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.


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.


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:


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.



Should Players Hit With Backspin? The Data Might Surprise You

I’ve been researching connections between hitting mechanics and data for a while and wanted to share some surprising findings that I thought you might find interesting.

Hitting with backspin has been a popular, “conventional” objective in hitting for some time. We know from basic physics that a ball hit with backspin travels farther than a ball hit flat or “square.” I developed a model to assess the distance impact from spin based on Statcast data (the method and model are included at the end of this post ). As shown in the table below, high backspin balls result in high BABIP. It is important to note that the data in the following table is based on ball, not player performance (the dataset is balls hit with Exit Velocity >=90MPH and Launch Angle of >=15 degrees).

Ball Performance By Spin Quartile

At the player level, however, square-hitting players significantly outperform high backspin players as evidenced by higher levels of BABIP (.324 vs. .300) and wRC+ (129 vs. 105). The following table is based on Qualified Hitters from 2015-2017).

Player Level - Backspin vs. Performance

Wow! So high backspin balls by themselves outperform, but the players who hit high backspin balls more often actually underperform? That seems crazy! Actually, when you consider that hitting a ball with backspin requires greater precision in order to hit the bottom half of the ball just right, it’s really not all that surprising. The distance difference between the groups is considerable. The square hitting group had slightly higher EV as well as three degrees of additional loft and should have had a distance advantage of approximately 20 feet; however, the average distance of the square-hitting group was actually eight feet less than the high backspin group. This opposite performance relationship between balls and players is shown in the chart below for each backspin quartile:

Backspin Ball vs. Player Performance

Clearly, at the player level, there is a “cost” side of the equation that needs to be considered. Thus, players cannot simply choose to hit only the “good” backspin balls – they must accept the full distribution of results that come along with that strategy. The spin impact can be seen in the following chart of hits for both player groups over the 2015-2017 seasons.

Spin Groups & Unexpected Distance

The spin impact for both player groups as shown above indicates that there is a spin-type “tendency” at the player level. Additionally, over the examination period, only one player switched groups, confirming that the player/spin relationship is not random. As suggested in the chart above, the horizontal angle of the hit reflects the type of spin (i.e., backspin vs. sidespin) which has a significant influence on distance (see model here for additional detail).

Although the R2 between spin and wRC+ is not very high maxing out at .17 (for the Qualified Player dataset), the outliers are quite remarkable. In fact, of all the extremely high performing players (wRC+ >135), none are hitting with high levels of backspin. Similarly, of all the very low performing players (wRC+ <80), none are hitting the ball with low levels of relative spin. The dataset below includes players with at least 200 PAs each year for 2015-2017.

Information in the Outliers

I was curious how spin compared to exit velocity as a performance factor. After all, EV is widely considered as one of the best performance related metrics. It turns out that spin-related performance for players with high levels of plate appearances (PA) is indeed significant based on an examination of the top and bottom quartiles for both EV and spin (inclusive player membership required for all years from 2015-2017).

Exit Velocity vs. Spin

Not only did players in the top quartile, flat-hitting group outperform the top quartile, high EV hitters given high plate appearances (PAs), the performance difference between the top and bottom quartiles was greater for the square-hitting group. As PAs increase, the “noise” of the short-term outperformance of backspin is essentially extracted, revealing the greater value of a square hitting approach.

Without question, EV has a strong connection to performance; however, the ability of players to influence EV is limited due to physical size, strength and swing speed; consequently, players likely have more upside by switching from a backspin to “square” approach than attempting to increase EV.

I had a hunch that smaller players might be tapping into the backspin-driven distance gains – indeed they are!

Player Size vs. Spin

This is quite remarkable. The smaller players are consistently utilizing more backspin and are hitting the ball farther despite both lower launch angles and exit velocities. In terms of why the smaller players are paying such a high “price” for the incremental distance, I’d be interested to hear your thoughts. Here are just a few that I’ve come up with:

  • Whether consciously or subconsciously, players learn that hitting with backspin increases distance. Since the larger players generally have more natural power, they haven’t needed to use backspin to “keep up” with their peers in terms of distance. The data suggests the smaller players may be blinded to the “cost” side of the equation, and are focused more on the extra distance. Maybe human nature in seeing what we want to see?
  • It could also be a selection issue where distance is being incorrectly viewed as “power” for the smaller players and those players are being promoted through the various levels of baseball.
  • Is the typical pre-game batting practice where many players go for home runs causing or contributing to the issue? Ego is a very real issue and the typical batting practice sessions may be unknowingly changing the swing paths of the smaller hitters to generate more backspin. I noticed the other day that Tony Kemp with the Astros (a smaller player) is now avoiding all pre-game, on-field hitting because he doesn’t want to be tempted to “swing for the fences”. Without spin data at lower levels of play, however, it is difficult to know when, in the course of the smaller player’s career, spin is being added.


Given that “hit with backspin” has been part of consensus views for some time, this advice is not merely ineffective but it is actually performance-detracting. What’s more, significant improvement may be possible for players who are in the high backspin group and simply reconsider the “truth” of backspin.

If there seems to be interest in the topic, I will submit a follow-up post regarding the specific mechanical differences, based on data, of “how” players are hitting the ball square – the findings are equally surprising.


Statistical Baseball Research Bibliography

Hi everyone – I’ve attached the latest version of my Statistical Baseball Bibliography, along with instructions for using it.  I am in the middle of a major revision of it, including a revised classification scheme, and when you see entries in bold that means I’ve not yet had to time to reclassify them.  I hope you find this interesting and useful.  If any of you need a particular article for your work, let me know and I can probably get it for you.  I will have more material to share with you in a few weeks.




defense, Statcast

The Future Of Defensive Analysis

Statcast Lab: Outfield fielding components of Reaction, Route, Burst, Speed

The current state of defensive metrics is “pretty good”, in evaluating who the best defenders are.  There are mixed views on that, but largely the metrics used to generate the SABR Defensive Index (SDI) are aligned with Statcast’s OAA (to date) by player.  Baseball Info Solutions’ DRS (for most of this blog, I will just reference DRS, since UZR uses the same data) and RED, based on STATS’ ZR data, both do a good job of describing turning batted balls into out.

From the information Tom Tango talks about here, we will be able to understand *why* players are better or worse at turning batted balls into outs.  Which players get the best jumps.  Which players take the best route.  What is the spread of those talents and how predictable is it.

What I have seen (though not fully studied) is players come into the league and learn to adjust to the speed of the game, performing around or below average.  In their second, third, fourth seasons, they use their youth – speed, reflexes, reaction – to maximize the balls they turn into outs, peaking in their runs saved early, and then begin the decline into designated hitter, or just retirement.

The defensive aging curve is not dissimilar to the offensive aging curve with respect to shape. However, it is a couple of years earlier.  Defense is a younger man’s game because much of it relies on speed and reaction.  As more data rolls out of Statcast at baseball savant (nee Daren Willman’s site), we’ll be better at understanding how a player succeeds, and probably better predicting whether he will continue to do so.



The Length of World Series Games

There has recently been a lot of discussion over the length of baseball games. But to really understand the problem or even decide if there is one, we first need to put it in context and examine what’s behind the increase. To that end, I have looked at changes in World Series games over the past 100 years.

In SABR’s 2000 Baseball Research Journal I highlighted changes in pitch counts based on data I found in the Spalding Guide for the 1919 World Series. I have since found pitch count data for the 1916 World Series in The Sporting News. With this information and wanting to include recent seasons (particularly given the recent shifts that seem to have occurred after the 2015 All-star game), I thought it might be interesting to compare averages over five year periods. Accordingly, I averaged the World Series data over the years ending in five through nine, including only those seasons where I found complete statistics (using baseball-reference.com). The most recent averages, therefore, cover 2015 to 2017, while 1915 to 1919 includes only 1916 and 1919.

Here’s the evolution of World Series game times over the past century:

1915/19 1:56
1975/79 2:41
1985/89 3:05
1995/99 3:18
2005/09 3:31
2015/19 3:34

A century ago a World Series game lasted right around two hours. By the late 1970s it had increased to slightly over two and a half hours. The late 1980s saw World Series games finally break the three hour mark, on average. Today a World Series game lasts just over three and a half hours. Thus, over the last 100 years, the average time of a World Series game has nearly doubled, increasing by 85%.

World Series data is not fully representative of the regular season in that teams obviously manage differently in a short, winner-take-all series than over the grind of a regular season. Today, regular season games average closer to three hours than three and a half, but comparing World Series games against each other offers an interesting and valid look at trends over time.

Defining the number of pitches in a game as the “clock,” two possible explanations exist for the increase in game times: more pitches per game and/or fewer pitches per minute. Let’s look at the number of pitches per game first.

1915/19 116
1975/79 130
1985/89 141
1995/99 149
2005/09 147
2015/19 145

The number of pitches per game has clearly surged from 100 years ago. From the teens of the twentieth century through the late 1990s, the number of pitches per game increased by around 30—roughly 25%—and has held relatively steady since. As an aside, this has interesting connotations when comparing pitcher workloads over time. Assuming workload is closely tied to pitches per game, Corey Kluber or Max Scherzer tossing 7 1/3 innings today is equivalent to a complete game out of Walter Johnson or Grover Cleveland Alexander a century ago.

At a macro level, there are only two ways for the number of pitches per game to rise: an increase in the number of batters faced per game and/or an increase in the number of pitches per batter. In fact, as the table below makes clear, both have occurred. As the run scoring environment jumped at the end of the Deadball Era around 1920, a pitcher would have to face more batters to get his three outs in an inning. More recently, however, over the past 20 years as a smaller percentage of runs are accounted for by sequentially generated offence of multiple hits and more through home runs, the number of batters faced per game has come back down.

While the increase in BFP per game is meaningful, most of the increase in pitches per game can be attributed to an increase in pitches per batter. Pitchers have been going deeper into counts with each hitter, highlighted by the recent increase in strikeouts.

1915/19 36.4 7.4% 9.9% 17.4%
1975/79 37.9 8.1% 14.0% 22.0%
1985/89 38.8 8.6% 17.1% 25.7%
1995/99 39.2 11.5% 17.0% 28.5%
2005/09 38.6 9.4% 20.3% 29.8%
2015/19 37.4 8.1% 22.5% 30.6%

In the World Series over the last three years, just over 30% of each plate appearance ended in a walk or a strikeout. Clearly, the average plate appearance ends deeper in the count today than it did thirty years ago and much later than it did a century ago.

The 25% increase in pitches per game—from both the increase in BFP per game and the number of pitches per batter—does not fully account for the fact that game times have increased by 85%. The second possibility, pitches per minute, shows an even more dramatic shift, highlighted in the table below.

1915/19 2.01
1975/79 1.62
1985/89 1.52
1995/99 1.51
2005/09 1.40
2015/19 1.36

Over the last century there has been a significant and steady decrease in the number of pitches per minute during a World Series game. One hundred years ago there were roughly two pitches per minute when averaged over the length of a game. Today this has fallen to 1.36.

In sum, game lengths have expanded as pitchers have gone deeper into counts and the time between pitches and innings has risen. Quantifying these causes helps provide a framework into how we might roll back game lengths without affecting their watchability or integrity. Reducing the number of pitches per game is likely a much more difficult or intrusive challenge than reducing the time between pitches. Any change to the number of strikeouts and walks will require a fundamental change to the way the batter/pitcher matchup is now approached by each. The huge number of strikeouts has been receiving a large amount of scrutiny recently due to the negative aesthetic of pitches not being put in play. Any rule change that transforms the current approach and leads to a decrease in strikeouts in favor of balls being put in play will likely also decrease the times of games, at least at the margins.

My sense is that there is more low hanging fruit on the number of pitches per minute front. Limiting mound visits this year was one relatively easy action that should be having at least a marginal impact. There are many other possibilities that have been floated as well, such as a pitch clock, limiting time between innings, and regulating the batter’s ability to step in out of the batter’s box, as well as more radical ideas.

A century ago a fan’s time commitment for a World Series game resembled what now might be required for a college basketball game. A World Series game today requires a time commitment closer to a college football game. While that might be acceptable for a short, highly intense series, having a 162 game season with game lengths approaching the time required to play out the spectacle of a college football Saturday—without the requisite increase in on-field action—is a recipe for a shrinking interest in our national game.