Tag Archives: pitching statistics

The Shelf Life of Top Shelf Performances

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Short summary: I look at how much year-over-year persistence there is in the lists of best position players and pitchers, using Wins Above Replacement (WAR). It appears as though there is substantial volatility, with only the very best players being more likely than not to repeat on the lists. In the observed data, pitchers are slightly more likely to remain at the top of the league than position players, but the difference is not meaningful.

Last week, Sky Kalkman posted a question that I thought seemed interesting.

Obviously, this requires having a working definition of “ace,” for which Kalkman later suggests “the top dozen-ish” pitchers in baseball. That seems reasonable to me, but it raises another question: what metric to use for “top”?

I eventually wound up using an average of RA9-WAR and FIP-WAR (the latter being the standard WAR offered by FanGraphs). There are some drawbacks to using counting stats rather than a rate stat, specifically that a pitcher that misses two months due to injury might conceivably be an ace but won’t finish at the top of the leaderboard. That said, my personal opinion is that health is somewhat of a skill and dependability is part of being an ace.

I chose to use this blend of WAR (it’s similar to what Tangotiger sometimes says he uses) because I wanted to incorporate some aspects of Fielding Dependent Pitching into the calculations. It’s a bit arbitrary, but the analysis I’m about to present doesn’t change much if you use just FIP-WAR or plain old FIP- instead.

I also decided to use the period from 1978 to the present as my sample; 1978 was the expansion that brought the majors to 26 teams (close to the present 30), keeping the total pool of talent reasonably similarly-sized throughout the entire time period while still providing a reasonably large sample size.

So, after collecting the data, what did I actually compute? I worked with two groups—the top 12 and top 25 pitchers by WAR in a given year—and then looked at three things. I first examined the probability they would still be in their given group the next year, two years after, and so on up through 10 years following their initial ace season. (Two notes: I included players tied for 25th, and I didn’t require that the seasons be consecutive, so a pitcher who bounced in and out of the ace group will still be counted. For instance, John Smoltz counts as an ace in 1995 and 2005 in this system, but not for the years 2001–04. He’s still included in the “ace 10 years later” group.) As it turns out, the “half-life” that Kalkman postulates is less than a year: 41% of top 25 pitchers are in the top 25 the next year, with that figure dropping to 35% for top 12 pitchers who remain in the top 12.

I also looked at those probabilities by year, to see if there’s been any shift over time—basically, is the churn greater or less than it used to be? My last piece of analysis was to look at the probabilities by rank in the initial year to see how much more likely the very excellent pitchers are to stay at the top than the merely excellent pitchers. Finally, I ran all of these numbers for position players as well, to see what the differences are and provide some additional context for the numbers.

I played around and ultimately decided that some simple charts were the easiest way to convey what needed to be said. (Hopefully they’re of acceptable quality—I was having some issues with my plotting code.) We’ll start with the “half-life” graph, i.e. the “was this pitcher still an ace X years later?” chart.

HalfLife

As you can see, there’s a reasonable amount of volatility, in that the typical pitcher who cracks one of these lists won’t be on the same list the next year. While there’s a small difference between pitchers and position players for each group in the one year numbers, it’s not statistically significant and the lines blur together when looking at years further out, so I don’t think it’s meaningful.

Now, what if we look at things by year? (Note that from here on out we’re only looking at the year-over-year retention rate, not rates from two or more years.)

AllByYear

This is a very chaotic chart, so I’ll explain what I found and then show a slightly less noisy version. The top 25 groups aren’t correlated with each other, and top 25 batters isn’t correlated with time. However, top 25 pitchers is lightly positively correlated with time (r = 0.33 and p = 0.052), meaning that the top of the pitching ranks has gotten a bit more stable over the last 35 years. Perhaps more interestingly, the percentage for top 12 pitchers is more strongly positively correlated with time (r = 0.46, p < 0.01), meaning that the very top has gotten noticeably more stable over time (even compared to the less exclusive top), whereas the same number for hitters is negative (r = -0.35, p = 0.041), meaning there’s more volatility at the top of the hitting WAR leaderboard than there used to be.

These effects should be more visible (though still noisy) in the next two charts that show just the top 12 numbers (one’s smoothed using LOESS, one not). I’m reluctant to speculate as to what could be causing these effects: it could be related to run environments, injury prevention, player mobility, or a whole host of other factors, so I’ll leave it alone for now. (The fact that any explanation might have to also consider why this effect is stronger for the top 12 than the top 25 is another wrinkle.)

12ByYear

SmoothByYear

The upshot of these graphs, though, is that (if you buy that the time trend is real) the ace half-life has actually increased over the last couple decades, and it’s gone down for superstar position players over the same period.

Finally, here’s the chart showing how likely a player who had a given rank is to stay in the elite group for another year. This one I’ve also smoothed to make it easier to interpret:

RankPlot

What I take away from these charts are that, unsurprisingly, the very best players tend to persist in the elite group for multiple years, but that the bottom of the top is a lot less likely to stay in the group. Also, the gaps at the far left of the graph (corresponding to the very best players) are larger than the gaps we’ve seen between pitchers and hitters anywhere else. This says that, in spite of pitchers’ reputation as being volatile, at the very top they have been noticeably less prone to large one-year drop offs than the hitters are. That said, the sample is so small (these buckets are a bit larger than 30 each) that I wouldn’t take that as predictive so much as indicative of an odd trend.

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What’s the Point of DIPS, Anyway?

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In the last piece I wrote, I mentioned that I have some concerns about the way that people tend to think about defense independent pitching statistics (DIPS), especially FIP. (Refresher: Fielding Independent Pitching is a metric commonly used as an ERA estimator based on a pitcher’s walk, strikeout, and HR numbers.) I’m writing this piece in part as a way for me to sort some of my thoughts on the complexities of defense and park adjustments, not necessarily to make a single point (and none of these thoughts are terribly original).

All of this analysis starts with this equation, which is no less foundational for being almost tautological: Runs Allowed = Fielding Independent Pitching + Fielding Dependent Pitching. (Quick aside: Fielding Independent Pitching refers both to a concept and a metric; in this article, I’m mostly going to be talking about the concept.) In other words, there are certain ways of preventing runs that don’t rely on getting substantial aid from the defense (strike outs, for instance), and certain ways that do (allowing soft contact on balls in play).

In general, most baseball analysts tend to focus on the fielding independent part of the equation. There are a number of good reasons for this, the primary two being that it’s much simpler to assess and more consistent than its counterpart. There’s probably also a belief that, because it’s more clearly intrinsic to the pitcher, it’s more worthwhile to understand the FI portion of pitching. There are pitchers for whom we shy away from using the FI stats (like knuckleballers), but if you look at the sort of posts you see on FanGraphs, they’ll mostly be talking about performance in those terms.

That’s not always (or necessarily ever) a problem, but it often omits an essential portion of context. To see how, look at these three overlapping ways of framing the question “how good has this pitcher been?”:

1) If their spot on their team were given to an arbitrary (replacement-level or average) pitcher, how much better or worse would the team be?

2) If we took this pitcher and put them on a hypothetically average team (average in terms of defense and park, at least), how much better or worse would that team be?

3) If we took this pitcher and put them on a specific other team, how much better or worse would that team be?

Roughly speaking, #2 is how I think of FanGraphs’ pitcher WAR. #1 is Baseball Reference’s WAR. I don’t know of anywhere that specifically computes #3, but in theory that’s what you should get out of a projection system like Baseball Prospectus’s PECOTA or the ZiPS numbers found at FanGraphs’. (In practice, my understanding is that the projections aren’t necessarily nuanced enough to work that out precisely.)

The thing, though, is that pitchers don’t work with an average park and defense behind them. You should expect a fly ball pitcher to post better numbers with the Royals and their good outfield defense and a ground ball pitcher to do worse in front the butchers playing in the Cleveland infield. From a team’s perspective, though, a run saved is a run saved, and who cares whether it’s credited to the defense, the pitcher, or split between the two? If Jarrod Dyson catches the balls sent his way, it’s good to have a pitcher who’s liable to have balls hit to him. In a nutshell, a player’s value to his team (or another team) is derived from the FIP and the FDP, and focusing on the FIP misses some of that. Put your players in the best position for them to succeed, as the philosophy often attributed to Earl Weaver goes.

There are a number of other ways to frame this issue, which, though I’ve been talking in terms of pitching, clearly extends beyond that into nearly all of the skills baseball players demonstrate. Those other frames are all basically a restatement of that last paragraph, so I’ll try to avoid belaboring the point, but I’ll add one more example. Let’s say you have two batters who are the same except for 5% of their at-bats, which are fly balls to left field for batter A and to right field for batter B. By construction, they are players of identical quality, but player B is going to be worth more in Cleveland, where those fly balls are much more likely to go out of the park. Simply looking at his wRC+ won’t give you that information. (My limited knowledge of fantasy baseball suggests to me that fantasy players, because they use raw stats, are more attuned to this.)

Doing more nuanced contextual analysis of the sort I’m advocating is quite tricky and is beyond my (or most people’s) ability to do quickly with the numbers we currently have available. I’d still love, though, to see more of it, with two things in particular crossing my mind.

One is in transaction analysis. I read a few pieces discussing the big Samardzija trade, for instance, and in none did they mention (even in passing) how his stuff is likely to play in Oakland given their defense and park situation. This isn’t an ideal example because it’s a trade with a lot of other interesting aspects to it, but in general, it’s something I wish I saw a bit more of—considering the amount of value a team is going out of a player after adjusting for park and defense factors. The standard way of doing this is to adjust things from his raw numbers to a neutral context, but bringing things one step further, though challenging, should add another layer of nuance. (I will say that in my experience you see such analyses a bit more with free agency analyses, especially of pitchers.)

The second is basically expanding what we think of as being park and defensive adjustments. This is likely impossible to do precisely without more data, but I’d love to see batted ball data used to get a bit more granular in the adjustments; for instance, dead pull hitters should be adjusted differently from guys who use the whole field. This isn’t anything new—it’s in the FanGraphs page explaining park factors—but it’s something that occasionally gets swept under the rug.

One last note, as this post gets ever less specific: I wonder how big the opportunity is for teams to optimize their lineups and rotations based on factors such as these—left-handed power hitters go against the Yankees, ground ball hitters against the Indians, etc. We already see this to some extent, but I’d be curious to see what the impact is. (If you can quantify how big an edge you’re getting on a batter-by-batter basis—a big if—you could run some simulations to quantify the gain from all these adjustments. It’s a complex optimization problem, but I doubt it’s impossible to estimate.)

One thing I haven’t seen that I’d love for someone to try is for teams with roughly interchangeable fourth, fifth, and sixth starters to juggle their pitching assignments each time through the order to get the best possible matchups with respect to park, opponent, and defense. Ground ball pitchers pitch at Comiskey, for instance, and fly ball pitchers start on days when your best outfield is out there. I don’t know how big the impact is, so I don’t want to linger on this point too much, but it seems odd that in the era of shifting we don’t discuss day-to-day adjustments very much.

And that’s all that I’m talking about with this. Defense- and park-adjusted statistics are incredibly valuable tools, but they don’t get you all the way there, and that’s an important thing to keep in mind when you start doing nuanced analyses.

A Little Bit on FIP-ERA Differential

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Brief Summary:

Fielding Independent Pitching (FIP) is a popular alternative to ERA predicated on a pitcher’s strikeout, walk, and home run rates. The extent to which pitchers deserve credit for having FIPs better or worse than ERAs is something that’s poorly understood, though it’s usually acknowledged that certain pitchers do deserve that credit. Given that some of the non-random difference can be attributed to where a pitcher plays because of defense and park effects, I look at pitchers who change teams and consider the year-over-year correlation between their ERA-FIP differentials. I find that the correlation remains and is not meaningfully different from the year-over-year correlation for pitchers that stay on the same team. However, this effect is (confusingly) confounded with innings pitched.

 

After reading this Lewie Pollis article on Baseball Prospectus, I started thinking more about how to look at FIP and other ERA estimators. In particular, he talks about trying to assess how likely it is that a pitcher’s “outperforming his peripherals” (scare quotes mine) is skill or luck. (I plan to run a more conceptual piece on that FIP and other general issues soon.) That also led me to this FanGraphs community post on FIP, which I don’t think is all that great (I think it’s arguing against a straw man) but raises useful points about FIP regardless.

After chewing on all of that, I had an idea that’s simple enough that I was surprised nobody else (that I could find) had studied it before. Do pitchers preserve their FIP-ERA differential when they change teams? My initial hypothesis is that they shouldn’t, at least not to the same extent as pitchers who don’t change teams. After all, in theory (just to make it clear: in theory) most or much of the difference between FIP and ERA should be related to park and defensive effects, which will change dramatically from team to team. (To see an intuitive demonstration of this, look at the range of ERA-FIP values by team over the last decade, where each team has a sample of thousands of innings. The range is half a run, which is substantial.)

Now, this is dramatically oversimplifying things—for one, FIP, despite its name, is going to be affected by defense and park effects, as the FanGraphs post linked above discusses, meaning there are multiple moving parts in this analysis. There’s also the possibility that there’s either selection bias (pitchers who change teams are different from those who remain) or some treatment effect (changing teams alter’s a pitcher’s underlying talent). Overall, though, I still think it’s an interesting question, though you should feel free to disagree.

First, we should frame the question statistically. In this case, the question is: does knowing that a pitcher changed teams give us meaningful new information about his ERA-FIP difference in year 2 above and beyond his ERA-FIP difference in year 1. (From here on out, ERA-FIP difference is going to be E-F, as it is on FanGraphs.)

I used as data all consecutive pitching seasons of at least 80 IP since 1976. I’ll have more about the inning cutoff in a little bit, but I chose 1976 because it’s the beginning of the free agency era. I said that a pitcher changed teams if they played for one team for all of season 1 and another team for all of season 2; if they changed teams midseason in either season, they were removed from the data for most analyses. I had 621 season pairs in the changed group and 3389 in the same team group.

I then looked at the correlation between year 1 and year 2 E-F for the two different groups. For pitchers that didn’t change teams, the correlation is 0.157, which ain’t nothing but isn’t practically useful. In a regression framework, this means that the fraction of variation in year 2 E-F explained by year 1 E-F is about 2.5%, which is almost negligible. For pitchers who changed teams, the correlation is 0.111, which is smaller but I don’t think meaningfully so. (The two correlations are also not statistically significantly different, if you’re curious.)

Looking at year-to-year correlations without adjusting for anything else is a very blunt way of approaching this problem, so I don’t want to read too much into a null result, but I’m still surprised—I would have thought there would be some visible effect. This still highlights one of the problems with the term Fielding Independent Pitching—the fielders changed, but there was still an (extremely noisy) persistent pitcher effect, putting a bit of a lie to the term “independent” (though as before, there are a lot of confounding factors so I don’t want to overstate this). At some point, I’d like to thoroughly examine how much of this result is driven by lucky pitchers getting more opportunities to keep pitching than unlucky ones, so that’s one for the “further research” pile.

I had two other small results that I ran across while crunching these numbers that are tangentially related to the main point:

  1. As I suspected above, there’s something different about pitchers who change teams compared to those who don’t. The average pitcher who didn’t change teams had an E-F of -0.10, meaning they had a better ERA than FIP. The average pitcher who did change teams had an E-F of 0.05, meaning their FIP was better than their ERA. The swing between the two groups is thus 0.15 runs, which over a few thousand pitchers is pretty big. There’s going to be some survivorship bias in this, because having a positive ERA-FIP might be related to having a high ERA, which makes one less likely to pitch 80 innings in the second season and thus more likely to drop out of my data. Regardless, though, that’s a pretty big difference and suggests something odd is happening in the trade and free agency markets.
  2. There’s a strong correlation between innings pitched in both year 1 and year 2 and E-F in year two for both groups of pitchers. Specifically, each 100 innings pitched in year 1 is associated with a 0.1 increase in E-F in year 2, and each 100 innings pitched in year 2 is associated with a 0.2 decrease in E-F in year 2. I can guess that the second one is happening because lower/negative E-F is going to be related to low ERAs, which get you more playing time, but I find the first part pretty confusing. Anyone who has a suggestion for what that means, please let me know.

So, what does this all signify? As I said before, the result isn’t what I expected, but when working with connections that are this tenuous, I don’t think there’s a clear upshot. This research has, however, given me some renewed skepticism about the way FIP is often employed in baseball commentary. I think it’s quite useful in its broad strokes, but it’s such a blunt instrument that I would advise being wary of people who try to draw strong conclusions about its subtleties. The process of writing the article has also churned up some preexisting ideas I had about FIP and the way we talk about baseball stats in general, so stay tuned for those thoughts as well.