Archive for August, 2017

MLB Dream Team: Active Players Bound for the Hall of Fame, Part I

Sports always allow us to ask, what if? What if a baseball lineup — complete with all nine positions and a designated hitter — was composed of all-time greats in their best seasons.

I have composed a lineup filled with the very best active players who I think will make the Hall of Fame.

These players will not be judged on their performance this year; they will be chosen based on how well they performed during their primes.

I have designated a player’s “prime” as the best seven years of their career — not necessarily consecutive — and these selections are based on the player’s likelihood to make the Hall of Fame. Some members of the team will be inducted on the first ballot, and some will take years to make it to the Hall, but ultimately I think that every player on this list has a great shot at being immortalized in Cooperstown.

This article is part one of a two-part set in which I show my Dream Team. Part two will be released tomorrow.

Metrics Explained

Wins Above Replacement, or WAR, is the most commonly used advanced metric in baseball. It is a measure of how many wins a team gained by playing a specific player instead of a replacement player, who would have a WAR of 0. If a player records 2 WAR in a season, he is considered starting material, 4 or 5 WAR is acknowledged to be All-Star value, and 8 WAR is MVP-level production.

The Jaffe WAR score system, or JAWS, is simply the average of a player’s seven-year peak WAR and career WAR. For example, if a player had 100 career WAR and 50 seven-year peak WAR, his JAWS would be 75. This metric gives us perspective on how likely it is for a player to make the Hall of Fame compared to those who played their position.

Fielding Percentage is a measure off how often a player commits an error. For example, a fielding percentage of 97% means the player committed an error on 3% of the plays he made.

Note: This list favors older players because:

  • They have more career WAR
  • They have more years from which to choose their seven-year peak WAR
  • They are closer to entering the Hall of Fame than younger players.

Batting leadoff and playing right field…

Ichiro Suzuki

59.4 career WAR / 43.6 7yr-peak WAR / 51.5 JAWS

Average HOF RF:

73.2 career WAR / 43.0 7yr-peak WAR / 58.1 JAWS

17th in JAWS out of 24 Hall of Fame Right Fielders

Accolades: MVP (2001), Rookie of the Year (2001), 10x All-Star, 10x Gold glove, 3x Silver Slugger

Ichiro was one of the easiest selections for this Hall of Fame Dream Team. He was a trendsetter — the first Asian position player to debut in the Major Leagues.

In his rookie season, Ichiro set the baseball world ablaze, winning MVP and Rookie of the Year, and leading the league in hits, stolen bases, and batting average.

Ichiro was a revelation in the big leagues, and his game was predicated on speed not power, completely opposite to the direction baseball was trending. According to FanGraphs, Ichiro occupies the first seven spots on the list of highest single-season infield hit totals.

Ichiro was the hit king. He holds the records for most hits in a season (262) and most consecutive 200-hit seasons (10). He also tied the record for most 200-hit seasons (10), and led the league in hits seven times.

Recently, Ichiro reached the 3,000 hit plateau, and if you count his hits from his time in Japan, he broke Pete Rose’s record for most hits across all of baseball’s professional leagues.

In his prime, Ichiro was one of the best players in the world. Only Albert Pujols and Alex Rodriguez accumulated more WAR than Ichiro from 2001 to 2010. On top of being one of the greatest to ever play in the outfield, Ichiro was a cultural icon, and many of the recent advances that Asian players have made are attributable to him.

Batting second and playing left field…

Mike Trout

52.0 career WAR / 52.0 7yr-peak WAR / 52.0 JAWS

Average HOF CF (out of 19):

71.2 career WAR / 44.6 7yr-peak WAR / 57.9 JAWS

14th out of 19 Hall of Fame Center Fielders

Accolades: 2x MVP (2014, 2016), Rookie of the Year (2012), 6x All-Star, 5x Silver Slugger

Trout usually plays center field, but I had to move him over to left in order to accommodate him in the lineup.

Mike Trout is hands-down the best player in baseball right now, and is surely destined for Cooperstown.

Trout has only played five full seasons, but his numbers stack up well next to other center fielders who are enshrined in the Hall. And at just 25 years old, Trout is only entering his prime, meaning that his best years are ahead of him.

Now that’s a stunning thought.

Trout also has the sixth-best seven-year peak WAR out of the 24 center fielders in Cooperstown, in only five seasons!

Here I am talking about how Trout is a generational talent, and I haven’t even mentioned the countless honors that he has collected. Trout has made the All-Star team (for which he has won MVP twice), taken home a Silver Slugger, and been either MVP winner (twice) or runner-up (three times) in every season of his career.

That level of dominance is mind-boggling and completely unprecedented.

Batting third as the designated hitter…

Miguel Cabrera

70.0 career WAR / 44.6 7yr-peak WAR / 57.3 JAWS

Average HOF 1B:

66.4 career WAR / 42.7 7yr-peak WAR / 54.6 JAWS

10th in JAWS out of 20 Hall of Fame First Basemen

Accolades: Triple Crown (2013), 2x MVP (2012, 2013), 11x All-Star, 7x Silver Slugger, World Series Champion (2003)

Miguel Cabrera, still one of the best players in baseball, is a generational talent and already a surefire Hall of Famer. The Venezuelan has been tearing up the big leagues ever since debuting in 2003, and has brought a cheerful smile and a love of the game to wherever he plays.

In the beginning of his career, Cabrera was a young star on the Florida Marlins, one of the youngest teams in baseball. He experienced success early on when the Marlins won the World Series in his rookie year. Then, after a blockbuster trade to the Detroit Tigers in 2007, he continued to amaze in the American League.

From 2011 to 2015, Cabrera was the most feared hitter in all of baseball. During that time, he won four batting titles, took home two MVPs, and racked up five All-Star selections. In 2013, Cabrera captured the Triple Crown (leading the league in batting average, home runs, and RBIs), a feat that had not been accomplished since 1967.

Cabrera already has 2,598 hits and 458 home runs as of July 22nd, so he has a good chance to join Hank Aaron, Willie Mays, and Alex Rodriguez as the fourth member of the 3,000 hit and 600 home run club. Cabrera’s near-incomparable match of hitting for both power and average have vaulted him into the conversation as one of the best hitters of all time.

Batting cleanup and playing first base…

Albert Pujols

100.1 career WAR / 61.6 7yr-peak WAR / 80.8 JAWS

Average HOF 1B:

66.4 career WAR / 42.7 7yr-peak WAR / 54.6 JAWS

2nd in JAWS out of 20 Hall of Fame First Basemen

Accolades: 3x MVP (2005, 2008, 2009), Rookie of the Year (2001), 10x All-Star, 2x Gold Glove, 6x Silver Slugger,  World Series Champion (2006, 2011)

The easiest choice on the roster, Albert Pujols should make the Hall of Fame on the first ballot. Much like Pujols’ overflowing trophy cabinet, I don’t have room enough to praise Pujols, truly one of the greatest players ever.

Pujols has faded since he signed with the Angels on a 10-year, $240-million contract in 2012, but don’t let his struggles of late affect your judgement on his case for the Hall of Fame. He trails only Lou Gehrig in career WAR among first basemen, and is one of only 21 position players to record 100 career WAR.

Pujols’ nickname “The Machine” was an apt description of his time as a Cardinal. His 162-game average stats for his 11 years in St. Louis were: .328/.420/.617 with 127 RBIs, 123 runs, and 43 home runs. Pujols finished in the top 10 of the MVP voting all 11 years, ending up in the top five in ten seasons, and winning the award three times. But Pujols isn’t just a slugging first basemen, he is a very capable defender and has won two Gold Gloves.

Pujols became the ninth member of the 600 home run club earlier this year, and next year he should join the 3,000 hit club (as of July 22nd he has 2,908 hits). Pujols leaves a legacy as one of the best ever, and he deserves to be enshrined in Cooperstown.

Batting fifth and manning the hot corner…

Adrian Beltre

91.5 career WAR / 49.7 7yr-peak WAR / 70.6 JAWS

Average HOF 3B:

67.5 career WAR / 42.8 7yr-peak WAR / 55.2 JAWS

5th in JAWS out of 13 Hall of Fame Third Basemen

Accolades: 4x All-Star, 5x Gold Glove, 4x Silver Slugger

Adrian Beltre, still chugging along at the ripe age of 38, has graced baseball with his presence for 20 seasons. From hitting home runs off one knee, to his aversion of people touching his head, Beltre is one of the true characters of the game.

Beltre is third all-time in WAR among third basemen, trailing only Mike Schmidt and Eddie Mathews. He also figures to be the next member of the 3,000 hit club, needing only 15 more hits as of July 22nd. And if he decides to come back and play next year, he has a great chance of overtaking Brooks Robinson for most games played at third base.

Those are just some of the records that Beltre is approaching, and he does not seem to be slowing down.

There is just no debate on Beltre’s Hall of Fame candidacy. Among all third basemen, he ranks in the top five in games played, hits, doubles, home runs, RBIs, and WAR.

Beltre’s legacy will be as one of the best defensive third basemen of all time, and he trails only Brooks Robinson in Defensive WAR among players who have manned the hot corner. His highlight reel of diving stabs, barehanded picks, and throws from all the way across the diamond make him one of the best ever to play third base.

Special thanks to for all of these helpful stats. I could not have written this article without them.

Thanks for reading Part I. Part II will be released at a later date and it will include spots 6-9 in the batting order as well as the starting pitcher.

To be continued…

Christian Yelich, Fly Balls, and a New Hope

Christian Yelich is a very good baseball player. Since becoming a full-time major leaguer in 2014, Yelich has accumulated 13.8 Wins Above Replacement, good for 35th among qualified hitters. Yelich owns a career 120 wRC+, showing he’s a fine hitter. Yet there has always been a lingering question: Can his bat be even better?

Yelich hits the ball hard. Since 2016, only 10 players have a greater average exit velocity (minimum 2500 pitches seen). More importantly, his 94.3 MPH exit velocity off of fly balls is 25th from the same group. If we add in line drives with fly balls, Yelich’s 95.7 MPH exit velocity ranks 17th, sandwiched in between Manny Machado and Yasmany Tomas. Exit velocity is only part of the story, though. His launch angle is not ideal. Despite hitting the ball more than a mile harder than sluggers such as Bryce Harper, Michael Conforto, and Anthony Rizzo, Yelich has routinely chosen a ground-ball-based approach. Since the All-Star break, we might have gotten another indication of a possible transformation. The prospects are tantalizing. Have always been tantalizing.

Last season, Yelich saw his wRC+ rise to 130, the best of his career. This was partly related to him increasing his power level, as shown by a .185 ISO, the highest of his career. No doubt like every other batter, he was aided by a mysterious force (most likely the ball), but he also had a slight approach change. Yelich hit more fly balls, and so far in 2017, he’s expanded on that. Yelich has the 35th highest (122 players) difference between his 2016 fly-ball rate and 2017 fly-ball rate (minimum 350 plate appearances in both seasons). Slowly, Yelich might just be embracing the fly-ball revolution. This is also seen in his launch angle. In 2016, Yelich’s average launch angle was 2.5 degrees. In 2017, it’s 4.9 degrees, nearly double (more on this later).

Yelich’s 15 Game Rolling GB% and FB%

Yelich seems to have committed to some sort of approach in which fly balls are more sought after. In September of last year, Yelich carried a fly-ball rate at nearly 30%. He began April hitting fly balls at a 27.2% clip, followed by 23.6% in May, and to a low 14.1% in June. He seemed to abandon the fly-ball approach as his results weren’t up to his standards. Have you ever done something you were excited about but didn’t do well that you sort of slowly stopped? I’d imagine something like that may have happened with Yelich. During the second half so far, his fly-ball rate is 32.3%! It could very well be the result of small sample size, but it could also be a sign of Yelich looking to become a better hitter. Since the All-Star break, the Marlins outfielder’s average launch angle has been 10.4 degrees. This is what we want to see. And interestingly enough:

Yelich’s 15 Game Rolling GB% and FB%

We haven’t really seen Yelich be at this power level. He’s had spikes for sure, but nothing as high as the power streak he has shown recently. It coincides with him lifting the ball more. Since the start of the second half, Yelich has a .250 ISO. To give you an idea of the type of power output, that’s pretty much what Anthony Rizzo and Miguel Sano have this season (both at .247).

This feeds into what I mentioned above with psychological factors possibly playing a role. Yelich is seeing good results; perhaps he may experiment a little more with a greater emphasis on fly balls.

As mentioned above, Yelich hits the ball hard. But he also hits it hard to all fields. This is just another example of the kind of strength that exists within Yelich and his all-fields approach making him a tough out. Being able to hit the ball to the opposite part of the park with authority is a rare skill. It’s one of the reasons why Rafael Devers is such an exciting prospect.

View post on

Now combine that all-field power with solid zone control and you’ve got a good hitter. Then combine someone who is has a better batted-ball mix and you might just end up with a great hitter. If Yelich shows more power, which his 6’3″, 195lb figure suggests is there, Yelich will likely be given more free passes. Basically, Yelich has the tools to be that rare hitter than can hit for average and power.

Back to the launch angle which has nearly doubled — FanGraphs Andrew Perpetua recently had an intriguing article advising caution when using Launch Angle. In the article, Andrew writes, “Launch angle is largely dependent on the particular swing and approach of a given batter. If they have an uppercut, then they will produce high launch angles with their high-velocity balls. If they swing down on the ball, then they will have lower launch angles with their high-velocity balls.” Furthermore, Andrew mentions in the comments, “I think launch angle is so intimately tied with swing mechanics that you probably shouldn’t talk about it outside the context of swing mechanics.” This does make sense. Hitters need to alter their bat path to hit the ball at specific angles. Bringing it back to Yelich, we can try to see if he has altered his mechanics. Take the following with a massive grain of salt because it’s only a couple of videos, and I’m no swing expert. From the videos I’ve seen of Yelich, he seems to have a pretty smooth swing path and uses a leg kick for additional power. Here are two of his home runs this year: the first from June 2 against the Diamondbacks and the second from July 26 against the Rangers.

I don’t see a major difference. A bit of a stronger leg kick in the homer against the D’Backs.

In both of these videos, Yelich hits an opposite-field double. Against the Braves, Yelich seems to do a double leg kick. He did this in the next game as well. It’s not something that I’ve seen stick. I’d imagine it might have been due to seeing something he may not have been expecting. Either way, it must’ve been an interesting conversation between Yelich and the hitting coach.

From the limited video evidence, I can’t decipher much. Someone more experienced might want to look into it. The numbers show Yelich very well may have altered his bat path slightly.

One of the criticisms of Yelich was his lack of damage done when pulling the ball. He’s been the fourth-best hitter when going opposite field over the past three calendar years.

View post on

On the plus side, this is another area of improvement for Yelich.

View post on

Christian Yelich could very well remain a ground-ball-heavy hitter and be one of the better hitters in the majors. His plate approach has been lauded for many years to go along with his full-field power. If he is part of the fly-ball revolution, Yelich very well could be one of the best hitters in the game. He’s shown signs of a different approach. With the results to back it up, the rest of the season will give us a glimpse into the hitter that Yelich both wants to be and could be.

Do Big-Name Trades Have an Impact on the Division?

I can’t remember if it was in a podcast or over the radio but when the trade deadline was approaching, there was talk about the effects of how a team trading away their stars would affect the playoff picture. Not in the way where a team has a hole in their rotation so they trade for a solid starter. No, this piece was talking about how trading a great player would make it easier for teams in that division to get ahead and how the newly-acquired player would make his new division harder to play in.

My first thought was there’s no way a player’s performance can impact a division so heavily, right? Baseball is a team sport and while affecting their own roster is one thing, affecting the outcome of four other teams in the process seems like a stretch. So I did a little bit of digging and here’s what I found.

For this study, I’ve included players that had a WAR of 2 or greater before being traded from 2007-2016. Additionally, I gathered data from the day they were traded of their old team’s winning percentage, new team’s winning percentage, old division’s winning percentage, and new division’s winning percentage. I also took the difference of their WAR per games played before and after the trade as a percentage.


Player Year Hitter/Pitcher New Team Old Team WAR/G Dif New Team Win% Change Old Team Win% Change New Div Win% Change Old Div Win% Change Playoffs
Drew Pomeranz 2016 P Red Sox Padres -75.7% 0.53% -1.64% 0.25% -3.96% ALDS
Carlos Beltran 2016 H Rangers Yankees -91.3% 0.17% 2.77% 0.21% 0.29% ALDS
Jonathan Lucroy 2016 H Rangers Brewers 18.5% 0.17% -0.22% 0.21% 1.12% ALDS
Alex Wood 2015 P Dodgers Braves -50.0% 1.61% -9.01% -3.53% 3.05% NLDS
David Price 2015 P Blue Jays Tigers 39.3% 13.66% -6.12% 0.55% -0.67% ALCS
Scott Kazmir 2015 P Astors Athletics -100.0% -4.67% -7.49% -0.10% 0.29% ALDS
Cole Hamels 2015 P Rangers Phillies -22.6% 10.82% 1.04% -1.87% 0.79% ALDS
Johnny Cueto 2015 P Royals Reds -46.4% -3.62% -11.83% -1.02% 2.56% Won
Austin Jackson 2015 H Cubs Mariners -64.9% 5.27% 1.52% -2.95% 1.41% NLCS
Yoenis Cespedes 2015 H Mets Tigers 20.8% 7.96% -5.15% -1.24% -0.19% World Series
Jeff Samardzija 2014 P Athletics Cubs 6.3% -11.85% -0.22% 1.33% -4.29% Wild Card
David Price 2014 P Tigers Rays 26.6% 0.72% -3.26% 0.88% -0.10% ALDS
John Lester 2014 P Athletics Red Sox -28.4% -11.99% -1.35% 3.60% -0.57% Wild Card
Yoenis Cespedes 2014 H Red Sox Athletics -1.0% -1.35% -11.99% -0.57% 3.60% No
John Lackey 2014 P Cardinals Red Sox -47.5% 4.32% -1.35% -1.50% -0.57% NLCS
Marlon Byrd 2013 H Pirates Mets -42.6% 0.00% 0.66% 0.25% 0.98% NLDS
Shane Victorino 2012 H Dodgers Phillies -4.7% -0.38% 11.86% 5.71% -1.90% No
Adrian Gonzalez 2012 H Dodgers Red Sox -2.4% -2.21% -9.75% 1.04% 2.12% No
Anibal Sanchez 2012 P Tigers Marlins -2.0% 0.18% -9.17% -3.91% 3.78% World Series
Omar Infante 2012 H Tigers Marlins -51.7% 0.18% -9.17% -3.91% 3.78% World Series
Zack Greinke 2012 P Angels Brewers -47.6% 0.73% 13.78% 2.15% -4.67% No
Ubaldo Jimenez 2011 P Indians Rockies -18.2% -3.14% -5.45% -0.26% 3.37% No
Edwin Jackson 2011 P Cardinals White Sox -63.5% 5.10% -1.41% 1.94% -0.83% Won
Michael Bourn 2011 H Braves Astros -19.3% -5.02% 6.79% -3.17% 1.74% No
Doug Fister 2011 P Tigers Mariners 44.8% 12.05% -2.59% -4.44% 1.35% ALCS
Hunter Pence 2011 H Phillies Astros 122.2% -0.94% 6.79% -4.38% 1.74% NLDS
Carlos Beltran 2011 H Giants Mets -25.8% -8.61% -7.59% 4.32% -2.12% No
Roy Oswalt 2010 P Phillies Astros 12.4% 9.11% 12.74% -2.23% 0.34% NLCS
Alex Gonzalez 2010 H Braves Blue Jays -75.4% -4.91% 6.28% 0.35% -1.16% NLDS
Dan Haren 2010 P Angels Diamondbacks 35.0% -4.08% 7.22% -3.83% 3.40% No
Cliff Lee 2010 P Rangers Mariners -31.1% -4.30% -4.56% -1.65% -1.90% World Series
Victor Martinez 2009 H Red Sox Indians 43.1% -0.34% -3.84% -1.95% 0.98% NLDS
Scott Rolen 2009 H Reds Blue Jays -29.0% 3.63% -2.73% -3.39% -1.34% No
Cliff Lee 2009 P Phillies Indians 12.8% -1.71% -3.84% 2.42% 0.98% World Series
Matt Holliday 2009 H Cardinals Athletics 37.1% 5.05% 9.98% -4.89% -2.01% NLDS
Xavier Nady 2008 H Yankees Pirates -47.5% -1.79% -11.16% -0.37% 1.48% No
Manny Ramirez 2008 H Dodgers Red Sox 88.7% 3.80% 4.64% 2.05% -0.71% NLCS
CC Sabathia 2008 P Brewers Indians 87.3% 0.91% 19.05% -0.55% -3.51% NLDS
Mark Teixeira 2008 H Angels Braves 96.4% -1.44% -3.06% -4.11% 2.19% ALDS
Kyle Lohse 2007 P Reds Phillies -30.8% 3.00% 4.47% -1.06% 0.25% NLDS
Mark Teixeira 2007 H Braves Rangers 99.8% -0.76% 4.51% 0.20% -1.06% No
Kenny Lofton 2007 H Indians Rangers -66.3% 1.72% 3.58% -4.23% -0.81% ALCS

First things first, let’s see if a great player can really impact a divisional outcome. Out of the 42 players in this study, only six (14.3%) had a positive WAR/G difference, a positive difference in winning percentage of their old division, and a negative difference in winning percentage of their new division:

Victor Martinez – 2009

Doug Fister – 2011

Hunter Pence – 2011

Mark Teixeira – 2008

Roy Oswalt – 2010

CC Sabathia – 2008

For Fister, Oswalt, and Sabathia, their new teams’ win percentage improved. For Martinez, Pence, and Teixeira, the win percentage decreased. All teams made the playoffs, however, with Fister and Oswalt making in to their respective league championship games. It’s interesting to see that the three players whose teams’ win percentage also improved are all pitchers, while the other three were all hitters.

The split between hitters and pitchers in the study was right down the middle, with 21 pitchers and 21 hitters. After their respective trades, 16 out of the 42 players had a positive WAR/G differential. Again, the results were right down the middle, with eight pitchers and eight hitters posting the positive WAR/G difference. Looking at the 26 players that had a negative WAR/G differential after the trade, you could’ve guessed it; half (13) were pitchers and the other half were hitters. I’m not 100% sure what that could mean, but I found it as a fascinating observation.

Out of the 42 teams that made trades in this study, three were under .500 when they made the trade; Reds for Scott Rolen (missed the playoffs), Red Sox for Cespedes (missed the playoffs), and Rangers for Hamels (ALDS). Let’s see how the rest of the teams that were .500 or better fared with their new trade pieces:

No Playoffs – 9 (23%)

Wild Card – 2 (5.1%)

DS – 14 (35.9%)

CS – 7 (17.9%)

WS – 5 (12.8%)

Won – 2 (5.1%)

It should be noted that the WAR/G differential doesn’t include playoff statistics. This is important to note while looking at players in this study that went to or won the World Series. For example, in 2015 the Royals acquired Johnny Cueto from the Reds. Looking at the data alone, Cueto had a -46.4% WAR/G differential and the Royals’ winning percentage dropped by 3.62% after the trade. Looks like a bad trade so far. Fast-forward to the ALCS where Cueto gives up eight earned runs in two innings against the Blue Jays. This trade looks like a disaster. Until Cueto takes the mound against the Mets in Game 2, allowing one run on two hits for the complete-game victory, edging the Royals closer to a World Series title. If given the opportunity again, do the Royals make the trade? Absolutely.

On the other side of the spectrum is Edwin Jackson, the only other player in this study to win the World Series. He as well sported a -63.5% WAR/G differential after the trade. The next question would be, would the Cardinals make the trade again? With a 5.76 ERA that postseason, my guess would be no.

The main question in this study is, “Does an impact player have so much influence in the game around them that they can shift the outcomes of a division?” The quick answer, and one that I’m sure everyone already knew, is not really. There is no correlation between the new division winning percentage change and the old division winning percentage change. A lot of the outcomes of divisional win percent changes seem to be circumstantial. Just because the new team’s division has gotten worse and the old division has gotten better doesn’t always mean that it’s the result of the player. It does seem apparent that a pitcher may have more of an influence than a hitter in these terms however (see Sabathia, Oswalt, and Fister above).

The biggest takeaway for me is that teams seem to be reluctant to overpay and make the smaller, longer-term deals as opposed to big-name rentals as seen at the deadline this year. It’s become apparent that just because you make the trade for the big-name player doesn’t guarantee a World Series victory, trip, or even a spot in the playoffs. Speaking of those big pieces, it will also be interesting to see how Quintana and Darvish affect the data after the season is over. Additionally, I would love to see the implications of a Harper or Trout trade to see if a hitter can ever truly be able to affect a divisional outcome. We can only dream.

Home Runs and Temperature: Can We Test a Simple Physical Relationship With Historical Data?

Unlike most home-run-related articles written this year, this one has nothing to do with the recent home run surge, juiced balls, or the fly-ball revolution. Instead, this one’s about the influence of temperature on home-run rates.

Now, if you’re thinking here comes another readily disproven theory about home runs and global warming (a la Tim McCarver in 2012), don’t worry – that’s not where I’m going with this. Alan Nathan nicely settled the issue by demonstrating that temperature can’t nearly account for the large changes in home-run rates throughout MLB history in his 2012 Baseball Prospectus piece.

In this article, I want to revisit Nathan’s conclusion because it presents a potentially testable hypothesis given a large enough data set. If you haven’t read his article or thought about the relationship between temperature and home runs, it comes down to simple physics. Warmer air is less dense. The drag force on a moving baseball is proportional to air density. Therefore (all else being equal), a well-hit ball headed for the stands will experience less drag in warmer air and thus have a greater chance of clearing the fence. Nathan took HitTracker and HITf/x data for all 2009 and 2010 home runs and, using a model, estimated how far they would have gone if the air temperature were 72.7°F rather than the actual game-time temperature. From the difference between estimated 72.7°F distances and actual distances, Nathan found a linear relationship between game-time temperature and distance. (No surprise, given that there’s a linear dependence of drag on air density and a linear dependence of air density on temperature.) Based on his model, he suggests that a warming of 1°F leads to a 0.6% increase in home runs.

This should in principle be a testable hypothesis based on historical data: that the sensitivity of home runs per game to game-time temperature is roughly 0.6% per °F. The issue, of course, is that the temperature dependence of home-run rates is a tiny signal drowned out by much bigger controls on home-run production [e.g. changes in batting approach, pitching approach, PED usage, juiced balls (maybe?), field dimensions, park elevation, etc.]. To try to actually find this hypothesized temperature sensitivity we’ll need to (1) look at a massive number of realizations (i.e. we need a really long record), and (2) control for as many of these variables as possible. With that in mind, here’s the best approach I could come up with.

I used data (from Retrosheet) to find game-time temperature and home runs per game for every game played from 1952 to 2016. I excluded games for which game-time temperature was unavailable (not a big issue after 1995 but there are some big gaps before) and games played in domed stadiums where the temperature was constant (e.g. every game played at the Astrodome was listed as 72°F). I was left with 72,594 games, which I hoped was a big enough sample size. I then performed two exercises with the data, one qualitatively and one quantitatively informative. Let’s start with the qualitative one.

In this exercise, I crudely controlled for park effects by converting the whole data set from raw game-time temperatures (T) and home runs per game (HR) to what I’ll call T* and HR*, differences from the long-term median T and HR values at each ball park over the whole record. Formally, for any game, T* and HR* are defined such that T* = T Tmed,park and HR* = HR – HRmed,park, where Tmed,park and HRmed,park are median temperature and HR/game, respectively, at a given ballpark over the whole data set. A positive value of HR* for a given game means that more home runs were hit than in a typical ball game at that ballpark. A positive value for T* means that it was warmer than usual for that particular game than on average at that ballpark. Next, I defined “warm” games as those for which T*>0 and “cold” games as those for which T*<0. I then generated three probability distributions of HR* for: 1) all games, 2) warm games and 3) cold games. Here’s what those look like:

The tiny shifts of the warm-game distribution toward more home runs and cold-game distribution toward fewer home runs suggests that the influence of temperature on home runs is indeed detectable. It’s encouraging, but only useful in a qualitative sense. That is, we can’t test for Nathan’s 0.6% HR increase per °F based on this exercise. So, I tried a second, more quantitative approach.

The idea behind this second exercise was to look at the sensitivity of home runs per game to game-time temperature over a single season at a single ballpark, then repeat this for every season (since 1952) at every ballpark and average all the regression coefficients (sensitivities). My thinking was that by only looking at one season at a time, significant changes in the game were unlikely to unfold (i.e. it’s possible but doubtful that there could be a sudden mid-season shift in PED usage, hitting approach, etc.) but changes in temperature would be large (from cold April night games to warm July and August matinees). In other words, this seemed like the best way to isolate the signal of interest (temperature) from all other major variables affecting home run production.

Let’s call a single season of games at a single ballpark a “ballpark-season.” I included only ballpark-seasons for which there were at least 30 games with both temperature and home run data, leading to a total of 930 ballpark-seasons. Here’s what the regression coefficients for these ballpark-seasons look like, with units of % change in HR (per game) per °F:

A few things are worth noting right away. First, there’s quite a bit of scatter, but 75.1% of these 930 values are positive, suggesting that in the vast majority of ballpark-seasons, higher home-run rates were associated with warmer game-time temperatures as expected. Second, unlike a time series of HR/game over the past 65 years, there’s no trend in these regression coefficients over time. That’s reasonably good evidence that we’ve controlled for major changes in the game at least to some extent, since the (linear) temperature dependence of home-run production should not have changed over time even though temperature itself has gradually increased (in the U.S.) by 1-2 °F since the early ‘50s. (Third, and not particularly important here, I’m not sure why so few game-time temperatures were recorded in the mid ‘80s Retrosheet data.)

Now, with these 930 realizations, we can calculate the mean sensitivity of HR/game to temperature, resulting in 0.76% per °F. [Note that the scatter is large and the distribution doesn’t look very Gaussian (see below), but more Dirac-delta like (1 std dev ~ 1.66%, but middle 33% clustered within ~0.4% of mean)].

Nonetheless, the mean value is remarkably similar to Alan Nathan’s 0.6% per °F.

Although the data are pretty noisy, the fact that the mean is consistent with Nathan’s physical model-based result is somewhat satisfying. Now, just for fun, let’s crudely estimate how much of the league-wide trend in home runs can be explained by temperature. We’ll assume that the temperature change across all MLB ballparks uniformly follows the mean U.S. temperature change from 1952-2016 using NOAA data. In the top panel below, I’ve plotted total MLB-wide home runs per complete season (30 teams, 162 games) season by upscaling totals from 154-game seasons (before 1961 in the AL, 1962 in the NL), strike-shortened seasons, and years with fewer than 30 teams accordingly. In blue is the expected MLB-wide HR total if the only influence on home runs is temperature and assuming the true sensitivity to be 0.6% per °F. No surprise, the temperature effect pales in comparison to everything else. Shown in the bottom plot is the estimated difference due to temperature alone in MLB-wide season home run totals from the 1952 value of 3,079 (again, after scaling to account for differences in number of games and teams). You can think of this plot as telling you how many of the total home runs hit in a season wouldn’t have made it over the fence if air temperatures at remained constant at 1952 levels.

While these anomalies comprise a tiny fraction of the thousands of home runs hit per year, one could make that case (with considerably uncertainty admitted) that as many as 59 of these extra temperature-driven home runs were hit in 2016 (or about two per team!).

wOBA Flippers and the Playoff Charge

Early on in a season, we get to talk about eye-popping numbers that players put up. We warn of sample sizes, though, and almost crave stability. We wait impatiently for the season to steady itself and almost breathe a sigh of relief when it happens — when we can start to buy into what an individual is doing.

But as the season wades on and we move toward the postseason, the biggest stories often come from singular moments. And while we can’t predict who, exactly, will define his team’s season with a single play, we might be able to take a pretty good guess.

With weighted on-base average from Statcast, we get to see just how much a player is contributing each time they step to the plate. With expected weighted on-base average, we get to see how well their results line up with their approach.

woba flippers

The differences in expected and actual wOBA for these players in the early going is no small thing. The 20-to-45 point gap would have put them in a completely different class of players had things gone as expected. Manny Machado figured to rank ahead of Kris Bryant; in reality, he lingered above Freddy Galvis. There’s an example like that for each of the other three, too. While the early performances of these guys might have lasted long enough to make us feel like they were a certain kind of reliable this season, their recent play highlights how fast things can change.

The rankings associated with each player give a sense of what their teams would have enjoyed had circumstances fell more in their favor. Rankings aren’t included since the start of July because the sample size may emphasize a gap that could be misleading — Kyle Seager, for instance, has the smallest difference of the four in wOBA-based production but drops 76 spots because of it.

That’s also to intentionally emphasize something else: all of these players’ teams are in the playoff hunt. Seager’s Mariners are tied for the Wild Card lead and Machado’s Orioles, despite abysmal pitching, are only 1.5 games out. Moreland’s Red Sox and Santana’s Indians each lead their division by four games. And for better or worse, their turnarounds could be playing a big role in who’s playing in October.

So consider the implications. Do the Mariners possibly lead the Wild Card at this point if Seager’s production more closely matched what was expected? Are the Orioles smashing expectations again if the same were true for Machado?

Could Santana have delivered a more comfortable divisional lead for Cleveland earlier? Is he doing that now by exceeding expectations with a white-hot bat? Moreland broke his toe in June — what impact has that had on the Red Sox building similar divisional comfort, and how big of a role could him simply being able to put pressure on his back foot play?

The answers to these questions may or may not be rhetorical, but all of these players are having a string of moments that could help define their team’s season. While we’ve longed for stable samples to dig into, their turns in production are showing us the ebb and flow of a game that remembers snapshots more than anything. As we come down to the wire, the big picture is telling us how it’s constructed of little ones.

Ronald Acuna Is Already Setting Records by Improving at Every Level

There have been several surprising prospect performances this year, but the one that seems to top the rest has been Atlanta Braves outfielder Ronald Acuna. The 19-year-old began his season in high-A and, by my notes, was unranked in the MLB pipeline top-100 (he was 36th on Keith Law’s list and 67th on Baseball America).

Fast forward to now, and Acuna has been promoted twice and is a consensus top-10 prospect. In the recent prospect hot sheet chat, Baseball America’s Josh Norris speculated that Acuna was a frontrunner for minor league player of the year and next season’s #1 prospect overall.

The aspect that I could not get my head around is that Acuna is improving faster than he can be promoted. With at least 100 PA at each level, he has improved every relevant rate stat with each stop.

Name Team Age G AB PA HR AVG BB% K% OBP SLG OPS ISO wRC wRC_plus
Ronald Acuna Braves (A+) 19 28 115 126 3 0.287 6.30% 31.70% 0.336 0.478 0.814 0.191 18 135
Ronald Acuna Braves (AA) 19 57 221 243 9 0.326 7.40% 23.00% 0.374 0.52 0.895 0.195 41 159
Ronald Acuna Braves (AAA) 19 24 92 107 4 0.348 12.10% 18.70% 0.434 0.576 1.01 0.228 22 183

This led me to answer the question of whether this had ever been observed before.

I pulled every minor league player season from 2006-2017 (the farthest back FanGraphs can go). I filtered on at least 100 PA, and consolidated a list where players appeared in at least three levels. I used wRC+ as a catchall stat for offensive production and scored players that improved their output in the jump from one level to the next.

It is worth observing that this list is quite artificial — not many players appear in three different minor league teams in a single season, and only 22 have done so in the past 11 years. And the list certainly isn’t a who’s-who of top prospects, so take this analysis with a grain of salt.

Since 2006, Acuna is the only minor-league player to have posted improved wRC+ at three different stops in the same season (see chart below, labeled players with blue lines showed improvement in at least one jump). While Acuna’s high-A 135 wRC+ wasn’t setting the world on fire, posting a 183 wRC+ at his third league level in a single season is untouchable.

See graph

Owing to Acuna’s excellent hit tool, he is also the only prospect to have posted similar improvements in AVG, OBP or OPS since 2006.

On plate-discipline statistics, the only other player to show the consistent improvement in BB% is Ryan Court (2013). Two other players show a consistent decline in K% across each level (Brett Wallace and Sawyer Carroll in 2009).

For power statistics, Acuna also finds a little company. Two other players showed improvements in isolated power at each level, Tyler Pastornicky in 2015 and Rando Moreno in 2016, though their improvements went from horrific (0.038 and 0.040, respectively) to simply not good (0.111 and 0.082). Pastornicky was the only non-Acuna player to increase his SLG at each level (.314 to .394 compared to Acuna’s .478 to .576).

As was said at the outset, the players that reach 100 PA in three levels in a single season are not any sort of elite bunch. But it is telling that among them, Acuna is the only one that has shown a consistent ability to improve while facing better and older talent. I’m not holding my breath for a 200 wRC+ when Acuna makes it to The Show, but I will be watching.

Scott Rolen’s Case for the Hall of Fame

Scott Rolen will appear on Hall of Fame ballots in 2018. Rolen played professional baseball from 1996 – 2012, and was a premier third baseman whose value was largely underrated due to the fans’, writers’ and even some teams’ lack of acceptance of advanced metrics. In this piece, many of these metrics, along with a few traditional ones, will be used to describe the value that Rolen produced at the plate and at third, a value that is deserving of the Hall of Fame.

Third base has long been a position of heavy hitters, and in the high-powered offense era during which Rolen played most of his career, this may have caused fans to overlook him because he only broke 30 home runs three times in his career. However, we’ll examine Scott Rolen’s worth as a hitter as compared to other players who played the slugger-heavy position of third base. Rolen has 8,495 plate appearances. According to, among third basemen with at least 7,000 plate appearances, Rolen ranks 5th in OBP, 6th in SLG, 5th in Runs Created and 6th in Runs Produced. In all of these categories, Rolen ranks behind players like Chipper Jones, Wade Boggs, Mike Schmidt, George Brett, and Adrian Beltre. It is worth noting that he actually ranks ahead of Brooks Robinson in Runs Created, even though Robinson is generally known for setting the gold standard as a third baseman defensively, not offensively.

Again according to baseball-reference, from 1997-2007, the majority of Rolen’s career, he was 1st in Runs Created, hits, stolen bases, and times on base w/o ROE among third basemen with at least 1,000 plate appearances during that time span. He was 2nd in walks drawn, 3rd in HR, and 7th in OBP in that same period. Scott Rolen played in an era with some of the best third basemen at the plate- Chipper Jones, Alex Rodriguez, Aramis Ramirez, Adrian Beltre- and was consistently one of the best during his career. According to FanGraphs, for third basemen with at least 3000 PA during the entire span of Rolen’s career, he has 119 wRC+, a .360 wOBA, 0.357 OBP, and 148.5 wRAA. That puts him at 7th in wRC+, 6th in wOBA, 5th in OBP, and 5th in wRAA. Furthermore, his 128 wRC+ is higher than Hall of Famer Paul Molitor’s 122, and Molitor made the majority of his plate appearances as a DH.

We can see that Rolen consistently put himself on the short list of the most valuable offensive third basemen during his era, even if he was never considered to be the outright best at the plate. But Scott Rolen added most of his value on defense. During his career, he had four seasons in the top 10 in defensive WAR. According to FanGraphs, his 182.2 Defensive Runs Above Average rank 5th among 3B all time. He led the league twice in putouts and assists, with six seasons in the top 10 for putouts and eight seasons in the top 10 for assists. Wade Boggs, of course a Hall of Fame third baseman, was 95 Total Zone Fielding Runs above Average for his career. George Brett, inducted in 1999, has 54 in 17 seasons. Scott Rolen has 150 Total Zone Fielding Runs above Average for the same number of seasons. Scott Rolen ranks 2nd among 3B behind Adrian Beltre from the seasons of 2002 to 2012 in UZR (109), Defensive Runs Saved (114) and Range Rating (80.5). During 10 of his 17 seasons, he was in the top 10 in Total Zone Runs and Range Factor/9 innings. He also led the league twice in both of those categories. Maybe Rolen was a ‘good-not-great’ hitter, but his defense was nothing short of absolutely stellar.

If you happen to care about certain seasonal awards in Hall of Fame considerations (I certainly don’t, but HoF voters seem to), Rolen was a Rookie of the Year, a Silver Slugger, a seven-time All-Star, and an eight-time Gold Glove winner.

Immensely more important are Scott’s player-value numbers, which make his Hall of Fame case impossible to ignore. FanGraphs gives him a WAR of 70.1, which ranks 10th among 3rd basemen in the history of baseball. He played finished in the Top 10 for defensive WAR four times in his career, and three times in overall WAR. The average WAR for a Hall of Fame third basemen is 67.5. If you aren’t familiar with the JAWS score, developed by statistician Jay Jaffe, it measures whether or not a player is deserving of the Hall of Fame by comparing him to other players in the Hall who played his position. This score also accounts for the different offensive eras throughout the history of the game using advanced metrics, and produces a score that combines a player’s career WAR and his seven-year peak WAR to compare him to current Hall of Famers. The average JAWS score for third basemen in the Hall of Fame is 55.2. Scott Rolen’s is 56.8.

So why does Scott Rolen’s name rarely come up among casual conversations about some of the best third basemen ever? For one, these defensive metrics in which Rolen excelled were not widely accepted or even widely understood during most of the time that he played. Another reason may be that Scott Rolen only appeared in 39 postseason games, and did not play particularly well in those postseason appearances. Postseason appearances, especially when there is such a small sample size in the case of Scott Rolen, should not be a make or break factor in Hall of Fame consideration; but, there are still a decent amount of voters who look for that.

Whatever the reasons may be, Scott Rolen’s case is more than strong with the application of advanced statistics. The Hall of Fame is strangely lacking in third basemen, holding only 16 currently. To put that in context, there are 10 umpires enshrined and 23 players from the other corner of the infield. Hopefully, the BBWAA can begin to fix this imbalance, and they could start by inducting Scott Rolen, truly one of the greatest third basemen of the last two decades.

Altuve Is Defying the Evolution of Baseball

In 1912, the now-known as International Association of Athletics Federations recognised the first record in the 100 metres for men in the field of Olympics’ athletics. Donald Lippincott, on July 6, 1912, became the first man to hold an official record on the discipline with a time of 10.2 seconds from start to finish. He measured 5’10’’ and 159 lbs. It wasn’t until 1946 – 34 years later – that a man broke the 10-second barrier in the 100 meters. James Ray Hines did it at 6’0’’ and 179 lbs. Now fast-forward to 2009 and look up a name: Usain Bolt. There is no one faster on Earth. The Jamaican set the 100 metres world record (9.69 seconds) in Berlin holding a size of 6’5’’ and 207 lbs. I don’t think it is hard to see the evolution of the athletes’ bodies here. We, as human beings, are becoming taller and stronger, physically superior each year. At least some.

While we can’t compare the MLB and baseball as is with Olympic athletes and the demands of track and field, the evolution of sportsmen have been parallel to some extent between both fields. Look at this season’s sensation Aaron Judge. He’s huge. He’s a specimen of his own, truly unique in his size and power. Basically, he’s what we may call the evolution of the baseball player made real. Given that we have height and weight data from 1871 to 2017 provided by we can plot the evolution of both the height and weight of MLB players over the past 146 years. Here are the results.

Unsurprising, if anything. As we could expect, small baseball players populated the majors during the XIX century and the first third of the XX one, only to get reduced to a minimum that has never got past three active players of 67 inches or less for the past 61 years. On the contrary, players taller than 78 inches started to appear prominently in the 60’s and 70’s to reach their most-active peak in 2011 with 72 players spread over multiple MLB rosters. A similar story can be told about the weight of ballplayers, who tended to be lighter in the early days of the game than from the 70’s on, starting to be overcome in presence by heavier players at around the mid-to-late 90’s.

But even with as clear a trend as this is, there are always outliers out there. And in this concrete case of player size, Jose Altuve is defying the rules of evolution by no small margins. At 5’6’’, the Venezuelan is the shortest active MLB player, and he started painting his path to the majors by signing with Houston for a laughable $15,000 international bonus after being rejected earlier by the Astros due to him being too short. This happened in 2007, and by 2011 Jose Altuve was already playing in the MLB and finishing his rookie season with an 0.7 bWAR (good for 5th-best among 21 years old-or-less rookies, tied with RoY Mike Trout). By his second season, Altuve made the All-Star Game, became a staple at Houston’s second-base position and posted a 1.4 bWAR. From that point on he’s had seasons valued at 1.0, 6.1, 4.5, 7.6 and 6.2 bWAR. The next table includes the 20+ bWAR – during their first seven seasons playing in the majors – players of height 5’6’’ or smaller the MLB has seen since 1871.

Look at the debut season of all those players. Of the eight that made the list, two are from the XIX century and five from 1908 to 1941. That is, the closest “small” player with a 20+ bWAR during his first seven seasons of play to Jose Altuve is from more than 75 years ago – and Altuve’s yet to finish the 2017 season, which will probably enlarge his bWAR total.

Focusing on the 2017 season, a total of 1105 position players and pitchers have generated offensive statistical lines and accrued bWAR values by Here’s how they are distributed in terms of height/bWAR.

It is not hard to see how the average MLB player holds a height of around 72 inches (6’0’’), varying from 69 to 76 in most of the cases. There way taller (Chris Young, Alex Meyer, Dellin Betances) and way smaller (Tony Kemp, Alexi Amarista) outliers, and if we add bWAR to the equation, then there is Jose Altuve. Yes, Altuve is the blue dot in the chart, at the bottom right part of it. Not only is he the shortest player of the league, but he’s also the most valuable at this point (6.2 bWAR by Sunday, August 6) and by a good margin over his closer rivals Andrelton Simmons (5.7), Paul Goldschmidt (5.5), Aaron Judge (5.1) and Mookie Betts and Anthony Rendon (both 5.0).

Not just happy with that, Altuve is leading the league in hits (151, with just an 11.9 K% – 16th-best among qualified hitters), batting average (.365), OPS+ (176) and total bases (238). He has improved in virtually every statistical category during the current season, participated in his fourth consecutive All-Star Game, led the MVP race in the AL, and he’s on pace to get also his fourth Silver Slugger award at the second-base position. Even with all that, the likes of Judge and Trout are coming and finishing the year strongly, and there are no guarantees for Jose to become the first Venezuelan to win the MVP since Miguel Cabrera did it five years ago in 2012.

All in all, and looking at how his top rivals stack up in terms of size and production, their numbers could be somehow expected. What Altuve is doing at his size, though, not so much. We have been told that we’re living in the era of the strikeout and that of that of the home-run resurrection, but Jose is determined to turn back the clock and make us all appreciate the wonders of small ballplayers roaming the majors’ fields. Appreciate it while you can, because what he’s doing is truly unique in the history of the sport and its evolution expectations, although it doesn’t seem like anything will be stopping Jose “Gigante” Altuve any time soon.

An Index to Gauge the Quality of a Four-Seam Fastball

What is SPV?

Have you ever heard about “SPV”?  SPV: Spin rate Per Velocity (spin rate/ velocity) 

This is a useful index to gauge the quality of a four-seam fastball. SPV came up in Japan’s All-Star Game this year and came to baseball fans’ attention.

Many people might think spin rate is the most important metric for measuring the quality of four-seam because we often hear TV commentators talking about it. In MLB, pitchers who throw with a high spin rate have attracted a lot of attention and respect since we started using tracking data from systems such as TrackMan and Rapsodo, but I have some questions about this focus on spin rate, however, and think that we should consider using SPV instead.

As you know, spin rate bears a proportionate relationship to ball speed. It’s hard to guess which factors more effectively into pitching stats, high ball speed or high spin rate. Because the ball speed is high when the spin rate is high. It’s not enough to explain pitching performance only from spin rate.

As a note, Driveline has also written about the metric we call SPV, except they refer to it as “Bauer Units”.

I agree with Driveline’s analysis and would like to present some new data to promote the effective use of SPV. I will also discuss some of the limitations of using SPV and the importance of knowing the direction of the spin axis of a pitch. In the end, I will mention the challenges of promoting the popular use of tracking data in our home market of Japan.

Let’s take a look Fig. 1 below. This graph shows the relationship of spin rate and ball speed, based on a data set from Baseball Savant covering 115,759 four-seam pitches in 2016. As the red line in the graph shows, spin rate is proportionate to ball speed.

Fig. 1 Relationship between spin rate and ball speed, distribution of high SPV and low SPV

When trying to judge the quality of a pitch such as a four-seam, you cannot look in isolation at either the spin rate or ball speed. Using the ratio of SPV makes it possible to compare pitches – for the speed thrown, did the ball have more or less spin (more or less hop) than average?

Effect of SPV on a batted ball

Table 1 and 2 below rank high and low SPV pitchers, respectively. Each of the pitchers threw over 1,000 four-seams in 2016. The data shows that, in general, high-SPV pitchers have a high FB% (fly ball%) and that low SPV pitchers have a high GB% (ground ball%).

Unfortunately, Koji Uehara is not in this ranking, because he didn’t throw over 1,000 four-seams last year. But it would be fun to take a look at his data, since we could know his interesting character from it. As you know, his ball speed is not so high, but his SPV is really high. We can make a good guess about his ball quality. Also, in other pitchers, we can know their character from this data.

I wrote in a paragraph above that spin rate is proportionate to ball speed. A spinning ball obtains lift and appears to break due to the Magnus effect, but this only occurs when the spin axis is tilted perpendicular to the line of ball travel, providing useful spin. In the case of a four-seam, the more the spin axis is tilted toward the horizontal, the more backspin there is, and the backspin creates lift force and apparent hop. If the spin axis tilts towards the direction of ball travel, the component of spin in that direction (gyro spin) does not contribute to ball movement.

A batter at the plate creates a mental image of the ball trajectory. There is an average trajectory for each pitch type, and the batter extrapolates the trajectory from pitching form. In general, balls which follow a familiar trajectory, or have an average SPV, are easier to hit than non-average pitches. For example, when a high-SPV pitcher throws a four-seam, the lift force obtained from useful spin is higher than average, causing the ball to hang in the air, or hop, more than the batter had expected. In other words, when a ball’s trajectory is higher than average, the batter tends to swing low and hit a fly ball.

On the other hand, when a low-SPV pitcher throws a four-seam, the ball is affected by less lift than average pitches, and therefore drops more than expected, leading to an increase in ground balls.

Limitations of SPV

SPV is useful and a great index to know the quality of a ball, but it also has a negative side. As discussed above and according to a study (Jinji T. and Sakurai S, 2006: Direction of Spin Axis and Spin Rate of the Pitched Baseball, Sports Biomechanics), the direction of the spin axis has a huge effect on ball break. For example, the spin of a perfect gyro ball (spin axis is parallel to the direction of travel) does not create lift, regardless of how high the spin rate is. Consequently, a four-seam with a lot of gyro spin does not appear to hop even if the SPV is high.

We also need to consider the direction of the spin axis in order to understand how much of the total spin contributes to the desired ball movement. Table 3 below shows angle of spin axis (the ball direction and the velocity vector of spin rate) for each skill level. This angle was measured by motion capture systems (VICON MX). As the player skill level rises, “α” generally increase, meaning that more of the total spin is backspin and therefore contributes to apparent ball movement. And there is a tolerance in “α” of professional pitchers. So when we gauge the quality of ball, we should care about the effect of spin axis for each player.

We understand that SPV is possible to judge four-seam quality, but some balls are impossible. We might need to see how each is changing toward horizontal direction or vertical direction to judge the pitched ball exactly. A convenient tracking device such Rapsodo is available to measure not only spin rate, but also spin axis. That would help us to know the quality of the ball.

Popularity of tracking data in Japan

Although baseball is very popular in Japan, most baseball fans are not interested in seeing live tracking data or hearing discussions about it when they are watching games. Just recently, in July of this year, a Japanese TV station tried to indicate SPV during a live game, but there was negative feedback from many fans. In the future, I hope Japanese baseball fans take an interest in tracking data and start discussing points, such as how many inches a ball breaks. I expect that the more Japanese fans get exposed to tracking data and become familiar with the concepts, the more they will enjoy it, especially metrics such as SPV.

Are the Yankees Following the Red Sox Blueprint For Success?

The Yankees and Red Sox are battling it out atop the AL East, which brings one back to the early 2000s, when these two teams were virtually competing solely against one another to crown a division champion, with the Yankees more often than not edging out Boston. However, the tables have turned, and since 2004 the Red Sox have three world titles while the Yankees have only had one. In the last two seasons in particular, the Red Sox have relied on the emergence of young prospects, veteran leadership, and savvy trades/free-agent signings to be successful. Are the 2017 Yankees an original creation of Cashman and Steinbrenner, or were they inspired by the strategy employed by other teams in more recent years, such as the Royals, Cubs, and even (in an ironic twist) their arch rivals, the Boston Red Sox?

The Red Sox recently called up highly-talented prospect Rafael Devers to fix their gaping hole at third base, and he has revitalized the lineup. He, along with Xander Bogaerts, can grow to be one of, if not the best 3B/SS combo in the majors, not to mention their presence at the plate, with Bogaerts being considered the best two-strike hitter in all of baseball. The Red Sox also have an outfield stocked with young talent. The Killer B’s (Benintendi, Bradley, and Betts) have each regressed slightly at the plate this season, but are still putting up respectable numbers. Bradley and Betts are also playing outstanding defense, as Betts leads the AL with 2.1 dWAR this season. However, one shouldn’t forget about Dustin Pedroia, who provides veteran leadership to help these young prospects adjust to life in the big leagues while remaining a staple at second base, as well as in the lineup.

One can’t say that the Red Sox rebuilding strategy has been perfect, as they currently have a revolving door at catcher, first base, and DH. They are clearly affected by the departure of David Ortiz’s intimidating reputation in the DH spot. Hanley Ramirez has been productive at the plate, but his defense is less than stellar, to put it mildly. Mitch Moreland and Christian Vazquez are just now getting hot bats after struggling at the onset of this season. More than anything, the Red Sox have been plagued by injuries to their starting pitching, as well as poor free-agent signings, most notably Pablo Sandoval, David Price, and even Rusney Castillo, who many forget is still in AAA-Pawtucket.

Overall, I believe the Yankees have learned a thing or two from the Red Sox. It’s important to give Dave Dombrowski credit for sticking with Devers at third, rather than trying to orchestrate a trade to acquire Josh Donaldson, as tempting as the idea was. The Yankees have groomed a host of young talent including Gary Sanchez, Aaron Judge, and now Clint Frazier. They also made good trades for Sonny Gray and others by not having to give up too many big names within their stacked farm system, and added Matt Holliday in the offseason to add some veteran leadership in the lineup at a low-risk contract. Like the Sox, the Yankees aren’t perfect, and are sitting on their hands with some expensive free-agent contracts (I think I hear Jacoby Ellsbury’s name somewhere). While the Red Sox rebuilding efforts have been more or less successful, I believe the Yankees should look at themselves when deciding how the team will shape out in the coming years. The Yankees from the mid to late 90s are one of the best examples of how teams can keep sustaining success. The Yankees in that era were built with a core group of prospects (the core four comes to mind), some established veterans such as Paul O’Neill and Tino Martinez, and other guys that helped create unbreakable clubhouse chemistry. All of these elements, and also a little bit of luck, are the keys to shaping the next great baseball dynasty, whomever that may be.