Back in the old days, like WAY back in the old days, the running game was an integral part to baseball. I mean, look at the stolen base totals from the 19th century. Dudes were running wild. That was how the game was back then. Hitters didn’t (or couldn’t, more likely) rely on power nearly as much, and the pitchers threw a lot softer, making it easier on the potential thieves. Base stealing was incubated by the culture of baseball at the time. The way baseball is played now would seem entirely foreign to fans in the 1890s.

Since those high-flying days of the Grover Cleveland administration, aggression on the base paths died down. It saw its peaks and valleys, certainly, but swiping bags never quite reached that pinnacle of the early days of the game. The most recent influx of base-stealing came in the late 1970s through the 1980s. These were the times of Lou Brock and Vince Coleman and Rickey Henderson. And we applauded these speedsters. Running became a valued part of the game again. Though running saw a decline over the 90s as power came to prominence, it’s still a much speedier game than it was in those slow-poke days of the 60s. Read the rest of this entry »

A few weeks ago I noticed hitters no longer improved, but instead their production only declined as they aged. Additionally, I answered at few questions on the first article in a second article. Today, I am going to look at the individual hitter traits to see which ones may be leading to the early decline.

First off, I will start off with some disclaimers.

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At the end of the 1972 season, Steve Carlton was awarded the NL Cy Young award for his efforts with the Philadelphia Phillies. He certainly earned it. He posted a 12.1 WAR, a 22% strikeout rate, and an ERA- of 56. What many voters and fans were looking at, however, was his wins — 27, the most in the National League.

That number is impressive for a different reason to my father. Steve Carlton’s 27 wins in 1972 are important to my dad because it is included in one of his favorite baseball tales, a not-so-secret weapon for dads — Steve Carlton won 27 games in 1972, the same year the Phillies won all of 59. For those keeping score at home, that means that Steve Carlton was awarded a pitcher win in 46% of the games that the Phillies won all together.

This is more in the “fun fact” category than the “useful information” one, but it’s an eyebrow raiser nonetheless. Still, I needed to set the record straight and, during the 14th or 15th time my dad relayed his trivia nugget I, politely, mentioned that pitcher wins perhaps aren’t as important of a stat as we once thought. This led to a discussion involving all the usual stuff statistically-minded people mention when discussing wins. We sort of agreed to disagree, I guess, but something still stuck with me.

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For five years now, Bronson Arroyo has been better than his peripherals. Since 2009, only three pitchers have a bigger gap between their fielding independent numbers and their ERA, and those three didn’t come close to pitching as many innings. It’s tempting to say the free agent 36-year-old has figured something out… but what has he figured out, exactly? How has he become more than the sum of his parts? It has to be more than a whimsical leg kick.

Let’s use some basic peripherals to find comparable pitchers. His fastball struggles to break 90 mph, he doesn’t strike many out, and he doesn’t have great worm-burning stuff — but the control has been elite. Here are a few other pitchers that fit that sort of mold.
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We know that Billy Beane has joked before that his stuff doesn’t work in the playoffs. And we know that, at least this time around, his Athletics team is built on depth and getting value out of the back end of his roster. These things seem to go hand in hand: your sixth starter and sixth infielder may mean a lot during the season, and they may not even make your post-season roster. But can we study this more rigorously in an effort to estimate the true value of depth?

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A few days ago, I looked at the possibility of major league hitters no longer showing any hitting improvement, on average, once they debut in the majors. I believe both the banning of PEDs and teams being able to evaluate MLB ready talent are the keys to this change.

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For years it’s been assumed hitters will get to the major leagues and peak offensively around age 30. Teams and fans can hope the new, shiny, 20-home-run-hitting rookie will improve over time and someday will hit 30 to 40 home runs. Hitters were expected to improve until their late twenties and then begin to decline. But recent data show there’s no longer a hitting-peak age. Instead, hitters arrive at their peak and simply decline with age.

I pretty much stumbled on this finding a few days ago. I created an stolen base aging curve for Mike Podhorzer and then created one for home runs. I separated the data into pre- and post-PED ban eras, the latter of which happened between the 2005 and 2006 seasons. It didn’t surprise me to see a slow decline in the home run curve during the PED era. My biggest surprise was the post-PED data where home runs no longer peaked, they only declined. I examined just about every overall offensive stat (OPS and wOBA, to name a couple) and found the same thing: Hitters no longer peaked, they only declined. Here’s a look at the wOBA aging curve from pre- and post-PED ban eras, along with a note on how the curves were created.

Note: The aging curve was created by the delta method by weighting plate appearances using their harmonic means. With this method, there’s a small survivor bias summarized by Mitchel Lichtman at the Hardball Times:

… survivor bias, an inherent defect in the delta method, which is that the pool of players who see the light of day at the end of a season (and live to play another day the following year) tend to have gotten lucky in Year 1 and will see a “false” drop in Year 2 even if their true talent were to remain the same. This survivor bias will tend to push down the overall peak age and magnify the decrease in performance (or mitigate the increase) at all age intervals.

For 20 seasons, hitter production began to decline significantly around age 30. Over the past seven seasons, the decline has occurred immediately.

A problem exist when using wOBA in the recent lower scoring environment. The league wOBA in 2006 was .337, and in 2013 it was at .318. That’s a drop of 19 points in seven seasons, or 2.7 points  per season. Players will have the appearance of aging from season to season.

Hitting (wOBA) has been on the decline for several reasons. Teams have been better at evaluating players’ defense abilities and deploying better defensive alignments in the field. Also, the quality and quantity of hard-throwing relief pitchers has increased across the league. Finally, 2006 was the first full season with the harsher PED punishments (from 50-game suspensions to 100-games suspensions t0 lifetime bans). This overall decline leads to a large year-to-year aging factor. The recent decline in offense led me to create aging curves with wRC+, which is weighted to the season’s, the league’s and the park’s run-scoring environment. I ran the aging curve to look at four, seven-year time frames.

With wRC+, the most recent aging curve doesn’t immediately begin declining like the wOBA curve. Instead, it remains constant until it begins to decline. The decline starts at the same point when previous players began declining (between age 25 to 26 season). The curve shape is the same for pitcher aging curves: no up and down, just constant and then down. Additionally, the most recent rate of decline is almost the same as the pre-PED aging rate (82-89).

This information is important in predicting young players’ performance. Once a hitter makes it to the majors, he doesn’t really improve. In the past, people used to hope for improvement and growth as the player aged. These days, people should expect to see the player performing at his career best immediately.

A couple possible reasons may be behind the lack of improvement. First, players are more prepared for majors, physically and mentally. In the past, a player may not have had the best conditioning, coaching and training while he was in the minors. Teams are putting more resources into their minor league affiliates, and there isn’t room for improvement with the major league team. Second, teams may be better at knowing if or when a player will be MLB ready, meaning the player doesn’t have to mature and grow at a lower level. They are ready to contribute immediately

This trend of contributing right away may have been occurring before 2006. The uncontrolled use of PEDs may have masked the lack of an up and down curve. Players were improving chemically past their previous peak and were able to maintain their performance over time.

For years, pitcher performance declined as those players aged, but hitters seemed to have an up and down performance curve. In the past few seasons, hitters no longer improve once they arrive in the majors. Instead, their performance is constant until they begin to decline, which, on average, is at 26 years old. Improved training and development is probably behind the shift. If fans are hoping for a young position player’s performance to peak, they might be sorely disappointed. Chances are the player is likely producing at his career-best already.

In doing my research, I often like to take a look at correlations to get an idea about whether factors might be connected.  At the end of this season, I put together a spreadsheet to help me with that.  Well, I haven’t finished the research yet (FG+ subscribers will probably soon find out what’s been keeping me from it), but in the meantime, I thought I’d share what I hope will be a pretty handy tool for whomever out there might be interested in what lies a little beneath the surface of all these stats on FanGraphs.  And I do mean all of them.  Any pitching-related stat on FanGraphs should be represented in this tool.  You can compare one stat to another, or to itself in a different year.  Or, what the heck, you can even compare a stat to a different stat in a different year.  And, for you sticklers out there, it will even give you a confidence interval on these correlations (by default, it gives you the range of correlations that the true correlation has a 95% chance of being within).

What can you do with this?  Well, let’s say you want to see whether a stat is predictive of the next year’s ERA.  You could, for example, set Stat 1 to K% (after selecting the correct white box, type it in, or select from the drop-down list via the arrow to the right of the box), with the year set to 0 (meaning the present year), then set Stat 2 to ERA, with the year set to 1 (meaning the next year).  If you don’t change the IP or Season filters, you should see a correlation of -0.375.  That shows there’s a pretty decent connection between the two stats, in that if a pitcher has a high strikeout percentage in one season, he’ll likely have a low ERA the next (relative to the rest of the pitchers in the comparison).  If you change the year under ERA to 0, you’ll see the correlation gets stronger, whereas if you change it to 2 or 3, you’ll see it gets weaker.  That has a lot to do with the unpredictability of K%, and especially of ERA.  You’ll notice if you compare year 0 K% to year 1 K%, the correlation is a very strong 0.702, whereas if you do the same for ERA, it’s a moderate-to-weak 0.311.  Hopefully the graph will give you an idea of how strong those connections really are.
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I imagine that, for a front office exec, there’s nothing quite like the buzz you get from picking up another team’s non-tender and getting value from that player. Maybe it’s just ‘one man’s ceiling is another man’s floor,’ but in a business where one sector of the market has to continually work to find value in surprising places, it’s an important moment.

But is there much success to be found in the bargain bin? These are players that their own team has given up on — and we have some evidence that teams know more about their own players than the rest of the league, and that players that are re-signed are more successful. What can we learn from the successes and failures that we’ve seen in the past?

To answer that question, I loaded all the non-tendered players since 2007 into a database and looked at their pre- and post-non-tender numbers.

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Knock on wood, I certainly hope so. This piece isn’t about sending a tribute to the area, rather it is a discussion of the composition of the minor leagues and those who reach the major leagues.

While this article became a study of a the California League’s population, the concept began when I was thinking about Jake Lamb’s prospect status. Lamb signed with the Diamondbacks last June and I stumbled upon him during his first Spring Training with the club — he ranked among the 10 best prospects I saw in Arizona. Intrigued, I followed his injury-riddled season closely and thought he would never garner the attention I believed he deserved because of his old age and collegiate pedigree (though, Hulet ranked him higher than anyone else this off season!).  Suddenly, I found myself buried in Excel attempting to discover what Jake Lamb’s chances were to become a major leaguer.

Statistical studies of prospects are difficult because the minor leagues are vast and rife with variables and failure. There are 189 teams across 16 full-season, short season and rookie leagues, each stocked with talent that may never make a major league 25-man roster. With over 5,000 minor leaguers vying for 750 MLB roster spots it can be easier to study the successes.

Studying only the players who reach the major leagues may be easier, but often such studies snag on “survivorship bias.” Survivorship bias may be present when a study’s population consists of a select group amongst a larger class. If one is going to study success, it’s wise to study failure too. For a demonstration of survivorship bias, read Dave Cameron’s post on The Value of Hunter Pence.

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