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What a Drag: A Follow-up

Last week I wrote a post showing that there has been a sharp reduction in star players who have passed their 35th birthday. There was a lot of discussion about this on Twitter and elsewhere, mainly focusing on the likely explanations for my data. Most people seemed to believe the largest cause for this trend is “PED testing.” This might be correct, but I was trying to leave the speculation out of it and try to focus on what the data says.

A few people suggested that I should present the data for WAR/PA, rather than just total WAR for each age. I use WAR in studies like this (I have done many such studies showing contributions broken down by race) because I don’t want the replacement-level players to swamp the data. Which they would.

In the 1988-2017 period (30 years), there were 35,913 player-seasons. Here is a plot showing the annual average age, giving all players equal weight.

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The same rise and fall shows up here as I showed last week. With over 1200 players every season, a drop in average age of 0.8 years in the past 12 seasons is fairly dramatic.

Of this huge pool of seasons, 70% of them are fewer than 1.0 WAR, which are (roughly speaking) replacement level. In fact, if you combine these 25,012 seasons together, they sum to less than 0.0 WAR (there is more negative value in this cohort than there is positive value).

To this end, I will “bin” the rest of the data.

Replacement (less than 1.0 WAR): 25,102 total seasons, 69.6%.

Useful (1.0 <= WAR < 3.0): 7210 seasons, 20.1 %

Good (3.0 <= WAR < 7.0): 3401 seasons, 9.5%

Great (7.0 <= WAR): 290 seasons, 0.8%

These bin choices are mostly arbitrary—Tom Tango specifically asked on Twitter whether there are fewer “old” players between 1 and 3 WAR, so I thought I might as well created a few other bins.

Now I will just show the average age of the players in each of these bins.

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For the best cohort (shown in blue) I combined the “good” and “great” seasons, meaning that the line shows all seasons of at least 3.0 WAR. I do this because there are relatively few great seasons, and the “great” line becomes somewhat meaningless.

Although all four cohorts show the same rough trend, the replacement players tend to be younger (at least until recently), and the average age of the good and great cohorts both drop fairly dramatically between 2005 and 2009.

When added to the post from last week, it seems clear that the contributions of older players has shrunk dramatically in the past decade, and this is true across all levels of quality.

Finally, there was some speculation that the data I showed was partly due to teams deliberately playing younger players (to save money).  Its strikes me that the players most likely to be affected by salary-based attrition would be the replacement level players, but this is the part of the roster that has aged the least.  With the important caveat that teams do not know — in advance — how good their players are going to perform, it does not seem as if they are deliberately employing young players any more than they should.

 

 

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Tom Ruane: Fun with Retrosheet Data

Yesterday Tom Ruane posted a note to the Retrosheet mailing list about his latest research: teams who score the most (or the fewest) runs with a specific number of hits.

You can read Tom’s most recent articles here.

Tom has been a Retrosheet volunteer and board member for many years, and over the past decade has written dozens of articles on things he has gleaned from Retrosheet data.

You can see Tom’s archive here.

WARNING: his articles are very addictive.

 

 

 

 

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What A Drag It Is Getting Old

front_6400435Sometime earlier this summer I got to thinking about Miguel Cabrera, and how sad it was that he—like Albert Pujols—had fallen from his rightful and longtime place as one of baseball’s best hitters. Pujols signed a 10-year contract with the Angels after the 2011 season and had his last 4-win season (using bWAR, Baseball-Reference.com’s WAR) in 2012 at age 32. Cabrera put up a great season at age 33 in 2016 followed by two seasons of mediocrity or injury. The Tigers still owe him $154 million for the next five years.

Although their declines seemed inevitable, I got to wondering if players weren’t aging as well as they had 20 years ago. There didn’t seem to be as many good old players as there used to be. I decided to try to figure it out.

Dan Levitt helped me gather the data I needed, namely all the bWAR in major league history broken down by year and by the age of the player who accumulated it. This was enough to answer my questions.

The following chart shows the percentage of WAR contributed by players of every age between 19 and 43. (Younger and older ages, with comparably minimal values, have been removed for simplification.) As usual, a player’s “age” is his age on June 30 of the year in question.

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From 1876 through 2017, 27-year-old players accumulated 8785 WAR, which is 10.19% of the all-time total. This has been the “most valuable” age, but the surrounding years have been comparable – ages 25 to 29 make up nearly half — 48% — of the all-time value. The basic shape of this chart is likely no surprise.

The sum of these age bars will necessarily total 1.0. What I am mainly interested at the moment is the right part of this graph – the older players. Have there been fewer good old players in recent years? For the rest of this paper, I will use the term “old players” to mean “players age 35 or older”. Historically, these “old players” have produced 7.5% of the value in the major leagues.

To simplify things I am going to look at the data since 1968 – 50 years. The next chart shows, for each of these seasons, the percentage of WAR that were accumulated by old players.

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You can see that old player value has been in free fall in recent years. In 2017, age 35+ players accumulated 15.9 WAR in total, just 1.6% of the value in all of baseball. By percentage, this was the smallest total since 1877 when the major leagues were just getting started.

Although most of the annual percentages of WAR by old players falls between roughly 4% and 8%, there are a few exceptions.

There was a brief upsurge of old value in the early 1980s, peaking at 12% of the majors in 1982. Who were these old players? The following table lists all of the age 35+ players who attained 4.0 WAR in 1982:

1982 Player Age WAR
Steve Carlton 37 6.1
Joe Niekro 37 6.1
Al Oliver 35 5.3
Joe Morgan 38 5.1
Jim Palmer 36 4.8
Rod Carew 36 4.7
Tommy John 39 4.2
Hal McRae 36 4.1

Although there are a few position players here, this period was notable for its old pitchers; besides those in this table, players like Don Sutton, Phil Niekro, Tom Seaver, and others had fine “old” seasons in surrounding years.

A much larger and more sustained period of old age success came in the 1998-2007 period, with both its arrival and disappearance happening fairly suddenly.

In the 2002 season old players accounted for 14.2% of all big league value, the highest total since World War 2 upended major league rosters. The list of old players who had 4.0 WAR that season:

2002 Player Age WAR
Barry Bonds 37 11.8
Randy Johnson 38 10.5
Curt Schilling 35 8.5
Larry Walker 35 6.1
Jamie Moyer 39 5.6
Kenny Rogers 37 5.0
Greg Maddux 36 4.6
Rafael Palmeiro 37 4.5

The decade beginning in 1998 not only had an impressive collection of good old players, it also had a lot of GREAT old players. Here is a count of 7+ win seasons for the last three 10-year periods:

1988-1997 1
1998-2007 16
2008-2017 1

The best season (by this measure) put up by an old player in the 1988-1997 period was by … Ed Whitson in 1990.  I am not making this up.

As of today, the last old player to accumulate 7 WAR was Chipper Jones in 2008. It will not happen in 2018.

Here is a list of the 4.0 WAR players in 2017:

2017 Player Age WAR
Nelson Cruz 36 4.1

Cruz just squeaked over the the line, and the only other old player over 3.0 was Adrian Beltre at 3.6.  It is getting tough out there.

The season is not yet over, but I can say with confidence that the only 2018 qualifier will be Justin Verlander, who sits at 5.0 as of September 11.

Another way to show this obvious trend is to look at the average annual age, weighted by WAR. Every player’s WAR value is multiplied by his age, and then divided by the total WAR in the big leagues that season. Again using the past 50 seasons:

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The average age, weighted by WAR, has recently been at its lowest level in 40 years.

 

Setting aside the reasons for the exceptional aging that went on between 1998 and 2007, which has been debated to death, it is interesting to wonder why players are not aging as well today. The most common answer will be “steroids testing,” but could there be other causes?

Could the current game, which values velocity in pitchers and the ability to hit velocity in batters, be more of a “young man’s game”? Could pitching velocity be causing more injuries and therefore shorter careers? Could an historical crop of young stars – Mike Trout, Mookie Betts, Francisco Lindor, Carlos Correa and many more – be temporarily skewing the data?

All of this and more could be true.

How should this effect how the game is managed? In the past 40 years, many or most of the “bad free agent contracts” have come about because teams have signed 30-year-old players and expected them to keep playing the way they played over the previous five seasons. The historically great aging that took place in the 1998-2007 period might have convinced teams that things had changed, that they could finally sign 30-year-olds with confidence. Oops.

The baseball salary system favors older (read: declining) players. Generally speaking, if a player reaches free agency after his Age 29 season, around 70% of his career is likely to be behind him. His first team likely got his entire prime (ages 25-29) at relatively low cost, and the team that signs him will get his expensive, declining seasons.

In the recent off-season there was some controversy because several free agents did not get offers well into the winter. The players claimed collusion, which is certainly possible. But it also could be a (belated) collective understanding of how players are aging. The “solution” to this problem, for the players, is a salary system that rewards players in their 20s instead of in their 30s.

 

A Follow-Up post.

 

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Welcome to the blog for SABR’s Statistical Analysis Committee.  Although our members have published content for decades in a variety of fora, this blog should provide an additional outlet for our members, one which will result in more timely and communal feedback.  Buckle up.