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.

The 2013 season was a banner season for players going on the disabled list. The DL was utilized 2,538 times, which was 17 more than the previous 2008 high. In all, players spent 29,504 days on the DL which is 363 days more than in 2007. Today, I take a quick look at the 2013 DL data and how it compares to previous seasons.

To get the DL data, I used MLB’s Transaction data. After wasting too many hours going through the data by hand, I have the completed dataset available for public consumption.  Enjoy it, along with the DL data from previous seasons. Finally, please let me know of any discrepancies so I can make any corrections.

With the data, it is time to create some graphs. As stated previously, the 2013 season set all-time marks in days lost and stints. Graphically, here is how the data has trended since 2002:

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Yesterday, the names of the World Series umpires were released, with John Hirshbeck serving as the crew chief. Like I have done for the first two rounds in the playoffs, I will examine each umpire’s strike and ball calling tendencies. Overall, the group is pretty solid, with the exception of Bill Miller, who calls one of the league’s largest strike zones.

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After examining the National League Championship Series umpires yesterday, I will look at the American League Championship Series umpires today. Even though the ALCS umpire crew is led by fan “favorite” Joe West, they are generally neutral in their strike calling.

For each umpire, I have include their 3-year average K%, BB% and Zone% for both left-handed and right-handed hitters. To get the Zone%, I looked at the number of called strikes and balls in the league average called strike zone. The strike zone used is the same one that is used for FanGraphs hitter and pitcher Pitchf/x Zone% values.

Also, I have created a 100 scale which shows how much more or less an umpire’s values are compared to the league average. A value over 100 is always pitcher friendly (a lower BB% means a higher value).

Additionally, I have included a heat map of the umpire’s called strike zone compared to the league average zone. It subtracts the percentage of called strikes divided by the total of the called balls and strikes of the umpire from the league average. For example, if the umpire called a pitch in the zone a strike 40% of the time and if the league average is 50%, the output would be -10% (40%-50%) or 0.10.

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With all the Division Series now over, it now time to give a quick look at the League Championship Series umpires. I will look at the NLCS umpires today and the ALCS umpires tomorrow.

For each umpire, I have include their 3-year average K%, BB% and Zone% for both left-handed and right-handed hitters. I have created a 100 scale which shows how much more or less an umpire is than the league average. A value over 100 is always pitcher friendly (a lower BB% means a higher 100 value).

Additionally, I have included a heat map of the umpire’s called strike zone compared to the league average zone. It subtracts the percentage of called strikes divided by the total of the called balls and strikes of the umpire from the league average. For example, if the umpire called a pitch in the zone a strike 40% of the time and if the league average is 50%, the output would be -10% (40%-50%) or 0.10.

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After examining the National League division round umpires yesterday, I will look at the American ones today. I will look to see if they have any unique strike calling patterns and post their 2013 K/9 and BB/9 scaled to the league average strikeout and walk rates. Again I have included images of their called strike zones compared to the league average called zone.

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It is time to look at the third team on the field for the National League division round, the umpires. Each umpire is given a quick look to see if they have any unique strike calling patterns. Also, I posted their 2013 K/9 and BB/9 rates which I scaled them to the league average strikeout and walk rates. A 100 value is league average and a 110 value would be a value 10% higher than the average. Additionally, I added images of their called strike zones verses right and left handed hitters (from catchers perspective) compared to the league average. The scale is the percentage difference where -0.1 means 10% points less than the league average

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Here is a quick look at the called strike zone and strikeout and walk rates for the three home plate umpires over the next three nights.

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On Tuesday, Starling Marte got his first start in more than a month. To no one’s surprise — at least to those who follow the Pirates — he got hit by a pitch. It was his 22nd hit-by-pitch this season, the second-most behind Cincinnati’s Shin-Soo Choo. Prior to his start this week, Marte had been absent from the Pirates lineup since Aug. 18 — a day after he was hit in the hand. While some players get hit all the time, it looks like Marte might be playing an active role. In fact, it appears he’s getting hit when he’s close to striking out. And if that’s true, the strategy looks to have cost him at least a month’s production.

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