In other words, let’s say we have Scrappy Cleftchin. He has a 1.23 FIP (really good!) and only one blown save! Obviously this is because he’s an awesome player (and with a name like that, how couldn’t he be?!).

Let’s take Fatso McSucksalot on the other hand. He has a 3.45 FIP (pretty good) and 10 blown saves. Man, he must have problems and be worse than Scrappy Cleftchin, right? I mean, 5 blown saves? Clearly he is no good under pressure.

Only here’s the thing:
Scrappy Cleftchin: 5 IP, 1 save opportunity
Fatso McSucksalot: 120 IP, 80 save opportunities

In other words, the 1.23 FIP is over a really small sample size, and his rate of getting saves is 0% (0/1). Fatso McSucksalot, on the other hand, has pitched twice as many innings as the standard closer at a pretty good rate, and he has a 94% save rate!

It only gets worse when you think about it more.

Sometimes a pitcher comes on to get a save in a 1-run ballgame with the bases loaded and the heart of the batting order coming up. Sometimes a pitcher comes into the game with a 3-run lead, nobody on, and the bottom of the order coming up.

Yet both count equally as saves (and both count equally as blown saves for that matter). So how can we really judge that sort of thing properly?

My basic point is that before we remake the graph, we need to rethink what it’s showing, and whether it’s actually appropriate. In this case, the best graph would be no graph, or just to say, “Papelbon blew it last night” or “Lidge has really choked this year”. Save yourself the space, and all that.

Or you could go into the data and actually show what FIP has to do with save percentage. Mix in leverage index and you may be able to get something halfway decent as a conclusion.

I agree, removing Lidge’s data point was an action I took without justification. The test is to compute the supposed regression including Lidge, then plot the residuals, and determine whether they follow a normal distribution. A chi squared test would be a more discerning test than a simple box plot. When I get a free minute.

How about making a box-plot of the regression residuals? Use the Deming regression you demonstrated earlier since both ratings have error associated with them. If Lidge’s point is an outlier then, you probably can justify removing it.

In the only statistics class I ever took, the first thing the professor told us was: “There are lies, damn lies, and statistics.” The approach I like to take is to visualize the data to get a feel for it, then try to apply a statistical test that supports what my visual intuition is telling me. However, like Rebecca, I know that I can get myself into trouble quickly with statistics. Its best (I think) to stick to tests that you understand and use them consistently rather than trying to find a test that supports your position.

I this case, I think we both agree that there isn’t any correlation between FIP and blown saves, we just discussing the best way to show that.

Lidge’s blown saves are the outlier in this chart.

I was at the game Sept 30, the first after they clinched a playoff slot. They put all their scrubs on the field and got cooked 12-0. From that point on, they displayed none of the killer instinct required to succeed in playoff baseball.

I’m disappointed, but certainly not grieving. Unlike most years, I also can’t get excited about the rest of the post-season.

I’m not sure I completely agree with your rationale for removing the “outlier”. Just because removing the point supports the point you are trying to make doesn’t make it an outlier. There was no statistically significant correlation to begin with removing the point didn’t really add any value. One might even be able to remove a point that would make the correlation appear to improve so you need an impartial judgement of what is considered an outlier (I don’t know myself what test you would use in this situation).

Your chart highlights that there is no correlation and it is the better/proper format for investigating the potential correlation that was implied by the original chart and labeling. The statistical test lends an impartial perspective to the graphics. I’m sure one could design a chart that shows a correlation the same way other charts lie about magnitudes or relative changes.

Certainly more data would be useful in supporting or refuting the conclusion that there is no correlation. Ten points is never a very good data set.

I only called Lidge’s point an outlier since it was not part of the cloud of the other pitchers’ data. I looked for a correlation, because I thought the original chart and its labeling asserted that there was some kind of relationship. Removing Lidge’s point from the regression negated the correlation, which supported my calling it an outlier.

It would be interesting to see how all pitchers compare on this chart. Maybe there could be a series of light gray points for all pitchers, to show whether these hand-selected ones really are better.