Archive for Age

As I noted a few weeks ago, batters who were at least semi-regulars in both 2014 and 2015 were less effective in 2015 than in 2014, as measured by wRC+. That seemed directionally unsurprising — after all, players are subject to aging and regression every year — though the magnitude (an average decline of over five wRC+ points, or over four weighted by plate appearances) was a little higher than I’d expected. Was that decline, I wondered, unusual?

To answer, I calculated the change in wRC+ from one season to the next for players with at least 350 plate appearances in each season. I looked at every year from 1969 (four-team expansion, beginning of divisional play) to the present. (Fine print: I didn’t prorate my results for strike-shortened seasons, and I combined both leagues, with their different DH rules for most of the seasons, in the study. We’re looking at over 10,000 player-seasons, so small variations like the 1994 season and the four years in which the AL didn’t have a DH don’t amount to a lot.) Here are the results, with the second year of the pair of the x axis:

This graph should elicit two responses: (1) it looks as if year-on-year performance is declining, and (2) that is one noisy graph.

So I did another graph, taking the rolling three-year average change instead of the single-year change. Again, the second year of the pair is on the x axis, so 1972 refers to the average change for 1969-70, 1970-71, and 1971-72:

That’s less noisy, but it doesn’t change the conclusion: the year-over-year decline in offensive performance is the steepest it’s been in the nearly 50 years since divisional play began. I’ll use rolling average graphs for the remainder of this article.

The obvious question is: Why? What has changed that’s caused players to be nearly four points worse in terms of wRC+ in recent years when the long-term average decline is less than two, and hovered in a range of 0-2 in most years?

The first possibility that came to mind: Is it an age thing? Are players exhibiting different characteristics based on their year of birth? I divided the batters in my sample into four categories: Young (younger than 25 in the first season of the pair), Prime (25-29), Late Prime (30-34), and Old (35 or older). Here’s the decline in wRC+ for Young players. I used five-year moving averages, since limited sample sizes made the three-year moving averages pretty noisy.

Young players have been getting better, not worse, in consecutive years. That makes intuitive sense: we’d expect batters to improve a bit every year up to their peak in their late 20s. So youngsters aren’t the reason batters appear to be falling off more, year over year.

How about Prime years:

That’s the same scale as the last graph. This is a classic “You can go about your business, move along” graph. There’s been no notable change here. Batters entering their prime years have improved by about 1.5 wRC+ points in consecutive years, year-in, year-out.

Late Prime players:

Now we’re seeing declines, along with more noise. Players under 30, on average, improved their wRC+ from one year to the next. On the other side of 30, we see decline start to set in, to the tune of about a 3.8-point wRC+ average. And it’s gotten worse over the last ten years, rising from an average of about 3.1 in 1986-2005 to 4.1 in 2006-2015.

But we haven’t explained the problem yet. There’s nothing in the prior three graphs that would explain why the decline in wRC+ from one season to the next for semi-regular players has risen by over two points, because none of the prior three age groups has fallen off sharply. One more group left; let’s look at the Old players, 35 and up:

Whoa. That’s pretty dramatic. Year-over year, old players who are semi-regulars are declining a lot more now than they have been at any time since the mid-1970s, when trotting out the fossilized remains of Henry Aaron, Deron Johnson, and Billy Williams to play DH seemed like a good idea. This is the noisiest graph I’ve showed you so far, due to the limited number of older players in the game each year, but the marked climb since the 1990s is unmistakable.

Why is that? What’s happening to guys 35 and older? Nothing exactly leaps out, so here are some possible explanations:

Steroids. Admit it — that’s the first thing you thought. Same here. Fifteen or so years ago, you had all these guys in their late 30s putting up .300/.400/.500 lines with a couple dozen (or more, a lot more) bombs. Or at least it seemed that way. And sure enough, the five-year moving average decline in wRC+ for players aged 35 years or older was below the long-term average decline of about five wRC+ points for all but two years between 1989 and 2004. I think this points to a possibility of chemically-delayed aging patterns that have returned to normal, or perhaps even gotten worse.

More old guys. It’s not a secret that baseball players are better when they’re young than when they’re older. But, as noted above, the Steroid Era featured a lot of old guys hitting the crap out of the ball. Maybe that changed the thinking regarding roster construction, and teams are still carrying a lot of older hitters, even though they’re no longer as effective. Well, here’s a graph showing the percentage of players with 350+ plate appearances per season who were 35 or older.

No, GMs aren’t nostalgic for baseball in the late 1990s and early 2000s. There are fewer older players with regular or semi-regular roles today now than at any time over the past 20 years.

Worse old guys. Maybe the problem is just one of quality. Maybe older players today just aren’t as good as they were in years past. Maybe there was something about babies born in the 1970s. (Disco? The clothes? Watergate?) Here’s a chart showing players who were at least semi-regulars in consecutive seasons, aged 35 or older in their first season, and their wRC+ in their first and second seasons.

Nope, the older guys who’re good enough to get at least 350 plate appearances are still good players. They’re just getting worse faster, as evidenced by the widening gap between the red and yellow lines above.

Amphetamines. In baseball, the term performance-enhancing drugs is synonymous with steroids (and, to a lesser degree, HGH) in the public mind. But the list of banned substances is long, including all manner of illegal recreational drugs and, of relevance here, stimulants. Amphetamines — greenies, in baseball vernacular — have been associated with the game dating back to at least the 1960s. Baseball, of course, has a long season, with many more games than any other North American sport. Amphetamines help players improve reaction time, focus, and ward off fatigue. Those benefits accrue to everyone, of course, but they seem particularly relevant to older athletes, who face the inevitability of the aging process, mentally and physically. The amphetamine ban, which began in 2006, has likely had a larger impact on older players than younger ones. Of course, we’re talking about ten years of amphetamine testing, while the decline in older hitter year-on-year performance has lasted longer, so this can be only a partial explanation.

Sunk costs. Regular readers of FanGraphs are well acquainted with the concept of sunk costs; Dave Cameron has written about it repeatedly. Basically, a team should look at its total payroll as a cost of doing business, then allocate playing time in a manner that optimizes its chances of winning ballgames. That’s theoretical, of course. What actually happens is that teams are often reluctant to put high-salaried players into supporting roles. Take the 2016 Yankees, for example. They have a projected 2016 payroll of $230 million. They’ll spend about three quarters of that amount on nine players, all but one older than 30. Ideally, they should be willing to put CC Sabathia ($25 million in 2016, his age-35 season) in the bullpen, or make a DH platoon out of Mark Teixeira ($22.5 million, 36) and Alex Rodriguez ($20 million in each of 2016 and 2017, 40), or release Carlos Beltran ($15 million, 39) if any of them start particularly slowly. That’s what they might do with a 25-year-old making the major-league minimum. But the payroll obligation makes that move harder, even though that obligation’s a sunk cost — the team has to pay it regardless of how much the player plays. Here are the eight players aged 35 or older who, over the past two years, have suffered a wRC+ decline of 25 or more while retaining at least a semi-regular role, along with their contract status beyond the decline season:

All but Beltre and Byrd were below-average hitters in the second year, arguably not deserving of the plate appearances they received. But all but Suzuki, Utley, and Byrd were due at least eight figures after the year of their large decline. By contrast, a decade earlier, in 2004-2005, there were eleven semi-regular batters who, aged 35 or older, who had a wRC+ decline of 25 or more. Of them, only three — Luis Gonzalez and Jim Edmonds in 2005 and Bret Boone in 2004 — were in the midst of unexpired long-term multi-million-dollar contracts. Small sample size warnings and all, but there was a lot more future money committed to declining old batters in 2014-15 than 2004-05. Maybe those players wouldn’t be getting the plate appearances to meet the 350 threshold if it weren’t for the money that’s owed them.

Fastballs. One of the notable changes in baseball in recent years has been that pitchers throw harder. From 2007 to 2015, per PITCHf/x, the average fastball velocity increased from 91.1 mph to 92.4 mph. The increase was 1.3 mph, to 91.9 mph, for starters and 1.5 mph, to 93.2 mph, for relievers. Older batters can take advantage of their knowledge of the strike zone and pitch sequencing, but maybe they just can’t catch up to some pitches.

Granted, I’m guessing here. I’m leaning towards PEDs, both strength-enhancing and amphetamines, faster fastballs, and a tendency to put high-paid players in the lineup regardless of performance as the key drivers. But I’m not sure. This is an interesting trend, and sufficiently well-established that I don’t think we can write it off as a recent fluke. Something’s going on with players in the second half of their fourth decade that hasn’t happened in a long time.

Ever since I became enamored by the baseball statistical community, I’ve tried to gather as much information as I could. I registered on several websites dedicated to the analysis of baseball statistics such as baseballprospectus.com or FanGraphs.com or HardballTimes.com. I read every book, article I could get my hands on and even tried my hand at producing my own research and analysis in order to achieve two goals in my life: 1. Publish my research and become a savvy baseball analytical mind; and 2. Work within a baseball organization.

My first basic analysis came in the form of three year projections in order to try my hand at fantasy baseball. Personally, I’m proud to say that my first dip within the analytical waters where fruitful as my projections helped me win my league 3 times out of 5 attempts[1]. But, after many years keeping my projections and questions to myself; I’ve finally felt compelled to start more serious research and publish my questions and results online to share with people interested in these topics. So, without further ado, I give you my first serious publication.

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Many readers will often find that writers, commentators and analysts highly value a player before they reach their age 30 season. But, once they pass this mark, players will begin to gradually decline; their production will falter, they’re prone to getting injured more than once within the same season, their speed will begin to abandon them. In other words, the shine begins to disappear and is replaced by a shelled version of a player we, the fans, and managers value. Furthermore, I’ve often read in many articles that players even peak at the age of 27 – this being the season where a player will give his (all-time) best performance before beginning that slow decline into retirement.

Now, I have two problems with this:

1. What stats determine that a player’s best season is his age 27 season?
2. Does this peak age season vary for every position or are all players subjected to the same aging curve?

To answer the first question, I used player statistics starting from 1960 up to 2013 and looked specifically at power numbers – slugging percentage, isolated power and on-base plus slugging[2]. I then calculated each player’s age in accordance with their birthday and how old they would be by June 30^th and took this to be their age-season. Once I had this, I began running histograms in order to determine the lowest performance, highest performance, mean and first and third percentiles.

For this analysis, I only used the data for players who were between 20 and 35 years of aged during any given season. What I found, starting with SLG, was that players – power-wise – don’t reach their peak at 27 but after their 30s. A player’s SLG increases gradually as he gets older until he reaches his age 31-32 season. A player will have a mean SLG of 0.437 by age 27, while, during his age-32 season, the mean SLG will be 0.447 – ten percentile points higher or an increase of 2.3%.

So, as we can see, SLG-wise, a player will show a better performance past his 30^th birthday. But maybe I am biased. Maybe if I checked ISO, we will find different results.

What I found were very similar results. A player’s isolated power, again, on the mean, didn’t peak at age 27. The ISO was 0.159. And, the ISO didn’t peak during the age-32 season but a year earlier during the age 31 season. During this season, ISO was 0.167 while the next season it began to decline at 0.165. ISO increases by 5.0% during those five years.

Finally, I decided to take a look at OPS to see if I could find a similar pattern. Again, players mean OPS peaks during their age 32 season, going from 0.784 at their age 27 season to 0.801 by the time they’re 32. It’s not much of an increase (2.2%) but it’s something.

What I can determine, then, is that a player’s power begins to develop once he hits 27 years of age and will gradually increase right up to when he turns 32. But, after this, his power performance will begin to decline, though not by much.

Another thing that I concluded from looking at these three histograms is that, even though there are gradual increases every season.  Player performance – power-wise – will be fairly consistent from one season to the next. Save for the early seasons (21-25 when a player is still developing), there are no surprising jumps in power[3] from one age to the next. Therefore, though we might prefer younger players for cost control reasons, when we need power production, we can’t fully disregard an older player’s power performance. Chances are they will still produce the same.

***

Having checked how power changes as a player ages, I come to my second question: Does the aging curve differ across positions? Well in football – or soccer for Americans – we have four major positions: striker, midfielder, defense and goalkeeper. Through statistical analysis by Arsenal F.C.’s data department, Arsene Wenger, Arsenal’s manager, found that a players decline varies on the position he plays on the field. That is to say, a striker will age differently than a goalkeeper, and a defender will age different to these two positions.

And, as we all know, work at different positions takes a different toll on a player’s body. Catchers will suffer become more fatigued as a season rolls by than players at any other position; shortstops, as well, have a more demanding position that will require more physical effort. We expect different results from each of the three outfield positions. So, it would be natural that players at different positions age differently on the power curve[4].

What I found out was that my thoughts were correct: positioning on the diamond does affect a player’s power performance but not by much. These are the results based on the mean:

As we can see from the data, first basemen will usually be the first position players to peak. After them, the three outfield positions will peak at age 32. Catchers will then follow suit. Finally, the hot corner will peak at 34 and the middle infield will produce more power by the time they turn 35 than any of their previous years.

What we can conclude from this table is the following; because the demand on power from first base more than defense, players will tend to flex their muscles more often than not; whilst primarily defensive positions such as catcher, second base and shortstop will develop more power later in their careers than when they start off. Outfielders, on the other hand, tend to produce power throughout their careers.

The position that does surprise me is the hot corner. I would have expected third basemen to peak earlier in their careers because most players at the position are power hitters. Then again, there are many good defensive third basemen who aren’t big power players (I’m looking at you Juan Uribe).

***

After reviewing all the numbers, I can safely conclude that as players age, power doesn’t decline. On the contrary, power also increases though not by very much. Furthermore, the gradual increase in power at the plate will vary by position, much like a football – soccer – player’s performance will vary according to his position. Therefore, though we may like young players because of their hustle, cost-control and their energy, it doesn’t hurt to carry a few veterans in the lineup, if not to mentor the young ones, to provide some pop within the lineup.

[1] A small sample size, I admit, but nevertheless, a positive achievement as it encouraged me to delve deeper into baseball analytics.

[2] I didn’t look at OBP as I believe that this stat has more to do with a player’s ability at identifying pitch types, though in retrospect, this can also become better as a player ages and gains more experience.

[3] Though there are many outliers as you can see.

[4] I have charts and charts of histograms for each position measuring SLG, ISO and OPS but since I don’t want to oversaturate with information.

One of Dave Cameron’s articles a while back showed payroll allocations by age groups, and it shows that over the last five years or so more money is going to players in their prime years while less is being spent on players over 30.  That seems to be a logical thing for teams to do, but that trend can only continue for so long.  Eventually a point will be reached where older players are undervalued, and it might be possible that we are already there.

There are several things to keep in mind when comparing these age groups, and one of the biggest is the survivorship bias.  There is a natural attrition over time for players in general.  Let’s look at an example, and for all the following I will be using 2012 versus 2013 as a way to see what happens from year to year.  To look at survivorship, I looked at all position players in 2012 and then their contribution in 2013 to see how many disappeared the next year.  The players that were not in the 2013 year could be due to retirement, demotion, injury, etc.  I also took out a small group that played in both seasons, but were basically non-factors in 2013, for example Wilson Betemit played in both seasons, but in 2013 he only had 10 plate appearances.  The attrition rate for the age groups looks like this:

As you would expect, the attrition rate increases over time.  Players in their late teens and early 20s who make it to the majors are likely to be given opportunities in the near future, but as the age increases the probability of teams giving up on the player, major injury, or retirement goes up.  Players who make it from one group to the next have survived, and that is where the bias comes in.  By the time you get to the 36+ group a significant number of the players are really good because if they weren’t they would not have made it so far.  This ability to survive is also a reason why they should be getting a good chunk of the payroll.  As I will show you, it leads to steady play which teams should pay a premium for.

The next step is looking at performance risk among the groups.  To look at this I took each group’s performance in 2012 and compared it to the group’s performance in 2013, again only with survivors from year to year.  I looked at both wRC+ and WAR just to see if only the hitting component or overall performance behaved differently.

Further, to calculate a risk level I looked at the standard deviations of the differences (2013 minus 2012) for each player, but those are not directly comparable.  Standard deviation is higher for distributions with higher averages due to scaling issues.  For instance, the average 36+ player had a 95 wRC+ in 2012 versus, which is more than 10 wRC+ above the average 18 to 25 year old in the same year.  A 10% drop or increase  in production is therefore a larger absolute change for the 36+ player, so they naturally end up with a higher standard deviation.  To take care of this I calculated the standard deviation of the difference as a % of 2012 average production as the overall riskiness measure.

Don’t compare the wRC+ to WAR figures as there are again scaling issues, but look at the age groups.  A one standard deviation change is most volatile for the youngest age group, so the younger players are the most uncertain or most risky.  That is what we would expect as we have all seen prospects flame out.  The middle two groups are similarly volatile with the 31 to 35 group have a slightly lower risk level in the hitting for this sample and slightly higher overall play according to the WAR risk.  More years might need to be compared to see how consistent those groups are relatively.  The 36+ players are significantly less risky than the other ages.  If they decline by 1 standard deviation it will mean a smaller reduction in performance, less volatile and less risky.

The only thing that really hurts the older players is the aging curve.  They are more likely to see a decline in performance.  From the youngest group to oldest the percent of players who were worse in 2013 than they were in 2012 by wRC+ was 52.3%, 54.5%, 64.4%, 63.6%, and for WAR 52.9%, 48.7%, 56.7%, and 81.8%.  So it is more likely that the older players will see performance worse than the previous year, but again a drop for them will likely be smaller due to lower volatility and it is on average from a higher level of performance to begin with.

Older players are like buying bonds for your investment portfolio, you have a pretty good idea of what there going to pay in the next period with occasional defaults.  Younger players are more like growth stocks, you aren’t sure when or if they are going to pay dividends but when they do you can make huge returns.  Investors pay a premium for bonds (accept a lower rate of return) due to their stability, and teams pay more for older players than maybe their production seems to warrant for the same reason.

If you go back to the payroll allocation, part of the shift is in the number of players in each group.  The 31-35 year-olds no longer get the largest chunk of payroll in part because there are more 26 to 30 year-old players.  Baseball is getting younger overall, so a larger portion of the money going to younger players is inevitable.  The 18 to 25 group isn’t getting a large change in payroll allocation because they are generally under team control, but the teams are extending the players at that age with the money showing up as they get into the next couple age groups.  Like Chris Sale, who is making $3.5 million this year on the extension he signed (he’s 25), but when he is 26, 27, and 28 he will make 6, 9.15, and 12 million respectively.

So the 36+ group, as you can see only 4.7% of the players, used to make about 20% of the total salaries paid, but now they make 15 or 16% (I don’t have Dave’s exact numbers).  Is that premium fair, four times more of the allocation than they make up of the overall player pool?  That is a tough question, and one I am working on.  If anyone can give me tips on how to dump lots of player game logs, that is probably what I am going to do next, but haven’t figured out how to do it without eating up my entire life.  Being more certain on this sort of thing, and having a relative risk measure for players could make contracts a lot easier to understand and predict.