrete data points, you sometimes have to group and summarize the data in order to look for trends, trends that you would not be able to easily see (or see at all) when looking at the discrete data points.

When BL looks at his 600 data points, he sees nothing.  Yet there is a trend that's seen when the data is grouped and summarized, not just by the eyeball test (looking at the numbers themselves), but by statistical correlation.

Or are you denying that over the recent past that strikeouts have been steadily going up while scoring has been steadily going down?

No, grouping it together doesn't help you see a trend. Each team's Ks and run scoring are independent of other teams Ks and run scoring. By grouping them together, you miss that.

For example, if you looked at the team by team numbers for a two team league you might see this:

2014 Team A - 700 runs, 900 strikeouts
2013 Team A - 800 runs, 900 strikeouts
2012 Team A - 850 runs, 900 strikeouts

2014 Team B - 750 runs, 1200 strikeouts
2013 Team B - 750 runs, 1000 strikeouts
2012 Team B - 750 runs, 900 strikeouts

Grouping those teams together would give the appearance of a strong correlation when there clearly isn't one. If strikeout totals impact run scoring, we'd HAVE to see it in a team level. Otherwise, it isn't happening.

You're NOT going to see it at a team level because all teams do NOT have the same level of player talent.

If you had 30 teams that were virtual clones of each other with respect to talent level, offensive approaches (power versus speed, etc), then you can do that.

Really, you DON'T comprehend this?

Right - shouldn't the less talented teams, who score less runs (because they're less talented) have more Ks? 

Not necessarily. 

Mark Reynolds, Adam Dunn and Brett Wallace have similar K/HR rates?   Do you think they're equally effective?    Do they have the same type of teammates around them to take advantage of their strengths/weaknesses?

Right. That's kinda the point. In the same way Placido Polanco and Victor Martinez aren't equally effective. Ks really don't matter a ton when it comes to run scoring. If you showed me 3 guys with similar OPS numbers, I'd tell you "yea, they're pretty much equally effective."

But continue arguing the point that the correlation of Ks to run scoring is as strong as OBP to run scoring.

I'm not arguing that.   Would you like to point out where I did?

The point tec is making is that "Ks are up, scoring is down."    That is factually correct.    Which is why I said it's pretty pointless to argue against it.   Arguing "OBP is down, scoring is down" is a better counter than "Whiffs don't matter" because you can't really prove that they don't.

Posted by tecwrg on 3/4/2015 9:38:00 AM (view original):

Posted by bad_luck on 3/4/2015 9:32:00 AM (view original):

Posted by tecwrg on 3/4/2015 7:39:00 AM (view original):
When you're dealing with large volumes of discrete data points, you sometimes have to group and summarize the data in order to look for trends, trends that you would not be able to easily see (or see at all) when looking at the discrete data points.

When BL looks at his 600 data points, he sees nothing.  Yet there is a trend that's seen when the data is grouped and summarized, not just by the eyeball test (looking at the numbers themselves), but by statistical correlation.

Or are you denying that over the recent past that strikeouts have been steadily going up while scoring has been steadily going down?

No, grouping it together doesn't help you see a trend. Each team's Ks and run scoring are independent of other teams Ks and run scoring. By grouping them together, you miss that.

For example, if you looked at the team by team numbers for a two team league you might see this:

2014 Team A - 700 runs, 900 strikeouts
2013 Team A - 800 runs, 900 strikeouts
2012 Team A - 850 runs, 900 strikeouts

2014 Team B - 750 runs, 1200 strikeouts
2013 Team B - 750 runs, 1000 strikeouts
2012 Team B - 750 runs, 900 strikeouts

Grouping those teams together would give the appearance of a strong correlation when there clearly isn't one. If strikeout totals impact run scoring, we'd HAVE to see it in a team level. Otherwise, it isn't happening.

You're NOT going to see it at a team level because all teams do NOT have the same level of player talent.

If you had 30 teams that were virtual clones of each other with respect to talent level, offensive approaches (power versus speed, etc), then you can do that.

Really, you DON'T comprehend this?

So, just so I'm clear, how many times a team strikes out doesn't matter. All that matters is how good their hitters are?

Another example - let's say the US, Canada, and Mexico, universally, legalize all illegal drugs. You see, using last year's stats and this year's stats, that violent crime rose by 10% in all 3 countries.

Tec: "LOOK! LEGAL DRUGS = MORE CRIME!"
Me: "Well, it's not as easy as it seems. When you look at each state and province, you see that crime fell in a lot of places where drug use is prevalent, and crime rose a lot in other places where it wasn't, and..." 
Tec: "CANADA AND MEXICO ARE SO DIFFERENT! WHY WOULD I CONSIDER WHAT YOU'RE SAYING?"

You're bad at examples.

Posted by MikeT23 on 3/4/2015 9:51:00 AM (view original):
I'm not arguing that.   Would you like to point out where I did?

The point tec is making is that "Ks are up, scoring is down."    That is factually correct.    Which is why I said it's pretty pointless to argue against it.   Arguing "OBP is down, scoring is down" is a better counter than "Whiffs don't matter" because you can't really prove that they don't.

If you're arguing that tec's analysis of the data is correct, that's what you're saying.

Posted by MikeT23 on 3/4/2015 9:58:00 AM (view original):
You're bad at examples.

Yes, I recognize that examples that go against your point are "retarded" in your view.