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Last week I wrote a post showing that there has been a sharp reduction in star players who have passed their 35^th 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.

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.

 

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.

 

 

SABR Friends,

This next Thursday, Sept. 20th,  I will be co-hosting a webinar entitled, The Mindset Science of Playing at the Next Level. It is sponsored by my new venture, Diamond Scouting, Psychometrics. Todd Thomas, Diamond’s Director of Scouting, will be hosting the event.

The many benefits of psychometric scouting and player evaluation will be covered. We will explain how quantifying player makeup, mindset, and instincts can easily identify future All-Stars, that could be overlooked or missed altogether.

The Webinar is free and I would be honored to have the SABR Statistical Analysis Committee join in and share in the discussion. Please pass the word. I am looking forward to having you and the members join us. Here is the link to register, https://t.co/tj4EOuVT5K.

Hope to see you there,

Bill Bagley
SABR Member
Psychometrician

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.

 

 

 

 

Sometime 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.

 

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).

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).

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:

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.

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.

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).

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!

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.

Conclusion

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.