As I noted a few weeks ago, batters who were at least semi-regulars in both 2014 and 2015 were less effective in 2015 than in 2014, as measured by wRC+. That seemed directionally unsurprising — after all, players are subject to aging and regression every year — though the magnitude (an average decline of over five wRC+ points, or over four weighted by plate appearances) was a little higher than I’d expected. Was that decline, I wondered, unusual?

To answer, I calculated the change in wRC+ from one season to the next for players with at least 350 plate appearances in each season. I looked at every year from 1969 (four-team expansion, beginning of divisional play) to the present. (Fine print: I didn’t prorate my results for strike-shortened seasons, and I combined both leagues, with their different DH rules for most of the seasons, in the study. We’re looking at over 10,000 player-seasons, so small variations like the 1994 season and the four years in which the AL didn’t have a DH don’t amount to a lot.) Here are the results, with the second year of the pair of the x axis:

This graph should elicit two responses: (1) it looks as if year-on-year performance is declining, and (2) that is one noisy graph.

So I did another graph, taking the rolling three-year average change instead of the single-year change. Again, the second year of the pair is on the x axis, so 1972 refers to the average change for 1969-70, 1970-71, and 1971-72:

That’s less noisy, but it doesn’t change the conclusion: the year-over-year decline in offensive performance is the steepest it’s been in the nearly 50 years since divisional play began. I’ll use rolling average graphs for the remainder of this article.

The obvious question is: Why? What has changed that’s caused players to be nearly four points worse in terms of wRC+ in recent years when the long-term average decline is less than two, and hovered in a range of 0-2 in most years?

The first possibility that came to mind: Is it an age thing? Are players exhibiting different characteristics based on their year of birth? I divided the batters in my sample into four categories: Young (younger than 25 in the first season of the pair), Prime (25-29), Late Prime (30-34), and Old (35 or older). Here’s the decline in wRC+ for Young players. I used five-year moving averages, since limited sample sizes made the three-year moving averages pretty noisy.

Young players have been getting better, not worse, in consecutive years. That makes intuitive sense: we’d expect batters to improve a bit every year up to their peak in their late 20s. So youngsters aren’t the reason batters appear to be falling off more, year over year.

How about Prime years:

That’s the same scale as the last graph. This is a classic “You can go about your business, move along” graph. There’s been no notable change here. Batters entering their prime years have improved by about 1.5 wRC+ points in consecutive years, year-in, year-out.

Late Prime players:

Now we’re seeing declines, along with more noise. Players under 30, on average, improved their wRC+ from one year to the next. On the other side of 30, we see decline start to set in, to the tune of about a 3.8-point wRC+ average. And it’s gotten worse over the last ten years, rising from an average of about 3.1 in 1986-2005 to 4.1 in 2006-2015.

But we haven’t explained the problem yet. There’s nothing in the prior three graphs that would explain why the decline in wRC+ from one season to the next for semi-regular players has risen by over two points, because none of the prior three age groups has fallen off sharply. One more group left; let’s look at the Old players, 35 and up:

Whoa. That’s pretty dramatic. Year-over year, old players who are semi-regulars are declining a lot more now than they have been at any time since the mid-1970s, when trotting out the fossilized remains of Henry Aaron, Deron Johnson, and Billy Williams to play DH seemed like a good idea. This is the noisiest graph I’ve showed you so far, due to the limited number of older players in the game each year, but the marked climb since the 1990s is unmistakable.

Why is that? What’s happening to guys 35 and older? Nothing exactly leaps out, so here are some possible explanations:

Steroids. Admit it — that’s the first thing you thought. Same here. Fifteen or so years ago, you had all these guys in their late 30s putting up .300/.400/.500 lines with a couple dozen (or more, a lot more) bombs. Or at least it seemed that way. And sure enough, the five-year moving average decline in wRC+ for players aged 35 years or older was below the long-term average decline of about five wRC+ points for all but two years between 1989 and 2004. I think this points to a possibility of chemically-delayed aging patterns that have returned to normal, or perhaps even gotten worse.

More old guys. It’s not a secret that baseball players are better when they’re young than when they’re older. But, as noted above, the Steroid Era featured a lot of old guys hitting the crap out of the ball. Maybe that changed the thinking regarding roster construction, and teams are still carrying a lot of older hitters, even though they’re no longer as effective. Well, here’s a graph showing the percentage of players with 350+ plate appearances per season who were 35 or older.

No, GMs aren’t nostalgic for baseball in the late 1990s and early 2000s. There are fewer older players with regular or semi-regular roles today now than at any time over the past 20 years.

Worse old guys. Maybe the problem is just one of quality. Maybe older players today just aren’t as good as they were in years past. Maybe there was something about babies born in the 1970s. (Disco? The clothes? Watergate?) Here’s a chart showing players who were at least semi-regulars in consecutive seasons, aged 35 or older in their first season, and their wRC+ in their first and second seasons.

Nope, the older guys who’re good enough to get at least 350 plate appearances are still good players. They’re just getting worse faster, as evidenced by the widening gap between the red and yellow lines above.

Amphetamines. In baseball, the term performance-enhancing drugs is synonymous with steroids (and, to a lesser degree, HGH) in the public mind. But the list of banned substances is long, including all manner of illegal recreational drugs and, of relevance here, stimulants. Amphetamines — greenies, in baseball vernacular — have been associated with the game dating back to at least the 1960s. Baseball, of course, has a long season, with many more games than any other North American sport. Amphetamines help players improve reaction time, focus, and ward off fatigue. Those benefits accrue to everyone, of course, but they seem particularly relevant to older athletes, who face the inevitability of the aging process, mentally and physically. The amphetamine ban, which began in 2006, has likely had a larger impact on older players than younger ones. Of course, we’re talking about ten years of amphetamine testing, while the decline in older hitter year-on-year performance has lasted longer, so this can be only a partial explanation.

Sunk costs. Regular readers of FanGraphs are well acquainted with the concept of sunk costs; Dave Cameron has written about it repeatedly. Basically, a team should look at its total payroll as a cost of doing business, then allocate playing time in a manner that optimizes its chances of winning ballgames. That’s theoretical, of course. What actually happens is that teams are often reluctant to put high-salaried players into supporting roles. Take the 2016 Yankees, for example. They have a projected 2016 payroll of $230 million. They’ll spend about three quarters of that amount on nine players, all but one older than 30. Ideally, they should be willing to put CC Sabathia ($25 million in 2016, his age-35 season) in the bullpen, or make a DH platoon out of Mark Teixeira ($22.5 million, 36) and Alex Rodriguez ($20 million in each of 2016 and 2017, 40), or release Carlos Beltran ($15 million, 39) if any of them start particularly slowly. That’s what they might do with a 25-year-old making the major-league minimum. But the payroll obligation makes that move harder, even though that obligation’s a sunk cost — the team has to pay it regardless of how much the player plays. Here are the eight players aged 35 or older who, over the past two years, have suffered a wRC+ decline of 25 or more while retaining at least a semi-regular role, along with their contract status beyond the decline season:

All but Beltre and Byrd were below-average hitters in the second year, arguably not deserving of the plate appearances they received. But all but Suzuki, Utley, and Byrd were due at least eight figures after the year of their large decline. By contrast, a decade earlier, in 2004-2005, there were eleven semi-regular batters who, aged 35 or older, who had a wRC+ decline of 25 or more. Of them, only three — Luis Gonzalez and Jim Edmonds in 2005 and Bret Boone in 2004 — were in the midst of unexpired long-term multi-million-dollar contracts. Small sample size warnings and all, but there was a lot more future money committed to declining old batters in 2014-15 than 2004-05. Maybe those players wouldn’t be getting the plate appearances to meet the 350 threshold if it weren’t for the money that’s owed them.

Fastballs. One of the notable changes in baseball in recent years has been that pitchers throw harder. From 2007 to 2015, per PITCHf/x, the average fastball velocity increased from 91.1 mph to 92.4 mph. The increase was 1.3 mph, to 91.9 mph, for starters and 1.5 mph, to 93.2 mph, for relievers. Older batters can take advantage of their knowledge of the strike zone and pitch sequencing, but maybe they just can’t catch up to some pitches.

Granted, I’m guessing here. I’m leaning towards PEDs, both strength-enhancing and amphetamines, faster fastballs, and a tendency to put high-paid players in the lineup regardless of performance as the key drivers. But I’m not sure. This is an interesting trend, and sufficiently well-established that I don’t think we can write it off as a recent fluke. Something’s going on with players in the second half of their fourth decade that hasn’t happened in a long time.

Introduction

Muscle fatigue is a process that results in decreased force generating capacity, and impaired performance [1]. Reduced force due to muscle fatigue may result in less stable joints, which can increase the risk of injury [2].  Furthermore, muscle fatigue is known to reduce joint proprioception  [3]–[6], which can result in further compromised joint stability and increased injury risk. Baseball pitchers have been shown to alter their kinematics (joint angles) when fatigued, which may strain different tissues when compared to pitching without fatigue [7].  Repetitive strain on these tissues can result in injury, and in baseball pitchers, injuries such as Ulnar Collateral Ligament tear.

Fatigue has been named the number one cause of injuries in baseball pitchers, leading to a 500% increase in injury likelihood [8]. Handgrip strength has decreased by up to 5% after simulated baseball games [9], and pitch velocity decreases over the the course of a game [10]. Pitcher kinematics also change with fatigue, with the elbow dropping lower, and the stride getting shorter.

The PITCHf/x system was created by Sportvision, and installed in every MLB stadium since 2006. The system allows for tracking of pitch movement, velocity and release point for every pitch thrown at the major league level. Two cameras are mounted in each stadium, and are used to track each pitch and display data during live broadcasts and websites. With the use of free software, like the programming package R, and database software MySQL, anyone can download gigabytes of data within hours, allowing for detailed analyses of pitching and hitting. With this detailed data, it would theoretically be possible to track changes associated with muscle fatigue. The purpose of this study was to examine how pitch velocity and release point changed in starting pitchers during the 2015 season.

Methods

Data Acquisition

I queried the pitchFX data from the 2015 season, grouping pitches by pitch type, pitcher, and inning. A pitch had to be thrown 20 times in an inning to be included for further analysis. The pitchers included in this analysis were those who pitched a minimum of 100 innings as a starting pitcher. The main focus of this analysis was to examine peak velocity changes, so only fastball type pitches were included in the analysis (four-seam, two-seam, split finger, sinking, cut, and general fastball).

I calculated the average velocity for each pitcher during their first inning of pitching. I then calculated the minimum average velocity for these pitches during either the 5^th, or 6^th inning – which ever value was the lowest.

For release point, I calculated the resultant distance of the release point (at z0, x0), from 0,0 (Figure 1). I also examined the change in vertical release point (z0) between the first inning, and the minimum of the 5^th and 6^th innings. Using the horizontal release point, and the vertical release point, I also calculated the absolute release angle (normalizing for left-handed and right-handed pitchers).

Statistics

For this analysis, the independent variable was inning (first inning, minimum of 5^th/6^th inning).

To examine the effect of inning, I performed a dependent samples t-test on variables of peak velocity, resultant release point, vertical release point, and release angle, with p < 0.05. I also calculated Cohen’s D to determine the effect size of the inning.

Results

Peak velocity significantly decreased between the first inning (91.19 ± 2.91 mph) and 5^th/6^th inning of the start (90.61 ± 3.01 mph, p< 0.05; d=0.20) (Figure 2). Vertical release point significantly decreased from 5.9 ± 0.35 feet to 5.84 ± 0.36 feet (p < 0.05, d=0.17)(Figure 3a). Resultant release point also decreased from 6.15 ± 0.35 feet to 6.09 ± 0.35 feet (p < 0.05, d=0.18) (Figure 3b). All of these changes were statistically significant, however, represented small effect sizes.

Release angle was significantly different between the first and final inning, moving from 74.9 ± 6.17 degrees to 75.1 ± 6.31 degrees. This represents a release angle that is closer to the vertical plane, or, closer to the midline of the body. While this change was statistically significant, the effect size was negligible (d=-0.04) (figure 4).

Discussion

In line with previous research on baseball pitching and fatigue, fastball velocity decreased between the beginning and the end of the average game. A decrease in release point distance and height also indicates that kinematics have changed during the course of a baseball game.

The following examples are from pitchers in the top ten for fatigue-related changes between innings. Andrew Heaney has a nearly 2mph decline between the 1^st and the 6^th inning (Figure 5a), and Ervin Santana has his resultant release point decrease by 2.21% (Figure 5b). In both cases, it could be expected that performance would be impaired by these fatigue-related changes. Conversely, Jacob deGrom actually increases his release point by 0.58% (Figure 5d), and Max Scherzer increases fastball velocity by 0.38% between the 1^st and 6^th inning (figure 5c). In general, 70% of pitchers experience a decreased velocity between the first and final inning, 85% of pitchers have a decrease in their resultant release point, and 83% of pitchers have a decrease in their vertical release point.

The velocity change demonstrated in this analysis represents a decrease of only 0.5 mph from the 1^st to the 6^th inning. This represents approximately a 1% change in velocity. Previous research has shown up to a 5mph decrease in velocity, decreasing from 90 mph to 85 mph during spring training games [10]. This greater decrease in velocity may represent decreased conditioning from the pitchers at this time of the season. Crotin, et al., [11] found that fastball velocity increased during a season, as a result of conditioning and improved strength. These factors may wash out some of the differences that could be seen throughout the course of a game, when average velocities are calculated over the course of an entire season and by inning, like in this analysis.

The pitchers included in this analysis represent a highly elite subset of the population. Previous research that has examined fatigue in baseball pitchers has included pitchers in spring training [10], college [9], or even Japanese high school players [12]. The fatigue effects for the elite population may not be as severe, as elite athletes are able to moderate the detrimental effects of fatigue when performing their sport specific task [13].

Limitations

Despite the easy access to PitchFX data, there are concerns with the accuracy and reliability of the system. For one, the release point displayed by the PitchFX system is at a distance of 50 feet from the plate. Typically, pitchers release the ball at 54-55 feet from the plate, so the true release point is not exactly known [14] Additionally, inter-stadium differences may also contribute to inaccurate PitchFX data – as cameras are not always in the exact same place in all stadiums.

Conclusions

Examining PitchFX data for fastball velocities and release points, averaged by inning for qualifying starters in the 2015 season, have produced results comparable to more controlled, lab based studies, on fatigue during pitching. However, limitations with the PitchFX system, and averaging data throughout the entire season can possibly remove some of the differences that could possible be seen as a pitcher fatigues. Additional research should be performed to examine in-game changes in velocity for both good, and bad starts, to see if fatigue effects are more prominent as a pitcher becomes less effective.

References

[1]       R. M. Enoka and J. Duchateau, “Muscle fatigue: what, why and how it influences muscle function.,” J. Physiol., vol. 586, no. 1, pp. 11–23, Jan. 2008.

[2]       G. S. Fleisig, J. R. Andrews, C. J. Dillman, and R. F. Escamilla, “Kinetics of baseball pitching with implications about injury mechanisms,” Am. J. Sports Med., vol. 23, no. 2, 1995.

[3]       L. A. Hiemstra, I. K. Lo, and P. J. Fowler, “Effect of fatigue on knee proprioception: implications for dynamic stabilization.,” J. Orthop. Sports Phys. Ther., vol. 31, no. 10, pp. 598–605, Oct. 2001.

[4]       F. Ribeiro, J. Mota, and J. Oliveira, “Effect of exercise-induced fatigue on position sense of the knee in the elderly,” Eur. J. Appl. Physiol., vol. 99, no. 4, pp. 379–385, 2007.

[5]       M. Sharpe and T. Miles, “Position sense at the elbow after fatiguing contractions,” Exp. Brain Res., vol. 94, no. 1, May 1993.

[6]       H. B. Skinner, M. P. Wyatt, J. A. Hodgdon, D. W. Conrad, and R. . Barrack, “Effect of fatigue on joint position sense of the knee,” J. Orthop. Res., vol. 4, no. 1, pp. 112 – 118, 1986.

[7]       R. F. Escamilla, S. W. Barrentine, G. S. Fleisig, N. Zheng, Y. Takada, D. Kingsley, and J. R. Andrews, “Pitching biomechanics as a pitcher approaches muscular fatigue during a simulated baseball game.,” Am. J. Sports Med., vol. 35, no. 1, pp. 23–33, Jan. 2007.

[8]       J. Lemire, “Preventing Athlete Injuries With Data-Driven Tech – Athletic Business,” Athletic Business, 2015. [Online]. Available: http://www.athleticbusiness.com/athlete-safety/preventing-athlete-injuries-with-data-driven-tech.html. [Accessed: 21-Dec-2015].

[9]       M. J. Mullaney, “Upper and Lower Extremity Muscle Fatigue After a Baseball Pitching Performance,” Am. J. Sports Med., vol. 33, no. 1, pp. 108–113, Jan. 2005.

[10]     T. a Murray, T. D. Cook, S. L. Werner, T. F. Schlegel, and R. J. Hawkins, “The effects of extended play on professional baseball pitchers.,” Am. J. Sports Med., vol. 29, no. 2, pp. 137–42, 2001.

[11]     R. L. Crotin, S. Bhan, T. Karakolis, and D. K. Ramsey, “Fastball velocity trends in short-season minor league baseball.,” J. Strength Cond. Res., vol. 27, no. 8, pp. 2206–12, Aug. 2013.

[12]     L.-H. Wang, K.-C. Lo, I.-M. Jou, L.-C. Kuo, T.-W. Tai, and F.-C. Su, “The effects of forearm fatigue on baseball fastball pitching, with implications about elbow injury.,” J. Sports Sci., pp. 1–8, Oct. 2015.

[13]     M. Lyons, Y. Al-Nakeeb, and A. Nevill, “The impact of moderate and high intensity total body fatigue on passing accuracy in expert and novice basketball players.,” J. Sports Sci. Med., vol. 5, no. 2, pp. 215–27, Jan. 2006.

[14]     M. Fast, “The Internet cried a little when you wrote that on it – The Hardball Times,” The Hardball Times, 2010. [Online]. Available: http://www.hardballtimes.com/the-internet-cried-a-little-when-you-wrote-that-on-it/. [Accessed: 21-Dec-2015].

For the first six years of his career, Evan Longoria was the best position player in baseball based on WAR (as FanGraphs calculates it). Despite losing over a season’s worth of games to various injuries during that time, his combination of tremendous hitting and elite defense at the hot corner made him a superstar when healthy.

Then 2014 happened. Injuries weren’t the issue, as Longoria played all 162 games for the first time, but his production cratered. He batted .253/.320/.404—well below his career averages of .275/.357/.512 coming into the season. He’d been so good up to that point, though, and he was only 28, so his off year appeared to be nothing more than a fluke. Surely Tampa Bay’s $100-million third baseman would bounce back.

He didn’t. His numbers improved slightly, to .270/.328/.435, but his 2015 was essentially the same as his 2014. Once again he was healthy, appearing in all but two games, making his struggles even more mystifying. That made two down years in a row for Longoria, in what were supposed to be his prime years.

Unless there’s a career-altering injury involved, great athletes typically don’t fall off a cliff in their late 20s. Oftentimes, they get better. They’re still young enough to be at their physical peaks, but also experienced enough to have acclimated to major-league competition. These are supposed to be an athlete’s greatest seasons.

For Longoria, they have been his worst.

Over the last couple years, Longoria has slipped from a great player to a merely good one, declining in all facets of the game. It’s been five years since he won his last Gold Glove, with defensive metrics suggesting he’s now closer to an average fielder than the vacuum cleaner he was previously. His baserunning has also fallen off considerably. Once an asset with his legs, he’s managed just 14 steals and provided negative value on the basepaths over the past five years.

Defense and speed peak early, however, so it’s not surprising that Longoria lost some of both as he approached 30. What’s concerning is how he’s become a league-average hitter after previously producing like David Ortiz.

A major red flag is Longoria’s plummeting walk rate, which has declined every year since 2011. Once a very patient hitter, he’s now drawing free passes at a league-average rate. Longoria’s chasing, and hitting, more pitches outside the zone than ever before, which explains both his eroding walk rates and hard-hit frequencies. When batters expand the strike zone, their swings become longer and generate weaker contact. After swinging at just a quarter of pitches outside the strike zone in 2013–tied for 20th out of 140 qualified batters–he’s chased over 31 percent of non-strikes each of the last two years, falling back to the pack in this department.

It’s no secret that older players become more aggressive to compensate for diminished bat speed, as they have to guess more often and start their swing earlier to catch up with fastballs. It could also be that Longoria is responding to an increase in first-pitch strikes. Whereas his first-pitch-strike percentage was below the league average every year from 2009-2013, he’s seen more first-pitch strikes than average over the past two seasons combined. When batters fall behind early, they can’t afford to be patient and are at the pitcher’s mercy. In 2015 the league hit just .225/.265/.344 after going down 0-1. Longoria isn’t much better, batting .234/.277/.388 for his career after first-pitch strikes. Since he’s seeing more of those, it follows that his numbers have nosedived. As for why Longoria’s seeing more first-pitch strikes, the larger strike zone is likely to blame, but pitchers also appear to be challenging him more often.

What’s really troubling, though, is Longoria’s evaporating power. After averaging 33 home runs per 162 games with a .237 ISO through his first six seasons, he’s averaged just 22 long balls with a .158 ISO over the past two. His doubles were down too, from 41 per 162 games to 31, so it’s not like he was just getting unlucky with his HR/FB rates (though he did post the lowest one of his career–10.8 percent–in both 2014 and 2015). He’s not trading contact for power, either, as his strikeout rates and contact rates have held steady.

The reason for Longoria’s diminished power is simple and one I alluded to earlier; he’s not hitting the ball as hard as he used to. After reaching a high of 41.5 percent in 2013, his hard-hit rate crashed to 32.1 percent in 2014 and 30.6 percent last year. Meanwhile, his soft-contact rate nearly doubled from 2013 to 2015. This data, along with his rising infield-fly rates (he popped up as often as he homered last year) and shrinking fly-ball distances, suggests he’s not squaring up the ball as well as he used to. That’s a side effect of hacking, to be sure, but also reflects his waning bat speed and exit velocity.

Recent studies have shown that position players are peaking earlier than they used to, closer to age 26, and it appears that’s what happened with Longoria. His seemingly premature decline has likely been accelerated by injuries suffered early in his career as well as the rigors of playing a demanding defensive position. On that note,  his career seems to be following the same path as David Wright’s. Both peaked early and were at their best in their mid-20s, looking like future Hall of Famers. Then their performance started suffering in their late 20s, because of injuries with Wright and the reasons outlined above with Longoria (both were hurt by their home parks as well). Wright has yet to recapture the consistent greatness he exhibited through his first five seasons and, should Longoria continue on his current trajectory, neither will he.

The idea for the graphs in this article started with a post I read at Tom Tango’s website, which linked to this article. That article gave further credit to Sky Kalkman. Jeff Zimmerman also had a post in 2009 with this graphical representation, so be aware that I’m building off of the work of others, with some changes.

The methodology:

• I used only BBWAA-elected Hall of Fame players. Since I’m looking at players currently up for election by the BBWAA, I thought it would be best to look at players previously voted in by the BBWAA. The BBWAA has a higher standard for entry than the various Veterans Committees. Many of the Hall of Fame players with the lowest WAR totals were put in by Veterans or Old Timers Committees.
• I separated catchers from the rest of the hitters. I also created two graphs for relief pitchers. One compares relievers to all pitchers. The other compares relievers to just BBWAA-elected relievers.
• I used FanGraphs WAR. The articles I linked to above used Sean Smith’s WAR database, which uses Baseball-Reference WAR.
• BBWAA-elected Hall of Fame players are ranked by their highest WAR season to lowest WAR season.
• All of the highest season values for the Hall of Famers were grouped together, then the second highest seasons, then the third highest seasons, etc.
• When the WAR values went negative, they were zeroed out from that point forward.
• I found the 75^th, 50^th, and 25^th percentile for each season. This band is shaded in gray, with the black line representing the 50^th

The “No-Doubters” Tier

Barry Bonds (164.4 WAR, seasons above the median: all)—Setting aside the PED issue and focusing on just what he did on the field, Barry Bonds could be in a two-man Hall of Fame with Babe Ruth (168.4 hitting WAR). They are both nearly 15 WAR ahead of the next player, Willie Mays (149.9 WAR). Then again, if you add in the 12.4 WAR Babe Ruth earned for his pitching, the gap between Ruth and Bonds is greater than the gap between Bonds and Mays. Babe Ruth could be in his own personal Hall of Fame, where the hot dogs are always cooked to perfection and the beer flows freely.

Pre-1999 Barry Bonds (99.2 WAR)—The purple line on the graph represents the best 13 years of Barry Bonds career before the 1999 season, which is when it is commonly thought Bonds started using PEDs. Even if Bonds had retired before his incredible stretch of seasons from 2001 to 2004, he looks like an easy Hall of Famer.

Jeff Bagwell (80.2 WAR, seasons above the median: 13)—Bagwell compares favorably to Ken Griffey, Jr. His best three years are surpassed by Griffey’s best three years, but Bagwell had a longer stretch of seasons well above the Hall of Fame median. On the MLB Network recently, I heard Ken Rosenthal discussing Bagwell and Piazza’s Hall of Fame case with regard to the voters. Rosenthal suggested that some voters have hesitated to vote for Bagwell and Piazza because of the possibility they used PEDs and the fear that if they are elected and we find out down the road that they used PEDs, this would have implications for Bonds and Clemens. Essentially, if they find out there is a player in the Hall of Fame who has used PEDs, then how do they then justify not voting for Bonds or Clemens? To be clear, Rosenthal doesn’t feel this way himself; he was just explaining how other voters may feel.

Ken Griffey, Jr. (77.7 WAR, seasons above the median: 10)—He’ll go in easily. Like Frank Thomas before him, the writers feel Griffey was clean. Whether that’s true or not, we don’t really know. His best 10 seasons were at or above the median Hall of Fame level and he has five other seasons in the gray zone.

The “In the Conversation” Tier

Larry Walker (68.7 WAR, seasons above the median: 6)—Remember, these are BBWAA-elected Hall of Fame players and the gray zone represents the 25^th to 75^th percentile seasons for those players. Larry Walker has an interesting line. His two best seasons were at or above the two best seasons of the Hall of Fame median but his third through sixth best seasons drop below that level. His remaining seasons in descending order are generally close to the median. Other factors that likely hurt him with the BBWAA voters are his games played in Coors Field and that he always seemed to miss 20 or more games each year. In his 17-year career, Walker only played 150 or more games one time.

Mark McGwire (66.3 WAR, seasons above the median: 5)—McGwire’s line is similar to Walker’s, but with fewer seasons below the 25^th percentile level early in his career. McGwire’s sixth-best through tenth-best seasons are above the median, but he drops off quickly after his best 11 seasons.

Alan Trammell (63.7 WAR, seasons above the median: 1)—Trammell is consistently in the range between the 25^th and 50^th percentiles, but it isn’t until his 14^th best season where he is above the median for the Hall of Fame groups’ 14^th best season. More than half of the shortstops in the Hall of Fame were non-BBWAA selections. Trammell has more career WAR than many of those players, but beats out only one BBWAA-elected shortstop, Luis Aparicio. Trammell has been on the ballot for 14 years. His high total in voting was 36.8% in 2012, but he dropped to 25.1% last year. This is his final chance with the BBWAA.

Edgar Martinez (65.5 WAR, seasons above the median: 5)—Edgar has some things going against him. First off, playing primarily as a DH hurts him in the eyes of many voters. Second, based on the chart above, Edgar didn’t have the peak that many BBWAA-elected Hall of Famers had, as his five best seasons are in the gray zone between the 25^th and 50^th percentile. His sixth through tenth best seasons are above the zone and he does have 10 seasons with 4.7 or more WAR. That hasn’t been enough for the voters so far. His vote totals have dropped in each of the last three years.

The “Another Tier, Much Like the Previous Tier” Tier

Tim Raines (66.4 WAR, seasons above the median: 3)—Raines is a favorite candidate of many who is thought to be underrated and under-appreciated by Hall of Fame voters. He has gained support over the years, though, moving from 24.3% in his first year on the ballot to a peak of 55.0% last year. His place on the chart above shows that he’s similar to Alan Trammell. They both had long careers consistently in the gray zone below the median. Compared to the other BBWAA-elected hitters, Raines is a borderline candidate. He wouldn’t raise the level of BBWAA-elected hitters, but he’s better than some recent inductees. That being said, I added Tony Gwynn to this graph and it’s easy to see how similar Gwynn and Raines were in WAR. Gwynn made the Hall of Fame in his first year on the ballot. The key difference for voters may have been their distribution of hits and walks. Gwynn had 3,141 hits and 790 walks, for a total of hits plus walks of 3,931. Raines had 2,605 hits and 1,330 walks, for a total of hits plus walks of 3,935. Those 3,000 hits go a long way. Despite that, there isn’t enough of a separation between them that one should sail right in on his first ballot (97.6%) and the other gets 24.3% on his first ballot.

Jim Edmonds (64.5 WAR, seasons above the median: 5)—Half of Edmonds’ ten best 10 seasons were above the median Hall of Fame level and the other five were in the gray zone. His 11^th best and beyond seasons fall short.

Gary Sheffield (62.1 WAR, seasons above the median: 2)—Despite being such different players, Sheffield’s line is very similar to Tony Gwynn’s line, with a similar pattern of highs and lows. It’s uncanny.

The “It’s Not the Hall of Good” Tier

Fred McGriff (56.9 WAR), Jeff Kent (56.1 WAR)—Jeff Kent and The Crime Dog were good players with long careers, but they don’t compare favorably with other BBWAA-elected Hall of Fame hitters.

Nomar Garciaparra (41.4 WAR)—Six of Nomar’s first seven seasons were worth 4.8 WAR or more, but it was a steep drop-off from there. He played 14 seasons and those six seasons accounted for 92% of his career WAR.

The “New Guys Who Don’t Have a Chance” Tier

The eight players on the above two charts are unlikely to get the 5% needed to stay on the ballot, but they may get some scattered votes here and there. In case you were wondering, that 8-win season for Troy Glaus came in 2000 when he hit .284/.404/.604, with 120 runs, 47 home runs, 102 RBI, and 14 steals. He was fourth in the AL in WAR but didn’t receive a single MVP vote. The winner that year was Jason Giambi (with 7.7 WAR).

The Catchers

Mike Piazza (62.5 WAR, seasons above the median: 10)—Piazza is on the cusp of entry into the Hall of Fame. His voting totals have gone from 57.8% to 62.2% to 69.9%. Based on his numbers, he should have been voted in three years ago. Hopefully, he’ll get the 75% needed for induction this time around.

Jason Kendall (39.8 WAR)—Kendall has more career WAR than a couple of Veterans Committee inductees (Rick Ferrell and Ray Schalk) and more WAR than Roy Campanella, who had his career start late and end early. Kendall had six seasons with 3.9 WAR or more, which is impressive, but he doesn’t compare to the BBWAA-elected Hall of Fame catchers.

Brad Ausmus (17.2 WAR)—Ausmus hit .251/.325/.344 in one of the best eras for hitting in the history of the game. Imagine how poorly he would have hit had he played in the 1960s.

Starting Pitchers

Roger Clemens (133.7 WAR, season above the median: all)—Roger Clemens is the Barry Bonds of pitchers. They were both well above the median of BBWAA-elected Hall of Fame players and they are trapped in Hall of Fame voter purgatory for the time being, both with roughly 37% of the vote on last year’s ballot. They have seven more years on the ballot.

Mike Mussina (82.2 WAR, season above the median: 12)—Mussina and Schilling are an interesting comparison. Schilling’s six best seasons are better than Mussina’s six best seasons. From their sixth-best seasons and beyond, Mussina was better. Mussina has been on the ballot two years and saw his vote total go from 20.3% to 24.6%. Compared to other BBWAA-elected Hall of Fame starting pitchers, both seem worthy of induction.

Curt Schilling (79.7 WAR, season above the median: 12)—Schilling and Mussina both had 12 seasons above the median and similar WAR totals, but Schilling has the edge in voting so far. Schilling has been on the ballot three years, going from 38.8% to 29.2% to 39.2% in the voting.

Mike Hampton (28.0 WAR, season above the median: 0)—He doesn’t compare to the other pitchers on this ballot, but Hampton did hit .315/.329/.552 in 152 plate appearances with the Rockies in 2001-2002, which is pretty cool.

Relief Pitchers

Lee Smith (26.6 WAR, season above the median: 12)—The top graph shows how these three relievers compare to all pitchers elected by the BBWAA. In short, they don’t compare favorably. The difference in innings pitched is just so great between starters and relievers that it’s hard for a reliever to be as valuable. The bottom graph includes just relief pitchers elected by the BBWAA, but without John Smoltz or Dennis Eckersley, who each had more than 350 starts and around 200 wins. The four “true” relievers are Hoyt Wilhelm, Goose Gossage, Rollie Fingers, and Bruce Sutter. Lee Smith didn’t reach the heights of those four, but did have 12 seasons above the median, starting with his third-best season. He’s been on the ballot for 13 years and peaked with 50.6% of the vote in 2012. Last year, he was down to 30.2%.

Trevor Hoffman (26.1, season above the median: 9)—For what it’s worth, Harold Reynolds thinks Trevor Hoffman is a “slam-dunk” Hall of Famer. Of course, that’s worth exactly nothing because it’s coming from Harold Reynolds and he doesn’t have a vote. Hoffman does have those 601 saves, but he doesn’t stand out here as being much better than Smith or Wagner.

Billy Wagner (24.2 WAR, season above the median: 6)—It wouldn’t surprise me to see Hoffman get considerable support and Wagner be a “one and done” candidate, despite how comparable they actually were.

If I Had a Ballot:

Barry Bonds

Roger Clemens

Mike Piazza

Jeff Bagwell

Ken Griffey, Jr.

Mike Mussina

Curt Schilling

Edgar Martinez

Larry Walker

Alan Trammell

When Zack Greinke signed his six-year, $147 million contract with the Los Angeles Dodgers after the 2012 season, he became the highest-paid pitcher in baseball history in terms of annual salary. Now, after opting out of that deal and inking an even bigger one with the Arizona Diamondbacks, he’s the highest-paid player in baseball history in terms of annual salary.

How did Greinke get the same contract length and $60 million more at 32 than he he did at 29? By stringing together three straight dominant seasons in Los Angeles, the last of which was easily the best of his career and, in a normal year, would have earned him his second Cy Young. Greinke’s timing was impeccable, as he hit the open market after posting the lowest ERA (1.66) in 20 years and leading the majors in WHIP (0.84), winning percentage (.864), pitcher WAR (as calculated by Baseball-Reference), and ERA- (44). His two years before last weren’t too shabby, either, as he posted sub-three ERAs and drew Cy Young votes both years.

But were his last three campaigns really that much better than the three that preceded his Dodgers contract? It depends which stats you use:

2010-2012:  41-25  W-L  3.83 ERA  (106 ERA+)  1.22 WHIP  .248 BA  8.4 bWAR

2013-2015:  51-15  W-L  2.30 ERA  (156 ERA+)  1.03 WHIP  .219 BA  17.5 bWAR

By traditional metrics, Greinke was a much better pitcher from ages 29-31 than he was from 26-28, which are supposed to be a player’s prime years. His ERA was a run and a half lower in the same number of innings, which explains why his bWAR more than doubled (B-R bases pitcher WAR off ERA and innings pitched). He won more games, lost fewer, and improved his WHIP and opponent batting average considerably.

Advanced metrics tell another story. Let’s start by looking at the two things pitchers can control, strikeouts and walks. I don’t include home runs because those have a lot to do with park factors, temperature, air density, wind currents, and a bunch of other things beyond a pitcher’s sphere of influence:

2010-2012:  23.3 K%  6.2 BB%

2013-2015:  23.3 K%  5.4 BB%

Greinke’s strikeout rate remained identical, which one would expect given that nobody gains velocity as they get older. His walk rate improved a bit, which works out to be one fewer walk every two or three starts — hardly a big difference in the grand scheme of things.

People also believe pitchers have control over the type of hits they allow. Has Greinke’s distribution of batted balls become more favorable?

2010-2012:  20.3% LD  47.4% GB  32.3% FB  7.9 % IFFB

2013-2015:  21.8% LD  47.5% GB  30.8% FB  11.1% IFFB

Not really. Greinke’s groundball rate stayed the same, and he seemed to offset an increase in line drives with an increase in pop-ups. It’s weird that his line-drive rate went up, seeing as how he induced more soft contact and less hard contact over the past three years:

2010-2012:  Soft 17.0%  Med 54.7 %  Hard 28.3%

2013-2015:  Soft 19.3%  Med 53.3%   Hard 27.6%

Again, not much change, though there is some indication that he’s gotten better at generating weaker contact. Not enough to radically improve his results, mind you, or significantly alter his BABiP (keep that in mind for a minute).

While his ERA doesn’t reflect his stable peripherals, his FIP, xFIP, and SIERA all do.

2010-2012: 3.16 FIP 3.17 xFIP 3.26 SIERA

2013-2015: 2.97 FIP 3.12 xFIP 3.23 SIERA

As you can see, fielding-independent metrics support the information above, suggesting Greinke was essentially the same pitcher over the past six years.

So why, then, are his Dodgers numbers so much shinier? Moving to Dodger Stadium (where he has a 2.00 career ERA) and a weaker division gave him a boost. Leaving behind a god-awful defense in Milwaukee also helped. Having a better bullpen behind didn’t hurt, either.

But also, a lot of it was just pure luck. Greinke was fairly unlucky in the three years before coming to Los Angeles, only to become one of the most fortunate pitchers in baseball during his time with the Dodgers. Greinke had the highest strand rate in baseball over the last three years, but from 2010-2012 he had the worst of anyone who threw as many innings as him. Dodger Stadium and superior defense also helped him on balls in play. From 2010-2012, only Justin Masterson had a higher BABiP among pitchers who threw at least 600 innings. Over the last three years, however, Greinke had the third-lowest BABiP at .271 — roughly 30 points below the league average.

Greinke also had better luck on balls not in play, as in home runs. His HR/FB% dropped almost a full percentage point, which is substantial considering the league average is around 10% (Greinke’s mark from 2010-2012). Accordingly, his HR/9 rate improved by 16 percent. That works out to be only a handful of homers per season, but those long balls can make a serious dent in a pitcher’s ERA if they come with multiple guys on base.

Taking all this into consideration, Greinke is not a better pitcher now than he was three years ago. His park, fielders, and bullpen have made him look like a better pitcher, as has better luck, but at his core he’s the same guy. Here’s one more figure to prove it:

2010-2012: 13.6 fWAR (8th in pitcher fWAR)

2013-2015: 13.6 fWAR (8th in pitcher fWAR)

Greinke is getting paid to be the best pitcher in baseball, even though he’s not. After a few starts in the Arizona heat, that should become abundantly clear.

Just what are the Yankees getting at when they acquire Dustin Ackley, Aaron Hicks, and Starlin Castro within a few months of each other? The group would seemingly not have much in common other than perhaps age, but the Yankees have found a group of players with similar attributes that should benefit them nicely.

Ackley, 28 next season, Hicks, 26 next season, and Castro, 26 next season represent a “youth movement” for New York, as they attempt to distance themselves from the burdens of the Mark Teixerias of their roster. Given their closeness in age, let’s look at how they should be expected to perform in the coming years when examining their batted-ball-direction aging curves.

Hicks and Castro appear to be heading into their peak pull performance in terms of home run + fly ball distance.  Ackley on the other hand, looks like he’s attempting to stave off the effects of age. Let’s look at how different handedness affects hitters throughout their careers.

Here again, we see Castro and Hicks as right-handers are at their prime for pulling the ball in terms of batted-ball distance. Ackley looks like he’ll decline gradually up until about 32 when the average distance really takes a dive. However, both of these charts simply describe the distance at which these three can be expected to hit the ball. It doesn’t take into account how likely they are to pull the ball, or their results on fly balls over the last couple of seasons.

(Fly ball distance is unavailable for Hicks in 2014, and in 2015 only accounts for his left-handedness. Since he’s presumably hitting mostly right-handed for New York next season, I didn’t include the info.)

All three saw an uptick in their likelihood to pull the ball, with Ackley and Hicks seeing a substantial increase in how hard they hit fly balls. Ackley’s distance soared to 293 on fly balls yet was mostly unnoticed due to Safeco’s hampering effect on left-handed hitters. Hicks meanwhile, played in the not-so-friendly Target Field which isn’t exactly a hitter’s paradise either. Both should benefit from the move to the more hitter-welcoming Yankee Stadium next year. The Yankees may have noticed that both were showing solid skills yet the results were difficult to achieve in said environments, so they saw an opportunity to swoop in and pluck them.

Castro, on the other hand, pulled the ball slightly more while seeing a dip in Hard Hit% on fly balls, and a drop in distance. If he, along with Hicks, can continue to increase their ability to pull the ball, and combine it with the increase in distance associated with pulled fly balls, the outcomes should look much nicer on paper.

It seems as if the Yankees have found a beneficial meeting of aging curves, players who are pulling the ball more often, and teams who might not have quite the use for these players as New York. If all three can at a minimum come close to last year’s skills in terms of hitting fly balls, the Yankees have a trio of players who match their stadium perfectly.

(The graphs used in this post are sourced from http://www.hardballtimes.com/how-batted-ball-distance-ages/)

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Ron Santo is listed on the Cubs roster for the duration of his career while the Dodgers declare Steve Garvey and the Diamondbacks claim Justin Upton. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

Assessment

The 1997 Boston Red Sox          OWAR: 63.7     OWS: 317     OPW%: .583

Based on the revised standings the “Original” 1997 Red Sox outperformed the Yankees, seizing the American League pennant by ten games. Boston led the circuit in OWS and OWAR. GM Lou Gorman acquired 28 of the 36 ballplayers (78%) on the 1997 Red Sox roster.

Jeff Bagwell (.286/43/135) finished third in the MVP balloting and established personal-bests in RBI and stolen bases (31). Brady Anderson followed his 50-homer campaign in ’96 with 39 doubles and 18 jacks. Nomar Garciaparra (.306/30/98) merited the 1997 AL Rookie of the Year Award as he topped the charts with 209 base knocks and 11 triples. Mo “Hit Dog” Vaughn delivered a .315 BA with 35 round-trippers. John Valentin contributed 29 two-baggers and a .296 BA while third-sacker Wade Boggs managed a .292 average as a part-timer.

Bagwell and Boggs rate fourth among first and third basemen, respectively, according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates listed in the “NBJHBA” top 100 rankings include Roger Clemens (11^th-SP), Garciaparra (T-17^th-SS), Vaughn (51^st-1B), Anderson (63^rd-CF) and Burks (77^th-CF).

Roger Clemens (21-7, 2.05) paced the Junior Circuit in victories, ERA, complete games (9), shutouts (3), strikeouts (292) and WHIP (1.030). The “Rocket” collected the fourth of seven Cy Young Awards and made his sixth All-Star appearance. Curt Schilling struck out a career-high 319 batsmen and fashioned a record of 17-11 with a 2.97 ERA. Paul Quantrill led a bullpen-by-committee, posting a 1.94 ERA along with 6 wins and 5 saves.

The “Original” 1997 Boston Red Sox roster

Honorable Mention

The “Original” 1912 Red Sox             OWAR: 55.1     OWS: 317     OPW%: .591

Boston sailed to the pennant by a 13-game margin over the Athletics and Senators. Tris Speaker delivered an MVP season, notching League-bests with 53 doubles, 10 round-trippers and a .464 OBP. “The Grey Eagle” batted at a .383 clip, registered 136 tallies and posted career-highs in base hits (222) and stolen bases (52). Smoky Joe Wood (34-5, 1.91) dazzled opposition batsmen, twirling 10 shutouts and completing 35 of 38 starts. Larry Gardner contributed a .315 BA and legged out 18 three-baggers. Buck O’Brien recorded 20 victories with a 2.58 ERA in his only complete season in the Major Leagues. Hugh Bedient matched O’Brien’s win total and fashioned a 2.92 ERA in his rookie campaign. Duffy Lewis rapped 36 doubles and established a personal-best with 109 ribbies.

On Deck

The “Original” 1969 Reds

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

This article was co-authored by Daanish Mulla – @DanMMulla

Last month, we wrote an article on calculating a pitcher’s “stuff”. We were quite pleased with how our equation performed with respect to predicting a pitcher’s strikeout rate and his xFIP. Part of the discussion surrounding the equation was what exactly is stuff? Well, in our case, stuff can be thought of as a three-dimensional shape, where the three axes of the shape represent a pitcher’s peak velocity, a pitcher’s change in velocity between their fastest and slowest pitch, and the amount of distance that their pitches can break. In other words, it aims to represent the range in pitch velocity and movement batters must account for during any given at-bat against a particular pitcher.

However, there was still some room for improvement, and with help from the FanGraphs community, we’ve slightly modified our equation to improve various performance predictions. The first major change came from comparing faster breaking balls versus slower breaking pitches with greater movement. In our original stuff metric, pitchers with a slow, looping breaking ball received more benefit than pitchers throwing a fast breaking ball. I queried the PitchF/x database to see how swinging strike rates and batting average changed against curveballs with respect to pitch speed during the 2014 season. Pitches that were thrown for at least 1% of all pitches were included in this analysis. As you can see in the figure, swinging-strike percentage increases exponentially after 75mph, and is nearly 15% higher at 85mph than at 75mph. This encouraged us to find a better way to account for faster breaking balls.

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Secondly, the original metric did not account for pitch frequency. The Pitch Arsenal metric was improved from it’s original state by accounting for this, and realistically – a pitcher should be given more credit for a great pitch that they throw frequently, as opposed to a great pitch that they rarely throw. To account for this, pitches were classified as either off-speed/breaking or fastballs. The sum of pitch uses for each of these classifications was then used to modify the values in the equation. With that in mind, here’s how we have proposed to modify the stuff equation.

For a pitch to be included in the analysis, it had to be thrown by the pitcher 100 times. Just like the original stuff equation, z-scores were determined for the fastest pitch the pitcher threw, and for the amount of movement that could be seen with respect to that fastball, from the remaining pitches.  For further analysis, only qualified starters were used (those who threw 162 innings in the 2015 season).

Furthermore, z-scores were also determined for the % change in speed between the pitcher’s fastest and slowest pitch. Another z-score was determined for the velocity of the fastest pitch, between curveball, slider, or knuckle-curve. Frequencies were determined for the proportion of fastballs thrown by a pitcher, and the remaining non-fastball pitches. The z-score for velocity was multiplied by the fastball percentage, and the remaining z-scores were multiplied by the non-fastball frequency. The z-scores for peak velocity of breaking pitch and change in velocity were used to determine “pitch strategy” – either, power breaking ball, or change in speed. Whichever z-score was greater, was used in the final stuff equation.

So, the final “stuff” equation is as follows:

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To begin validation of the equation, the stuff value was then correlated with K/9 for all qualifying starters. This resulted in a predicted R value of 0.53 (figure 2), compared to the value of 0.42 from the original stuff equation.

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We’ve since applied the stuff equation to all pitchers from 2007 to 2015 to try and get an idea of the range of the metric. Here’s what we found. For interpretation of this figure, if a pitcher has a stuff value of 0.90, his stuff is better than 75% of all pitchers since 2007. If the value is 2.0, they have stuff that is better than approximately 99% of all pitchers since 2007. To put that in perspective, that means their stuff is better than nearly 4000 other starting pitchers. You’ll notice that in our list of the top 30 pitchers from 2015 – all of these pitchers fall within the top 15% range of stuff. These are elite pitchers with respect to this metric.

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These data have a wealth of applications, such as how a pitcher returns from injury or has even changed his repertoire between years. For example, the jump Chris Bassitt made from 2014 to 2015 – going from someone in the bottom half of the metric to the 99^th %ile. Similar to the Arsenal score, there is an application of these data in determining a pitcher on the verge of a breakout (perhaps the Joe Kelly of the second half of 2015 is the real Joe Kelly).

However, we felt that it would be in our best interest to let the community decide just how useful the metric was, so we’re making our evaluation data from 2007 to 2015 available in the form of a Google sheet. Simply select the pitcher you’d like to evaluate, and their stuff scores and xFIPs will be graphed for you. We’ve also posted the entirety of stuff scores from the 2015 season.

2015 Season

https://docs.google.com/spreadsheets/d/1picxCRD1OWpaeDq2H8uxC7jyR6fH7fpj5gOZjQGWsu4/pubhtml

Stuff worksheet

https://docs.google.com/spreadsheets/d/1PU3u3sJpr_jv70VAJIlyXnvOh4pq56l7eXuo70Py81Y/edit?usp=sharing

Philosophically, we feel that the stuff metric has a great benefit for advanced scouting, because it relies on measures that are solely dependent on the pitcher, and not an interaction of the pitcher and the hitter. Thanks to the FanGraphs community, r/baseball, and Eno Sarris for all of the support with this project.

Every year, we hear about batters taking a new approach at the plate that they expect to generate better outcomes. But, as has often been shown, a lot of player tendencies are hard-wired. Players generally don’t change that much. What happens when they do?

In June, I looked at hitters who were pulling the ball a lot less or a lot more than they had in 2014. The conclusion was that it didn’t really make much of a difference, in aggregate, on offensive performance, although some players did markedly better and some did markedly worse. Now that the full season’s in the books, I decided to take another look at the comparison to see how a change at the plate affects hitting.

To look at this, I selected hitters with 350 or more plate appearances in both 2014 and 2015, corresponding roughly to at least half-time play. There were 173 such players. Using that sample set, I evaluated three observations you hear a lot about modern hitters:

• They pull too much, allowing infielders to get extra outs by shifting. If they’d hit to the opposite field, they’d do better.
• They try to hit everything into the seats, resulting in too many infield flies and lazy fly balls to the outfield instead of hitting sharp grounders that can become singles.
• They’re too passive, getting behind the count by watching pitches.

I looked for changes in pull tendency, ground vs. air batted balls, and aggressiveness at the plate, measured by net pull percentage (i.e., percentage of balls pulled minus percentage hit to the opposite field), ground ball/fly ball ratio, and swing percentage as proxies. To gauge the impact of the changes, I looked at change in wRC+, since it is a park- and season-normalized comprehensive measure of hitting.

It’s important, I think, to make a distinction between a change in outcomes to a change in approach. Take pulling the ball. If a batter pulls the ball less from one year to the next, it could be because he’s consciously trying to spray the ball over the field more in order to become less predictable and therefore harder to defend. Mike Moustakas comes to mind. But a batter may pull less because of the effects of age and/or injury, making his bat slower and unable to turn on inside fastballs. Since we can’t divine approach from full-season statistics, we’ll have to satisfy ourselves with outcomes. Among the 173 players in the sample, Victor Martinez had the largest decline in hitting the ball hard, and his wRC+ decline of 90 points was similarly the largest in the group. That doesn’t mean that he went into the season deciding to hit the ball softer, and that his strategy backfired. Rather, it was a reflection of Martinez’s health. A change in outcomes isn’t necessarily reflective of a change in approach.

I ranked the 173 players by their change in pull tendency, ground vs. air batted balls, and aggressiveness at the plate, and divided them into quintiles based on plate appearances. As an example, for pull tendency, the quintiles were players who went the opposite way a lot more (net pull percentage down 7.5% to 25.9%), those who went the opposite way somewhat more (net pull percentage down 3.6% to 7.4%), those who hit about the same (net pull percentage down 3.5% to up 0.1%), those who pulled somewhat more (net pull percentage up 0.2% to 5.0%),and those who pulled a lot more (net pull percentage up 5.0% to 17.8%). I also selected examples of players whose wRC+ was considerably better or worse in 2015 for each quintile. Generally, these were the players at the top or bottom of the rankings, though I did ignore obviously injured underperformers like Martinez and Jayson Werth.

In the tables I’m going to display, there are a lot of negative numbers for change in wRC+. The reason is that among the 173 players with 350 or more plate appearances in 2014 and 2015, the average wRC+ declined by 5.2 points (from 109.6 to 104.4), or 4.3 points (110.9 to 106.6) weighted by plate appearances. While that may be a topic for future research, it’s not a shock, given aging curves, regression, and the emergence of young talent in the majors.

Players who pulled a lot more or went the other way a lot more in 2015 than in 2014 did better than their peers. (Again, the average player’s wRC+ declined by 5.2 points, or 4.3 weighted by plate appearances). Those who went the opposite way a lot more improved relatively, and those who pulled a lot more improved both relatively and absolutely. If there’s a benefit to hitting to the opposite field for pull-happy sluggers who make too many outs by hitting the balls to shifted infielders, we’d see the change in wRC+ decline as the net pull percentage increases. That’s not what happened. Bryce Harper, Chris Davis, and Shin-Soo Choo, among others, benefited from pulling more, not less.

Players’ ground ball tendencies, similar to their pull tendencies, resulted in positive variance at both extremes. Players who hit the ball on the ground a lot more improved relative to their peers, and those who hit it in the air a lot more improved relatively and absolutely. Harper’s an outlier again—he pulled a lot more, hit the ball in the air a lot more, and produced a lot more runs. It’s amusing to see Red Sox teammates Xander Bogaerts and Hanley Ramirez as prime examples of what can go right or wrong if you hit a lot more ground balls.

This project turned out to be murkier than I would’ve liked. Did batters who pulled a lot less, or those who it the ball on the ground a lot more, do better in 2015 than they did in 2014? Yes, but so did those who pulled a lot more and hit the ball in the air a lot more. And those are only aggregate figures; in every quintile, there are examples of batters who were a lot better or a lot worse. And we can’t completely tease out the change in approach from the change in a batter’s health or age or the way he’s pitched. About the only thing that seems to be safe to say is that swinging more is a dubious strategy. If a player goes into spring training talking about getting more aggressive at the plate and taking a lot more hacks, we might hope that his batting coach can talk him out of it.

Pirates pitchers hit more batters than any team in the majors this year, 75. They also led in 2014. And 2013. That’s unusual. The only teams to have lead the majors in hit batters for three or more seasons since 1901 are the 1921-23 Phillies, 1930-32 Cardinals, 1938-40 Senators, 2002-04 Rays, and the 2013-15 Pirates.

The Pirates also got hit more than any team in the majors this year, with 89 hit batters. On one hand, that makes sense, given baseball’s Book of Exodus stance: A hit batter for a hit batter. On the other hand, and more significantly, it’s been a rare occurrence. Since 1931, only 14 teams have led their league in both pitcher and batter hit by pitches (1943 Giants, 1947 Dodgers, 1955 Dodgers, 1963 Reds, 1966 White Sox, 1968 Astros, 1980 White Sox, 1982 Angels, 1983 Expos, 1996 Astros, 2009 Phillies, 2012 White Sox, and 2013 and 2015 Pirates). Only 8% of teams in that time span have led their league in both hitting batters and getting hit. Pure random chance would put that figure above 9%. It just doesn’t happen very frequently.

I should interject that I fall in the anti-hit batters camp. I don’t like seeing anybody getting hit by pitches. Sometimes they shake it off. Sometimes they miss time. Sometimes it’s horrifying. But when you consider that there were 1,602 batters hit last year, 844 on fastballs, and the average fastball velocity is 92.1 mph—well, they’ve all got to hurt. As I’ve discussed in previous posts, some hit batters are clearly accidents, often occurring when a pitcher, ahead in the count, comes inside on a pitch and misses. But some undoubtedly are a form of message-sending, with the message coming as a hard object thrown at a speed that would constitute assault were it not on a baseball diamond. (A notable case occurred when Cole Hamels hit Bryce Harper in 2012, then justified it on the grounds of “that old-school prestigious way of baseball.”) Intentionally throwing at hitters to exact some sort of vengeance, sorry, is dumb. But how often does it happen?

The Pirates provide a good test case, since by leading the league in hit batters on both offense and defense, they provide a decently large sample size. Here’s how large: Pirates batters were hit 89 times, tying them for 11^th among the 1,926 team-seasons since 1931. Their pitchers hit 75 batters, tying them for 43^rd. If you saw a lot of Pirates games, you saw a lot of batters getting hit.

And you heard a lot of excuses. The Pirates encourage pitching inside, drawing the ire of other clubs. But Pirates backers also point to the large number of Pirates batters who get hit, and the need for the pitchers to “protect” Pirates batters. You hit my Andrew McCutchen, I hit your Joey Votto. That sort of thing.

How often does that happen? Is protection–really, retaliation–a significant factor in batters getting hit? To try to answer, I classified every hit batter in Pirates games last season—164 in total—into three different categories:

• First Blood: Named in honor of the great American thespian Sylvester Stallone, the standard by which actors have been judged. (The New York Times memorably described Arnold Scharzenegger as “the thinking kid’s Sylvestor Stallone.”) First Blood (the initial work in Stallone’s Rambo oeuvre) occurs when a batter is the first one hit in a game or series.
• Retaliation: As a follow-up to First Blood, Retaliation occurs when a batter is hit by the pitcher whose teammate was last hit.
• Piling On: This occurs when a team, having already had a batter hit, suffers another, with no intervening Retaliation.

(I realize that I’m ignoring hit batters as retaliation for things like inside pitches, hard slides, and being Bryce Harper. Those don’t show up in game summaries, and besides, that’s more two-eyes-for-an-eye and therefore less acceptable.)

The timeframe is important here. Hit batters occur in the context of a game, but the casus belli can stretch out longer. Al Nipper hit Darry Strawberry with a pitch in spring training of 1987, allegedly in retaliation for Strawberry taking a slow trot around the bases after hitting a home run off Nipper in the prior year’s World Series. So I looked at hit batters in three settings:

• The game being played
• The series between the teams, to see whether retaliation carries over from one day to the next
• The season series, to capture longstanding grudges.

For example, on July 12, the day before the All-Star break, the Pirates’ Arquimedes Caminero hit the Cardinals’ Mark Reynolds with a pitch in the tenth inning. The next time the two teams played was on August 11. The Cardinals’ Carlos Martinez hit Pirate Aramis Ramirez in the first inning. That’s First Blood for the game and series, but Retaliation for the season series, since the prior hit batter, albeit a month earlier, was a Cardinal. Two days later, Pirates catcher Francisco Cervelli was hit by a pitch from Lance Lynn in the first inning. That was First Blood for the game, but Piling On for both the series and season series, as it followed his teammate getting hit. The next time the teams played, on September 4, Pirates reliever Jared Hughes hit Reynolds with a pitch in the ninth inning. That counts as First Blood for the game and the series, Retaliation with respect to the season series. The following day, the Pirates’ Starling Marte was hit by Jaime Garcia in the second inning. That was First Blood for the day, Retaliation for both the series and the season series. In the bottom of the second, Charlie Morton hit Jon Jay. That’s Retaliation in the context of game, series, and season series.

(Another aside: I am opposed to the use of plunked as a synonym for hit by pitch. Plunk is what happens when you’re rearranging books on your bookshelf and a paperback falls from a high shelf and hits you in the shoulder, or when you’re walking in the woods and an acorn hits your head. A 92.1 mph fastball is not a plunk.)

You may be thinking: This is pretty stupid, categorizing hit batters, what’s the point? The point is that if the Pirates pitchers are hitting opposing batters for some sort of tribal/protection/vengeance thing, we should see a lot of Retaliation. If that’s nonsense, it’s not the case.

Say a team plays 19 opponents, as the Pirates did (every National League team, plus the American League Central). Let’s also assume that the team’s pitchers hit 75 batters, as the Pirates did, and the team’s batters were hit 89 times, again as the Pirates were. If hit batters are random, we’d expect the team to be throw 19 x 75 / (75 + 89) = 8.7 First Blood pitches and get hit by 19 – 8.7 = 10.3 such pitches in season series. Thereafter, the odds of a hit batter being Retaliation or Piling On would be 50/50, subject to the distributional difference between 75 and 89. So the team would log 33.2 Retaliation and Piling on hit batters, and get hit by 39.3 of each type of pitch. Again, this assumes that hit batters occur completely randomly.

Here are the actual totals:

This kind of refutes the self-defense argument, doesn’t it? A Pirates batter was hit by a pitch before an opponent was in 61 games, accounting for nearly 70% of the team’s hit batters. But Pirates pitchers drew first blood in 56% of their games as well. Overall, retaliation accounted for only 20% of batters hit by Pirates pitchers in games. Over the course of a series, when my hit batter today can result in your hit batter tomorrow, retaliation explains only 32% of Pirates opponents hit. Even with the most liberal definition of retaliation, when it can be spread over the weeks or months of a season series, it still accounts for just 43%, less than half of batters hit by Pirates pitchers. Not that it was different on the other side: Pirates hit in retaliation accounted for only 15% of hit batters in games, 33% in series, and 39% in season series. The majority of hit batters occurred without seeming provocation.

Let’s compare the results of the Pirates games to those of the random distribution presented above. For Pirates pitchers, a random distribution would be 9 First Blood, 33 Retaliation, and 33 Piling On. Actual figures: 9, 32, 34. For Pirates batters, a random distribution would be 10 First Blood, 39 Retaliation, and 39 Piling On. Actual figures: 11, 35, 43. Those distributions (1) are pretty close to random and (2) feature less retaliation than a random distribution would suggest.

So what does it mean? Well, retaliation definitely does occur. We saw it the National League wild card game, when Pittsburgh reliever Tony Watson pretty clearly hit Cubs pitcher Jake Arrieta on purpose, in response to Cervelli and Josh Harrison getting hit by Arrieta, resulting in the silly spectacle of the benches clearing. But the example of the Pirates’ regular season, when there were a lot of hit batters, shows that retaliation isn’t as common as either code-of-honor defenders like Hamels nor hand-wringers like I might think. The numbers instead suggest that hit batters are, in fact, pretty random. Which would seem to make intentionally hitting batters a really uninformed idea as well as a bad one.