Archive for December, 2015

Can PitchFX Data Be Used to Identify Muscle Fatigue?

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 5th, or 6th 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 5th and 6th 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).

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Figure 1. Demonstration of how calculations were made for the vertical release point, resultant release distance, and release angle.

Statistics

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

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Figure 2. Fastball velocity significantly decreased between the first inning (91.19 ± 2.91 mph) and the minimum between the 5th and 6th inning (90.61 ± 3.01 mph) (p < 0.05). This represented a small effect size, of 0.20.

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Figure 3. Both vertical release point, and resultant release distance decreased between the first inning and the 6th inning, representing a possible change in pitcher kinematics. This represented a small effect size, of 0.18 and 0.17, respectively.

 

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Figure 4. Release angle increased (representing a release point closer to the midline of the body) between the first and final inning, though the effect size for this relationship was negligible.

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 1st and the 6th 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 1st and 6th 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.

 

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Figure 4. Case studies to illustrate changes in pitching velocity (A and C), and resultant release point (B and D), using average data for each pitcher during the 2015 season.

The velocity change demonstrated in this analysis represents a decrease of only 0.5 mph from the 1st to the 6th 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].


Longoria Losing Power, Patience

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 Least Valuable Players (LVPs) of 2015

After the announcement of the Cy Young and MVP winners, the award season is officially over and the offseason is in full stride. Most, except perhaps Royals and Mets fans, have moved on from the 2015 season and are focused on the year ahead. However, before doing so, I wanted to answer one final question about the past season: Who were the league’s Least Valuable Players (LVPs)?

Inspired by Neil Paine’s piece on Bryce Harper and the MVP I define the LVPs as position players (with at least 400 plate appearances) that not only had a bad year in terms of performance, but had an even worse performance relative to the salary they were being paid.

Why the distinction?  First off, most teams, with a few apparent exceptions (I’m looking at you Dodgers) have some sort of payroll limitation. Therefore having an expensive player stink up the place limits the opportunity that teams have to replace them via free agency or trade.

Secondly, it is my initial assumption, that underperforming players with large contracts may get disproportionally more playing time than similarly underperforming players with cheap contracts. This might be because teams hope that by giving players a chance to work things out at the plate they may salvage their initial investment or even entice another team to take a flyer with them. This, in the end, might be compounding the issue in the long-term as it robs the team the opportunity to try out existing farm-level talent at the position for instance.

It should be noted that it is possible that unlike most replacement-level players, underperforming players with big contracts were at some point actually good players and might have some other intrinsic value for their teams (i.e.: leadership, tradition, marketing, etc.) that justifies playing time; think of Ken Griffey Jr during his last few seasons or Derek Jeter’s farewell tour. However, for the intents and purposes of this article we will not be discounting player’s terribleness by any of these measures.

As far as methodology goes we will be replicating Paine’s approach from the previously mentioned article. FanGraphs calculates the monetary value of a player by estimating how much teams spent during the preceding offseason per projected WAR and then multiplying this value by accrued WAR during the season to get a sense of how much those wins above replacement would’ve cost in the “open market”. Then, from this “open market” value we subtract the actual salary (or rather salary cap hit from spotrac) of the players to get a grasp for their relative value or net value. In the case of over performing players this would turn into a value surplus for the team, whilst for underperforming players this would represent an additional cost for the team.

For example, per FanGraphs, the cost of a win in the 2015 offseason was approximately $8 million. Mike Trout accumulated WAR of 9.0 during the year, which means that the value his 2015 season was around $72 million. Meanwhile, his salary was a “mere” $6.1 million, which makes the surplus for the Angels somewhere around $66 million. In other words, the Angels paid $6.1 million in salary to get $72 million worth in production, which is a bargain of historic proportions.  Conversely, during the 2015 season Ryan Howard accumulated a WAR of -0.4, which translates into a -$3.2 million value. Not only that, but Howard was paid a cool $25 million for his services, which means that the true cost to the Phillies was of $28.2 million. In other words Philadelphia invested $25 million to get -$3.2 million in production, which over time is the kind of decision that leads to this.

So without further ado here are our Top 50 LVPs from the 2015 season:

Player Team WAR “Open market” value (MM USD) Salary cap hit (MM USD) Net value (MM USD)
Hanley Ramirez Red Sox -1.8 -$14.40 $19.75 -$34.15
Pablo Sandoval Red Sox -2 -$15.70 $17.60 -$33.30
Victor Martinez Tigers -2 -$15.80 $14.00 -$29.80
Ryan Howard Phillies -0.4 -$3.40 $25.00 -$28.40
Adam LaRoche White Sox -1.4 -$11.30 $12.00 -$23.30
Joe Mauer Twins 0.3 $2.20 $23.00 -$20.80
Matt Kemp Padres 0.4 $3.50 $21.25 -$17.75
Yasmany Tomas Diamondbacks -1.3 -$10.70 $5.38 -$16.08
Melky Cabrera White Sox -0.3 -$2.50 $13.00 -$15.50
Angel Pagan Giants -0.5 -$4.40 $10.25 -$14.65
Omar Infante Royals -0.9 -$7.00 $7.50 -$14.50
Jacoby Ellsbury Yankees 0.9 $6.90 $21.14 -$14.24
Alexei Ramirez White Sox -0.5 -$3.70 $10.00 -$13.70
Billy Butler Athletics -0.7 -$5.70 $6.67 -$12.37
Chris Owings Diamondbacks -1.4 -$11.20 $0.51 -$11.71
J.J. Hardy Orioles 0 -$0.20 $11.50 -$11.70
Jay Bruce Reds 0.1 $0.60 $12.04 -$11.44
Prince Fielder Rangers 1.6 $12.90 $24.00 -$11.10
Cody Asche Phillies -1.1 -$9.00 $0.47 -$9.47
Jimmy Rollins Dodgers 0.2 $1.70 $11.00 -$9.30
Avisail Garcia White Sox -1.1 -$8.60 $0.52 -$9.12
Michael Cuddyer Mets 0 -$0.30 $8.50 -$8.80
Alex Rios Royals 0.2 $1.30 $9.50 -$8.20
Ichiro Suzuki Marlins -0.8 -$6.20 $2.00 -$8.20
Albert Pujols Angels 2 $16.00 $24.00 -$8.00
Robinson Cano Mariners 2.1 $16.90 $24.00 -$7.10
Kurt Suzuki Twins -0.1 -$0.70 $6.00 -$6.70
Torii Hunter Twins 0.5 $3.90 $10.50 -$6.60
Yadier Molina Cardinals 1.3 $10.80 $15.20 -$4.40
Logan Morrison Mariners -0.2 -$1.50 $2.73 -$4.23

 

The American League LVP is a tight race between two teammates in which Hanley Ramirez narrowly beats out Pablo Sandoval, even after failing to accumulate enough plate appearances to qualify for the batting title. Meanwhile, Ryan Howard stands head and shoulders above the competition in the National League specially after considering that the Dodgers heavily subsidized Matt Kemp’s salary.

When considering teams most affected by this subset of underperforming stars we can highlight the Red Sox and White Sox leading the way with over $60 million of net value lost each seriously shooting themselves in the foot as both had aspired to contend in 2015.  This was particularly damning for Boston; had they not had these terrible contracts on hand, and holding all else constant, the Red Sox would have finished with the 6th best positional net value in the AL, ahead of playoff teams like the Astros, Rangers and Yankees and with sufficient cash to spend to shore up their well-documented starting rotation deficiencies.

Lastly, it’s worth noting the vast number of players on the list that were signed as free agents, extended or traded for during the past year. All in all roughly half of the players on this list fit that description, which is something to keep in mind when your team announces its next big move during the coming offseason (uh-oh).

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com


The BBWAA’s Hall of Fame, Graphically Speaking

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 75th, 50th, and 25th percentile for each season. This band is shaded in gray, with the black line representing the 50th

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 25th to 75th 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 25th 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 25th and 50th percentiles, but it isn’t until his 14th best season where he is above the median for the Hall of Fame groups’ 14th 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 25th and 50th 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 11th 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

 


Mike Leake and the Importance of Showing Up

Mike Leake is going to be paid $16 million dollars a year for the next five years. It seems to have people up in arms. This is why MLB ticket prices keep rising!

Mike Leake isn’t an elite pitcher. But he’s not the completely mediocre pitcher that he’s made out to be. He is a professional pitcher that has a track record of showing up to pitch. That holds a lot of value. There’s not many out there.

There are 30 teams in major-league baseball and, generally, they all use 5-man rotations. The best 150 pitchers would be all we would consider in a world with no injuries. We don’t live in that world.

We live in a world that has produced 851 qualifying pitcher seasons over the past 10 MLB seasons. A qualified pitcher needs to throw 162 innings, or one inning per game. In the case of starting pitchers who averaged 5 innings per appearance, that would be 33 starts. Essentially, to qualify you need to be on the mound consistently.

Since 2011, Leake’s five full seasons in the majors, there have been 195 pitchers who have combined for 429 qualifying seasons. That’s a little over 86 qualifying seasons per year.

Mike Leake has five of those seasons. Over those five seasons Leake has accumulated 8.9 WAR, or about 1.8 WAR per year. If we roll with the assumption that a win goes for between $7.5–8 million on the open market this deal makes perfect sense. But, this deal makes sense beyond that simple reason.

The Cardinals were a 100-win team. Their best pitcher from the prior year (Carlos Martinez) missed the playoffs with an elbow issues (not encouraging) but their best pitcher (Adam Wainwright) will be back healthy this year, which may compensate for some level of trepidation about Martinez’s durability and availability through 2016. Lance Lynn is done with Tommy John surgery and John Lackey has moved on to the Cubs. And while Leake hasn’t ever performed to the level Lackey did last year, or the level Lynn has over the past four years, he has shown the ability to eat innings at an above-average rate while throwing as an above-average pitcher.

Moreover, the things that Mike Leake doesn’t do well seem to be correctable and the Cardinals are an organization that tends to correct things.

First, Leake’s changeup is terrible. It may be time to consider scrapping that pitch altogether considering he possesses a slider and curveball that he uses regularly. The chart below, taken from BrooksBaseball.net, shows opponents’ slugging percentage against each of Leake’s offerings. As you can see the changeup hasn’t served any good since 2011. On the other hand, Leake’s slider has been improving where his changeup has been declining

A look at opponents’ batting average against tells the same story:

Leake’s second issue has been home runs. This is odd because Leake throws a sinker that often achieves its desired results. He has a 50.2% groundball rate for his career and 2014 and 2015 saw him induce the most groundballs in career, with 53.4% and 51.8% rates in those respective seasons. But when hitters put the ball in the air on Leake, they hit home runs at a 13.7% rate. This has been a consistent problem throughout Leake’s career, so it’s not as simple as looking at his xFIP and seeing hope for improvement.

This is where Leake’s biggest fault lies. If the Cardinals identify, or have identified, something to bring Leake’s home-run rate on fly balls down then they have just landed a bargain. But as of now, they landed a fair deal for a fair pitcher.

Mike Leake isn’t the reason tickets to a Cardinals game are expensive, but if you can make it out to the ballpark you’re going to see Mike Leake there, being Mike Leake; a professional pitcher; a reliable pitcher; a pitcher who shows up to work.


Top 5 Fantasy Starting Pitching Prospects for 2016

For this list there will be two requirements:

  1. The players under consideration must not have thrown even a single pitch in the major leagues. This throws out notable names such as Steven Matz, Jon Gray, and others who have already done so.
  2. The players under consideration must be projected to graduate from the prospect label in 2016 and have a significant influence on a major-league team. This throws out notable names like Julio Urias, Lucas Giolito, and others who are projected to be unlikely call-ups for the 2016 season.

 

So here we go, my projections for the top five pitching prospects you should keep an eye on for your fantasy team in 2016.

1. Tyler Glasnow

Team: Pittsburgh Pirates

Throws: Right

Height/Weight: 6’8”/225

Age: 22

Projected Path: Opening day rotation

Rundown: Nobody in this year’s projected rookie class boasts a bigger frame or, more importantly, a bigger fastball than Glasnow. Standing at an intimidating 6’8”, Glasnow is known to be able to pound the catcher’s mitt repeatedly with an upper 90s fastball that routinely overpowers hitters. MLB.com gives his fastball a 75 on the 20 to 80 scale, a truly remarkable grade. Add that to an above-average power curveball and an improving changeup, and it is easy to see why scouts rave about this guy’s immense upside.

But really, who cares about what scouts think? Not me, and neither should you. Let’s check out some upper-minor-league numbers Glasnow produced in 2015. Glasnow’s largest body of work in 2015 came in AA where he threw an even 63 innings over a span of 12 starts. Here he struck out a remarkable 33.1% of batters while only walking a respectable rate of 7.7% of batters he faced. His strikeout rate was tied with fellow highly-touted right-hander Jose De Leon for tops in all AA leagues among pitchers who threw at least 60 innings. Throw in his respectable walk rate, and he led all AA pitchers with the same inning restrictions in K-BB%. In AA he had an ERA of 2.43 while only stranding 66.4% of baserunners, a statistic generally attributed to luck. Again the average LOB% from 2015 was 72.3%, so one can assume he was a little unfortunate giving up some of those runs. With that said, his ERA could have easily looked more like his FIP which was an outstanding 1.98.

Either way, Glasnow proved to be dominant in AA and was later called up to the next level. In 43 innings of AAA ball he struck out a similarly great 27.6% of hitters. He walked some extra guys, leading to a high 12.6% BB%. Unsurprisingly, he was able to strand more runners in AAA, 73.3% of them to be exact, and yielded a 2.20 ERA. His FIP was 2.82.

Final take: Go get this guy. If he’s available late for a cheap price, Glasnow could be the ultimate diamond found in the rough. His best strength is in his strikeout numbers which plays really well for fantasy. The only weakness to his game is the walks. If he can find a way to limit walk totals, Glasnow could join the conversation for top young arms in 2016 and beyond.

2. Jose Berrios

Team: Minnesota Twins

Throws: Right

Height/Weight: 6’0”/190

Age: 21

Projected Path: Opening day rotation

Rundown: Perhaps more polished than Glasnow, Jose Berrios is a very strong name to have on your radar. As an undersized righty, Berrios hits mid-90s with his fastball, but will mainly live in the lower 90 range. He also throws a slurve-like breaking ball with various velocities as well as an above-average changeup. The best thing about Berrios is his plus command. As a 21 year old, he walked only 6.5% and 4.7% of batters in 90⅔ innings in AA and 75⅔ innings in AAA respectively. His strikeout numbers were also strong with a 25.1% mark in AA and a 27.7% effort in AAA. An interesting note on Berrios was his major improvement from AA to AAA. His K-BB% improved by 4.5%, as well as his FIP and ERA numbers.

Final Take: I was going back and forth for quite sometime trying to decide who was more valuable between Berrios and Glasnow. In the end I chose Glasnow mainly due to the unprecedented strikeout potential as well as the national league benefit. However, that by no means says that Berrios can’t be better. Led by his impressive ability to limit walks, go into your draft with Berrios’ name in mind.

3. Blake Snell

Team: Tampa Bay Rays

Throws: Left

Height/Weight: 6’4”/180

Age: 23

Projected Path: Opening day rotation

Rundown: Blake Snell is one of the most intriguing names in the prospect heap for 2016, partly because he came into the year as a relatively unknown 22-year-old in the Tampa Bays Rays A+ affiliate. The other part is that in 2015 he didn’t let up a run until his 50th inning of work. He escaped A+ ball in 21 innings without letting up a run and then rattled off another 28 scoreless innings in AA. Eventually though, he did prove to be human as he let up a first-inning home run to the Cubs’ Wilson Contreras ending his scoreless inning streak at 49. Nevertheless, he put up astounding numbers across three levels of the minor leagues in 2015.

Like Glasnow, Snell’s best tool is his ability to strike hitters out. He does this with a low to mid-90s fastball as well as an above-average slider and changeup. His biggest flaw is the walks, as he walked over 10% of the batters he faced in both AA and AAA. Like Berrios, he posted his best K-BB% numbers in AAA. In 44⅓ there he struck out 33.3% of batters and only walked 7.6%, good for an incredible 25.7% K-B%. Although it is very difficult to project his basic run-prevention skill without the aid of batted-ball type or velocity, he certainly excelled in that area in 2015. In 21, 68⅔, and 44⅓ innings in A+, AA, and AAA his ERA was 0.00, 1.57 and 1.83 respectively.   

Final Take: Like I said at the beginning, Blake Snell is intriguing. Walks will hold him down, strikeouts will bring him up. If you like what see take a shot and thank me later. He has the potential of an elite starter.

4. Jose De Leon

Team: Los Angeles Dodgers

Throws: Right

Height/Weight: 6’2”/185

Age: 23

Projected Path: Mid/late season call up

Rundown: The only thing holding De Leon back from being closer to the top of this list is the Dodgers’ management. Most likely, he will not make the team out of camp and will head to AAA to start the year.  However, due to the Dodgers’ thin staff and postseason desperation, De Leon is bound to make a splash sometime in 2016. As mentioned earlier, he was tied with Tyler Glasnow in K% in AA during the 2015 season among pitchers with more than 60 innings pitched. De Leon pitched a total of 76⅔ innings at the AA level through 16 starts. Before that, also in 2015, he threw 37⅔ innings at the A+ level. He put up ridiculous numbers there, striking out batters at a rate of a nearly unheard of 40% while only walking 5.4% of hitters. His walk rate increased a little bit in AA, but he still boasts better command than the likes of Glasnow and Snell. De Leon pairs his low to mid-90s fastball with a slider and changeup.

Final Take: Although De Leon is unlikely to make the team out of spring camp it is worth keeping this guy on your fantasy radar. Pay attention for any news on a potential call-up, and if you find any, don’t waste time to add him to your roster. In deeper formats, De Leon certainly deserves a late-round draft choice.

5. Josh Hader

Team: Milwaukee Brewers

Throws: Left

Height/Weight: 6’3”/160

Age: 21

Projected Path: Mid/late season call up

Rundown: After coming to the Brewers in the Carlos Gomez deal, Hader quickly improved his prospect stock by increasing his K-BB% by almost 10% with the move from the Astros AA affiliate to the AA affiliate of the Brew Crew. Although he started his only 7 games with Milwaukee, Hader spent time both starting and coming out of the pen before the deal in Houston. Over there he was not nearly as impressive with a higher BB% as well as significantly lower K% in 65⅓ innings. Like I promised, things got better in his 38⅔ innings for the Brewers in AA. Hader struck out a robust  32.9% of hitters while only walking 7.2%. Overall, Hader finished sixth in K-BB% among starters under 25 in AA who logged more than 60 innings. Hader pairs his mid-90s fastball with an average changeup and curveball. Due to his shot forward with the Brewers, and the lack of organizational pitching skill combined with likely trades of veterans either during the offseason or before the July trade deadline, Hader could be looking at a potential midseason call-up where his ability to get strikeouts would be an asset, especially in the NL. On top of this, Hader has better command than most 21-year-olds.

Final Take: Hader’s upside is real. A strong fastball, paired with above-average command bodes well for National League pitchers. Now all he has to do is continue his success in the minor leagues for the Brewers, and he will almost certainly see a call-up to the big-league rotation. If this happens make sure you remembered his name.

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Stats and research courtesy of FanGraphs and MLB.com


The New Zack Greinke: Same As the Old (But Richer)

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.


The Braves Might’ve Fleeced the D-Backs Out of $225 Million

Who got the most out of the Shelby Miller trade?

At the moment, based on present values of prospects, players and wins? It’s the Braves. Based on the industry consensus? It’s the Braves. Based on which team will be better next season? Maybe still the Braves. How did Arizona get so comprehensively fleeced in this deal? There have been some great articles written on FanGraphs these past two weeks about the GM of the Arizona Diamondbacks. Dave Stewart seems to want to do things differently from all other teams, going with his gut when building a roster. Gut decisions can work. His gut identified Shelby Miller as a good player and so wanted him on his team. But wow did this gut decision miss something. How much something? How about 225 million dollars?

That’s right, I am suggesting that, with present values of prospects, players and wins, Dave Stewart, GM, gave away $225M more value in this trade than he got back. See below for how I got to that number.

FanGraphs’ staff have been using a great contract tool recently to estimate the value of major-league free agents. It estimates that players get better each year until aged 27 then worse each year after reaching 31. I’ll be following their method, which estimates an aging curve using WAR where players improve by 0.25 WAR each year in their 18-27 age years, keep steady WAR in their 28-30 age years and get worse by 0.5 WAR each year in their 31-37 age years. It also includes an inflation increase in the market value for each WAR, starting at $8.0M in 2016 and increasing by 5% each year going forwards.  We end up with an estimated value for players over the lifespan of their contract.

I’m going to use last season’s FanGraphs WAR as the starting value for any major-league players (I’ll discuss the minor-league prospects later). I realise I could use ZiPS or many other predictors of players performance (which offer much lower WAR values), but it seems fair as both major-league players in the deal have some issues with their peripheral numbers that seem to balance out. The tool is an estimator based on current performance, so it seems fair we start with what they achieved this last year. Anyway, let’s get started.

The Major Leaguers

Shelby Miller (worth $98.48 M) – to Arizona

Year Age WAR $/WAR Est. Value
2016 25 3.65 $8.0 M $29.20 M
2017 26 3.90 $8.4 M $32.76 M
2018 27 4.15 $8.8 M $36.52 M
Totals   11.7   $98.48 M

 

Ender Inciarte (worth $172.91 M) – to Atlanta

Year Age WAR $/WAR Est. Value
2016 25 3.55 $8.0 M $28.40 M
2017 26 3.80 $8.4 M $31.92 M
2018 27 4.05 $8.8 M $35.64 M
2019 28 4.05 $9.3 M $37.67 M
2020 29 4.05 $9.7 M $39.29 M
Totals   11.7   $172.91 M

 

Holy cow! Not looking good for Arizona already. Shocked how much Inciarte is worth by this model? Me too. Those wins get expensive. He’ll do well to keep his performance at this level, but I’d argue that Miller has the same issue.

The Minor Leaguers

This isn’t quite as simple to work out. Minor-league players have a habit of not making it to the majors (an average 70% bust rate of ranked players not making a significant contribution to the major league team, according to this excellent article by Scott McKinney). I have used the valuations on ranked MLB prospects from Kevin Creagh and Steve DiMiceli with a couple of modifications.

Summarised, using historical prospect rankings, they took prospects ranked between 1-100 in the top prospect rankings each year and found the average WAR produced over their first six major league seasons at different rankings. These values are in the table below:

Tier Number of Players Avg. WAR Bust % Zero WAR or less
Hitters #1-10 53 15.6 9.43%
Hitters #11-25 34 12.5 8.82%
Hitters #26-50 86 6.8 31.4%
Hitters #51-75 97 5.0 44.33%
Hitters #76-100 96 4.1 41.67%
Pitchers #1-10 18 13.1 0%
Pitchers #11-25 47 8.1 27.66%
Pitchers #26-50 77 6.3 24.68%
Pitchers #51-75 94 3.4 47.87%
Pitchers #76-100 105 3.5 44.76%

Creagh and DiMiceli were looking at surplus value produced by a prospect. I’m more interested in total value. For each ranked prospect (Blair and Swanson) I found the average WAR produced by historical players with a similar prospect ranking (potentially flawed; I’d probably prefer median WAR for each group), then used an inflation model (again 5% per year starting at $8.0M/WAR in 2016) and an assumption that 2/3 of a prospect’s value is accrued during years 4-6 in the majors to find an estimate for total value.

Finally, some minor-league prospects don’t improve enough to reach the majors (they “bust”) so they aren’t as valuable as major-league players. This reduces the value of the prospect. Lower-ranked players are more likely to bust than higher ones so they should have a greater reduction in their value. The historical likelihoods of a prospect bust are shown in the table above (Bust % Zero WAR or less) for different ranks of prospect (again from Creagh and DiMiceli).  To account for the chance of a prospect bust I reduce their value by a factor of the bust percentage, taken from the table above (% Zero WAR or less). It isn’t perfect, but seems reasonable, I’m happy to discuss in the comments.

Dansby Swanson (worth $137.2M) – to Atlanta

  • #10 prospect currently (on MLB.com)
  • Hitter
  • Worth 15.6 WAR on average in years 0-6
  • Arrives in majors in 2017
  • 33% of value in years 0-3 (at 2018 cost per WAR) – 5.2 WAR – $45.76M
  • 67% of value in years 4-6 (at 2021 cost per WAR) – 10.4 WAR – $105.04M
  • Bust chance of #10 hitting prospect – 9%
  • Estimated total value – $137.2M

Aaron Blair (worth $14.85M) – to Atlanta

  • #61 prospect currently (on MLB.com)
  • Pitcher
  • Worth 3.4 WAR on average in years 0-6
  • Arrives in majors in 2016
  • Assume only lasts 3 years in majors (due to low WAR total)
  • 100% of value in years 0-3 (at 2017 cost per WAR) – 3.4 WAR – $28.56M
  • Bust chance of #61 pitching prospect – 48%
  • Estimated total value – $14.85M

Gabe Speier (worth $negligible) – sorry Gabe – this trade wasn’t about you.

Final value totals

Arizona:

  • Shelby Miller – $98.5M

Atlanta:

  • Ender Inciarte – $172.9M
  • Aaron Blair – $14.9M
  • Dansby Swanson – $137.2M
  • TOTAL – $325M

DIFFERENCE IN VALUE – $226.5M

There are a number of obvious caveats here. Miller could be better than this, Inciarte may not be that good, Swanson may never make it, Blair may never make it. However, at this moment, these are some of the values that you could reasonably ascribe to these assets. This is a staggering loss for Arizona. In what business can you lose $225M dollars in one transaction and keep your job? By this rather flawed measure, the Braves have just increased the value of their organisation by $225M. That pays for half a new stadium. Or Jason Heyward’s recent contract. Or the next 3 years of performance of Mike Trout (Trout is seriously valuable). I realise that the money can’t be accessed like that, but still, wow. Dave Stewart might be using his gut feeling when making deals, but he’d better start listening more to his analytics department or he’s liable to get robbed again.

 

A lot of the inspiration (it wasn’t plagiarism, honest) for this article came from Craig Edwards and his piece on “Attempting to rationalise the Shelby Miller Trade”. I just took it a different way. Thanks to Craig though! You should read it – http://www.fangraphs.com/blogs/attempting-to-rationalize-the-shelby-miller-trade/


The Yankees Search for Ideal Hitters

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.
AllFieldsAging

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.
AllPulls

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 Data for Dustin Ackley
Year Pull% Hard % FB Distance
2014 20% 36% 275
2015 25% 44% 293

 

Fly Ball Data for Aaron Hicks
Year Pull% Hard%
2014 15% 18%
2015 20% 32%

 

Fly Ball Data for Starlin Castro
Year Pull% Hard% FB Distance
2014 15% 41% 282
2015 17% 35% 279

 

(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/)


All In on Odorizzi

By now I think everyone is aware of Thing 1 and Thing 2, the splitter/change-up pitch currently used by Tampa Bay Rays starters Alex Cobb and Jake Odorizzi. (If you are unaware of them you can read up on it here.) An oblique injury limited Odorizzi to 169.1 innings over 28 starts in 2015, but the signs of a potential big-time pitcher were present throughout most of the campaign.

I took Odorizzi’s 2015 season with Thing 2 now in his arsenal and compared it to seasons that some of the top starters in the league have had early on in their careers. Here is what I found:

Jake Odorizzi 2015 (age 25) K% 21.4  BB% 6.6  Hard hit rate 26.7%  Soft hit rate 19.4%

Johnny Cueto 2010 (age 24) K% 17.7  BB% 7.2  Hard hit rate 30.2%  Soft hit rate 19.6%

Zack Greinke 2008 (age 24) K% 21.5  BB% 6.6  Hard hit rate 27.8%  Soft hit rate 19.6%

Jake Arrieta 2012 (age 26) K% 22.0 BB% 7.1 Hard hit rate 25.6%  Soft hit rate 20.5%

Max Scherzer 2010 (age 26) K% 23.0  BB% 8.8%  Hard hit rate 29.6%  Soft hit rate 19.1%

 

Now I know the rest of these pitchers didn’t add their own version of a Dr. Seuss-named pitch, or any pitch for that matter. However all of these pitchers continued to make a leap in their following season. Cueto’s 2011 saw his contact rates make almost a complete flip (27.7% soft 21.3 hard). Greinke took a huge leap winning the AL Cy Young in 2009. Arrieta’s 2013 saw him get traded to the Cubs and begin his trend upwards leading to the NL Cy Young in 2015. Max Scherzer had his walk percentage drop to 6.7% and his hard and soft hit rates move to almost even with each other(23.5% and 22% respectively).

You can see that Odorizzi’s K% and BB% are very similar to all four pitchers (especially Greinke’s), and also his hard and soft hit percentages are right in the middle of this data sample. Odorizzi was able to accomplish these batted ball rates, despite having the lowest groundball percentage of the five. Odorizzi’s ground ball percentage was 37.3% last season. Cueto, Greinke, Arrieta and Scherzer all had a groundball percentage higher than 40% with Scherzer being the lowest at 40.3%.

If Thing 2’s development stunts or the command issues that have plagued Odorizzi at different times in both the majors and minors crop up, then Odorizzi’s 2015 ratios (as listed above) line up extremely close to Aaron Harang’s career ratios — and that’s not a world that I’m ready to live in. I’m betting on the fact Odorizzi still has age and development time on his side, as he won’t turn 25 until right before the 2016 season starts. Along with Tampa’s relatively positive history of developing starting pitching and their use of defensive shifts and alignments, there are plenty of reasons to get excited about Odorizzi’s short term and long term future.