Top 10 First Basemen and Designated Hitters

	Official K-Zone Ranking	Mike’s Ranking	Ian’s Ranking	Mojo’s Ranking	Guti’s Ranking
1.	Joey Votto (CIN)	Paul Goldschmidt (ARI)	Paul Goldshmidt (ARI)	Joey Votto (CIN)	Joey Votto (CIN)
2.	Paul Goldshcmidt (ARI)	Joey Votto (CIN)	Joey Votto (CIN)	Freddie Freeman (ATL)	Paul Goldschmidt (ARI)
3.	Freddie Freeman (ATL)	Cody Bellinger (LAD)	Freddie Freeman (ATL)	Paul Goldschmidt (ARI)	Freddie Freeman (ATL)
4.	Anthony Rizzo (CHC)	Anthony Rizzo (CHC)	J.D. Martinez (BOS)	Cody Bellinger (LAD)	J.D. Martinez (BOS)
5.	J.D. Martinez (BOS)	Freddie Freeman (ATL)	Anthony Rizzo (CHC)	Anthony Rizzo (CHC)	Cody Bellinger (LAD)
6.	Cody Bellinger (LAD)	J.D. Martinez (BOS)	Nelson Cruz (SEA)	J.D. Martinez (BOS)	Anthony Rizzo (CHC)
7.	Nelson Cruz (SEA)	Eric Hosmer (SD)	Edwin Encarnacion (CLE)	Jose Abreu (CWS)	Jose Abreu (CWS)
8.	Jose Abreu (CWS)	Nelson Cruz (SEA)	Cody Bellinger (LAD)	Nelson Cruz (SEA)	Edwin Encarnacion (CLE)
9.	Edwin Encarnacion (CLE)	Edwin Encarnacion (CLE)	Jose Abreu (CWS)	Edwin Encarnacion (CLE)	Nelson Cruz (SEA)
10.	Eric Hosmer (SD)	Matt Carpenter (STL)	Eric Hosmer (SD)	Matt Carpenter (STL)	Carlos Santana (PHI)
Sleeper		Carlos Santana (PHI)	Miguel Cabrera (DET) Ryan Braun (MIL) Carlos Santana (PHI)	Carlos Santana (PHI) Justin Bour (MIA)	Justin Bour (MIA)

 

Along with its corner infield counterpart, first base is, as it has been in years past, one of the most talent-filled positions on the diamond. Reds’ 11-year veteran Joey Votto and his .428 career OBP tops the list. He slashed .320/.456/.578 last season with 165 wRC+. Paul Goldschmidt of the Diamondbacks splits the first place votes and comes in as a close second overall after hitting 36 home runs with 18 steals and a .404 OBP. Freddie Freeman, the Braves’ lefty, was putting up a 1.201 OPS before getting injured last May, but still finished the season with a very, very strong 152 wRC+. Anthony Rizzo had a .392 OBP last season despite only lucking into a .273 BABIP, as he looks to continue continue to build on his home run and steal counts in 2018. While he failed to get the $200MM deal he was looking for this offseason, late bloomer J.D. Martinez signed a well-deserved healthy contract to DH in Boston this coming season. Martinez hit 45 home runs in 119 games last season, bringing him to .690 SLG in 2017, including a .741 rate after being traded to Arizona at the deadline. Cody Bellinger broke out for the Dodgers in 2017 after his May promotion, hitting 39 bombs in 132 games. #7 on the list is Mariners’ DH Nelson Cruz, who, at 37 years old, has put together 4 consecutive seasons of 39 home runs, and three seasons of a .360 on-base percentage. He owns a three-year average .925 OPS. Jose Abreu, another model of consistency, has put up an OBP around .350 every season since debuting in 2014, along with plus power. Checking in at #9 is Edwin Encarnacion, who is entering his second year under contract with the Indians. In the past six seasons, he has hit 42, 36, 34, 39, 42, and 38 home runs. He owns a lifetime 11.1 BB% and 16.5 K%. Last on the list in $144 million dollar man Eric Hosmer. Hosmer’s career has been controversial to say the least, but he did put up 135 wRC+ and 4.1 WAR in 2017.

See the top 10 players at every other position here and follow us on Twitter to be the first to know whenever a new list comes out!
Sources:
fangraphs.com
bbref.com

Images Attributed to:
ap.org
allposters.com
espn.com

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Dissecting wRC+

-The K Zone-

March 7th, 2018

Dissecting wRC+, by Ian Joffe

This Year’s Sequel to Dissecting WAR

Today, weighted runs created plus, or wRC+, is the widely the considered the most comprehensive and most widely respected offensive statistic in the game. Last year, because I was tired of feeling obligated to provide context for WAR every time I mentioned it, I put wins above replacement on the dissection table, and just linked that article every time I mentioned WAR. Now, I plan to do the same for wRC+. Weighted runs created plus did not become the gold standard overnight. It took years of sabermetric theory to lead to the inception of wRC+, building up statistical knowledge from a foundation in Bill James and Moneyball and story by story with various different statistical techniques. Each letter in wRC+ represents its own floor in this allegorical skyscraper, and each of those floors needed several others to stand below it. Let’s at the very beginning, with Branch Rickey in 1954.

Ricky developed slugging percentage seven years after signing Jackie Robinson, part of his hall-of-fame string of career achievements. SLG was revolutionary for its time, granting real values to each part of the offensive game. However, as statistics advanced, people began to realize that the values which SLG assigned (1 point for a single, 2 points for a double, 3 points for a triple, and 4 for a home run) are actually rather arbitrary. A double is not worth exactly twice a single, and a home run is not four times a single but only 4/3 times as valuable as a homer. More recently, OPS (on-base plus slugging) tried to more accurately weight each batted ball event, but was still off by a little – it turns out that on-base percentage is about 1.8 times more valuable than isolated power. To figure out the exact values of each of these statistics, sabermatricians created a vital tool to modern stats: the run expectancy matrix. The run expectancy matrix, based on annual data, tells the odds of scoring a run in an inning given a certain outs-count and base runner situation. It also predicts the number of runs scored based on those factors. For example, with runners on the corners and one out, one can expect 1.130 runs to be scored, and there is a .634 chance that at least one run is scored. Here’s the full table from top SABR mind Tom Tango’s website.

It is from the run expectancy matrix that Tango figured out how to derive a value for each type of on-base event. Each hit or walk creates a change in the run expectancy of the innings. The relative changes in run expectancy are the true differences between the types of hits. Those relative differences are made into what we call linear weights, the more accurate equivalents of the 1, 2, 3 and 4 of slugging percentage. They change annually along with the run expectancy matrix, but currently are:

• 0.690 for unintentional walks
• 0.888 for singles (worth more than walks because of the potential to drive in unforced runners)
• 1.271 for doubles
• 1.616 for triples
• 2.101 for home runs

The numbers are put into an equation that looks similar to slugging percentage, but with the new weights, that sum is then multiplied by a constant to make results that appear on a scale that looks similar to on base percentage. The product is OBA, on base average.

The next step is to make RC+, runs created plus. Like any SABR statistic that ends in a plus sign, RC+ is weight so that league average is 100 and each 1 statistical point above 100 is 1 percentage point above the MLB average. A player with 105 RC+ has an OBA 5% above average. A player with 95 RC+ has an OBA 5% below league average.

The final step in making wRC+ has to do with the w, “weighted.” To add the w, one must take into account park and league (American vs. National) factors. After adjusting wRC for those numbers, you have the final product WRC+, which is the best offensive statistic that can be used to compare players from any team during any era.

A 100 wRC+ is, by definition, average. Offensive all-stars tend to have wRC+’s above 125. Keeping in mind that wRC+ only measures offense, MVP candidates need a wRC+ North of 140. Since 1900, Babe Ruth and Ted Williams have the highest career wRC+, at 197 and 188. Mike Trout is #7 on the list, with 169, and Joey Votto is 11th with 158.

In times long past, if you wanted to take a quick look to see how good a player was offensively, you checked their batting average, and maybe their home run total. If you considered yourself data savvy, you might use SLG. In 2018, if you want to quickly check how good a player is offensively, looking at their wRC+ is a smarter move. There is, of course, not single tell-all offensively statistic, but today, wRC+ is the closest you will get.

 

Sources:

Baseball Reference

Fangraphs

Tango Tiger

 

Images attributed to:

Getty Images

The Secret of Ray Searage

– The K Zone –

January 25th, 2017

The Secret of Ray Searage, by Ian Joffe

Some call him the pitch whisperer. Others say he’s just fun to hang out with. Either way, Pirates pitching coach Ray Searage has turned around countless pitching careers, and I am set out to figure out why. While Searage is not the biggest fan of the term, many call his various turnarounds “reclamation projects,” and while they don’t always work out (just ask Ryan Vogelsong and his 5.00 FIP), they tend to be a relatively safe bet. Pitchers who get traded to Pittsburgh warrant at a least watch list add in fantasy, or a weekly stat check on Baseball Reference. In the past six seasons, some some of the biggest reclamation projects have been Francisco Liriano (2013), reliever Mark Melancon (2013), Edinson Volquez (2014), J.A. Happ (traded at the 2015 deadline), Ivan Nova (from the 2016 deadline) and A.J. Burnett (twice, in 2012 and 2015). Those are the seven cases which I will use in my study.

My first attempt at the cracking the Searage code was to look through interviews. I had no luck – turns out that Ray guards his secrets pretty closely. Jared Hughes even joked about his organization’s secrecy with Sports Illustrated when they tried to talk to him, from “Uncle Ray” to his high-beets diet. In a 2013 interview with MLB.com, Liriano was asked why he was having so much success in his new ballpark. He starts “I don’t know, I don’t know what to say.” He continues to say that he feels under control and needs to not make mistakes. I never would have guessed. On the topic of Searage, Nova vaguely states to nj.com that “he’s a good pitching coach. He’s been a good pitching coach forever. He has a lot of guys who have pitched great games for him.” Nova goes on to suggest that perhaps people are just very comfortable with him, leading them pitch better. J.A. Happ and Jason Grilli add to this, telling USA Today that it’s all about trust, and that people trust Searage because of his enthusiasm and the level of research he puts into every game. Nova also tries to take a little credit off of Searage’s back. “It’s not all the time about the pitching coach.” Melancon also claims that catcher Russel Martin had the biggest influence on him. After all, both Martin and Melancon were once on a team with Mariano Rivera, to whom Melancon credits his cutter. The players are not lying; there is more that goes into a pitcher’s game than just advice from the pitching coach (from beets to former assistant pitching coach Jim Benedcit’s advice), but the wide array of reporting on Searage and his high count of success stories no matter who else is on the team to co-inform at the moment don’t lie either. There is something going on in Ray’s mind and the Pirates organization, so as I so often do, I turned to Brooks Baseball to find out.

For those who are unfamiliar with Brooks, brooksbaseball.net has the most comprehensive pitch tracking data on the internet. It was difficult to find any pattern among my pitcher sample, but eventually I reached something. In 6 out of the 7 cases, after being acquired by Pittsburgh, the pitchers threw far fewer classic four-seam fastballs than before, replacing them with moving fastballs such as sinkers and cutters. In some cases, like Liriano’s the pitcher eliminated the four-seamer from their arsenal altogether. The one exception to this rule is J.A. Happ, who actually threw more fastballs than ever after joining the Pirates. However, according to Fangraphs, Happ’s regular fastball actually moves a lot, the 11th most in baseball and just as much as any of Happ’s other pitches. So, one could say that Happ’s straight fastball is more like another pitcher’s moving fastball.

For as long as baseball has been played, the four-seam fastball has the been the most important pitch. It’s the first pitch kids are taught (which, I understand, is for health reasons, not necessarily for skill-building reasons) and the first pitch scouts will look for. A pitcher that throws more of another pitch than a fastball is a rare occurrence, especially for starters. But baseball is in an era of change. Perhaps Ray Searage is trying to pioneer a change of his own, which, when you really think about it, makes perfect sense. According to The Hardball Times, the straight fastball allows for the most flyballs (32%), the most line drives (19%), and the fewest grounders (39%) of any pitch type. Moving fastballs, on the other hand, lead to the least flyballs (19%), the least line drives (17%), and the most grounders (59%). An emphasis on keeping the ball on the ground is especially important, and its merits are more proven than ever, in this Statcast era, where hitters are focusing on launch angle and fly balls (or, as Daniel Murphy likes to put it, “high line drives”) more than ever.

Four-seam fastballs are important for command and speed differentiation. But, moving fastballs are only a few miles per hour slower, and exhibit much better results. Perhaps it’s time to take advice from Searage and start to question the value of the fastball as the primary MLB pitch. It could be far more advantageous as a secondary option, to blow a hitter away once they’re used to timing breaking pitches, or waiting for the cutter, sinker, or even splitter to move last-second. I’m not the very first to suggest this; Sports Illustrated first pointed out that the Yankees took a similar approach to a decrease in fastball use this year (although hopefully I’m near the front of the S-curve). They quote Yankees pitching coach Larry Rothschild, who is far more open than the Pirates have been. “Fastballs get hit,” he says bluntly, especially if they are not commanded well. The addition of the Pirates to this anti-fastball revolution (shall we call it #ForgetTheFastball?), however, certainly adds credibility to the idea that this could work for many types of pitchers on many different teams. Searage definitely does more than make pitchers throw fewer fastballs; what I have discovered is probably less than 10% of his plan for each pitcher. He works on mechanics, makes each pitch better, and has different game plans every day. This is especially notable in the case of Edinson Volquez, who did throw fewer fastballs after joining Pittsburgh, but saw his biggest drop in four-seam use the year before his signing. But, in this post-Moneyball decade, with teams looking to question everything to gain any slight advantage, Ray Searage has shown us that combating the hitter-friendly fly ball trend through a decline in straight fastball use could be a secret to pitching success.

Images attributed to The Associated Press and the Pittsburgh Post-Gazette

Stars and Scrubs Forever

– The K Zone –

January 13th, 2018

Stars and Scrubs Forever, by Ian Joffe

 

Every offseason, each team’s GM and front office has a choice to make: should we stock up on depth, or go sign the big fish on the free agent market? Recently, as Travis Sawchik of Fangraphs pointed out, teams have been trending towards the depth route, but when it comes to free agent hitters, teams are far better off allocating their money towards just a few stars. Here’s why:

 

I. Depth-based teams perform no better than Stars and Scrubs teams

Back in 2014, Jonah Keri and Neil Paine from FiveThirtyEight did some research (they, in turn, cite Fangraphs) to show that the way a roster is constructed has little effect on how it performs. Here is the chart they produced based on the data they found:

On their chart’s x-axis, the data shows how balanced a team is, while on the y-axis, the chart displays how well the team performed. While the article makes sure to note that at the highest extremes, depth works, there is not an overall trend to be found. The teams who had the most total contributions from the sum of their players did the best, whether that came concentrated on a few superstars or it came from every individual. And, when one thinks about it, it makes sense that neither strategy would be perfect. Banking on a few players seems to come with risks of health, but at the same time if they can stay healthy, those stronger players may be more consistent. Jonah and Neil also make an interesting point with regards to the trade deadline and further roster building after its base: It’s far easier and cheaper to replace a scrub at second base or left field with an average player than to replace an average player with a star.

So, to be clear, there is little correlation between how a team spreads out their roster and how well they do in a season. Both have advantages, and both have disadvantages, which turn out to be pretty equal, as shown by the data. The battle then becomes about value, which I wrote a little about with regard to the current free agent class. Between two teams that get equal contributions from the sum of their players, which roster construction type is cheaper? With the exception of an especially greedy owner, the team who chooses the more cost-efficient makeup should be able to afford an extra player for the same price, pushing them just over their competitor.

 

II. Stars and Scrubs is a more cost-efficient method of roster construction than Depth

To find this information, I built a Python program that looks at tabular data from Fangraphs and MLB Trade Rumors. Along the x-axis of my program’s graph (below) is the WAR of various position players in their contract years, and along the y-axis is the average annual value of the contract they proceeded to sign. Using a polynomial regression model, I made a curve of best fit (in red), which should show about how much it would cost annually to sign a player of each WAR value. 

The basic red curve takes on the form of an inverse cube function, steep in the middle stretching out lengthwise on either end. That means it costs more money to tack on an extra share of a win to an average player than to a great or a poor player. That concept is best illustrated by the blue graph (the red line’s derivative), which peaks at a 2.51 win player, just above average (2.0), meaning each extra part of a win you want to add is most expensive for players with a WAR between 2 and 3.

The green money line, however, is the most important, and you don’t need calculus to understand it. Let’s zoom in a little.

On the x-axis is the total WAR that a free agent accumulated last season, and on the y-axis is the amount of money that each of those wins costs (contract AAV divided by the WAR contribution). The math says that as a player’s WAR approaches zero, their price approaches infinity, but we’ll assume that a team can get a replacement level player for the MLB minimum wage, around $500,000. The lesson there is simply that buying a player with a WAR under 1.0 is a bad idea (but does buying a player with a negative WAR earn you money per win?). A 1.0-WAR player starts out as a rip-off per win, but the value quickly rises. A 1.6 WAR player represents the local minimum in cost per win, at only $4.18MM. The price of a win then starts to rise again for the average and above average athletes, hitting a local maximum of $4.35MM per win for a 3.3 WAR player. But then, as foreshadowed by the plateauing of the red curve and decrease in the blue curve, the green curve begins to drop. By the time it hits a 5.5 WAR player, a win only costs $3.66MM, which is as far as the data will take the line without overfitting the smaller sample up top.

The local minimum at 1.6 WAR is important for a team that only has money for maybe one very minor investment (namely, do not invest in a below-great player worth much more than 1.6 WAR, or much below because teams can always promote or claim 0.0 WAR players for minimum wage), but the ever-decreasing price tag per win of the best players is the most important part. To be a top hitting team in 2017, the nine players in your lineup needed to total around 27 WAR for the season – on average 3 WAR per player. To build this kind of roster of pure depth, that is every player is equal, each player would command an average annual value of $12.9 million, for a total cost of $116.1MM. However, a team who builds their 27 WAR with 5 5.5 WAR hitters and 4 replacement level hitters will only spend $102.5MM. If they want to spend the same amount of money as the first team, they could add an extra 3.25 WAR bat, making their team superior (that’s the difference between the Cardinals’ and Mets’ offense, or the Diamondbacks’ and Braves’ offense) to their depth-based counterpart.

If you exclude the ability to add replacement level players for minimum, a big advantage for more extreme stars and scrubs teams in keeping payroll down, here are the total payrolls of various 27-WAR roster constructions, with the deeper ones at the top and the shallower ones at the bottom:

Lineup Makeup	Payroll
9x 3 WAR	$116.1MM
4x 3.5 WAR, 4x 2.5 WAR, 1x 3 WAR	$117.7MM
4x 4 WAR, 4x 2 WAR, 1x 3 WAR	$116.5MM
4x 4.5 WAR, 4x 1.5 WAR, 1x 3 WAR	$103.7MM
4x 5 WAR, 4x 1 WAR, 1x 3 WAR	$103.3MM
4x 5.5 WAR, 4x 0.5 WAR, 1x 3 WAR	$105.3MM

 

There’s a sudden drop-off in payroll once a team gets below a certain amount of depth which coincides with both the part of the green graph at the end that becomes a really steep downhill and the part of the small valley in the beginning of the curve. If it didn’t already seem clear, this should answer up any questions. A Stars and Scrubs roster provides much more value for a team than a depth-based one, allowing them additional payroll space to add better players. The FiveThirtyEight data from Part I showed that roster makeup does not affect team record, and that team talent was decided purely based on how good the sum of the players are. By saving money through a Stars and Scrubs construction, a team can add more good players, therefore adding to that sum, and becoming the better team.

 

III. Conclusion

The collected data shows a lot, but it’s far from perfect. For starters, I only focus on WAR, which is a terrific statistic, but is in no way completely tell-all (I’ve written about the topic in the past). Additionally, I only look at Fangraphs’ fWAR, which is only 1/3 of the WAR story. Furthermore, the method assumes that free agents will replicate their previous season during the years of their contract, ignoring aging curves, or at least that teams assume they will. Anyone who follows baseball at all knows this is far from the truth. Teams know free agents are incredibly risky commodities, and the suggestion that a team would consider building a roster entirely out of free agents is kind of ridiculous. This is especially true for superstar free agents, who will require a longer commitment than average ones. The best method of player acquisition for value and talent has been, is, and will probably always be player development. That said, a made-up model of teams acquiring only free agents works well to represent a more realistic model, when a team might have to decide if it wants to allocate a small part of the budget to a few, or only one hitter. Finally, the study only looks at hitters. An analysis of pitchers would need a whole new article.

At first, the suggestion that the best teams should be superstar-driven is a little depressing. It’s fun to watch stars play, but part of the beauty of the game is that everyone is the lineup has the same chance to make a contribution. But one could also look at the findings in a much more positive light. Rebuilding teams don’t need every single prospect around the diamond to work out. Having just a few players break out in superstar fashion (e.g. the 2017 Yankees, who continue to add more superstar power) can make a team instantly competitive. Signing just one or two big free agents (teams are shying away, but J.D. Martinez plus Eric Hosmer could turn any franchise around if they continue to grind after signing) can turn a mediocre roster into a World Series contender. It’s all very good for the parity of the game. The power of just one or two stars can light up a whole team.

 

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Image Attributed to USA Today Sports

Playing Overrated/Underrated with the Top Winter Targets

The K Zone

January 3rd, 2017

Playing Overrated/Underrated with the Top Winter Targets, by Ian Joffe

Evaluating talent is a cornerstone of baseball analytics, but often, finding the most talented players is a secondary goal. What really matters is finding the most valuable players. It doesn’t take a genius to figure out who the best players in baseball are – Mike Trout, Clayton Kershaw, Joey Votto, etc. – but it’s incredibly unrealistic for a team to be able to sign or trade for all these players. With limited payroll space and trading power, teams need to figure out who they get for a bargain, and who they should avoid overpaying for. Stacking a team with underrated players is much more realistic than stacking the most talented players, so, here is a list of which of the more polarizing and incorrectly valued winter targets are overrated (low value), and underrated (high value).

J.D. Martinez: Underrated
Potential Suitors: Red Sox, Diamondbacks, Giants, Blue Jays
Since 2014, only four players have a higher wRC+ than J.D. Martinez: Mike Trout, Joey Votto, Giancarlo Stanton, and Bryce Harper. Martinez has been better offensively than Paul Goldshmidt, Kris Bryant, Josh Donaldson, and Jose Altuve during that period. While often ranked around the top 30 baseball players, J.D. makes a good case to be in the conversation for top 10. An early season injury barely disqualified him from the statistical leaderboards this season, but had he made it on, J.D. would have ranked third in offense, his only problem being defense, which, to be fair, has been horrific. But even with some of the worst defense in baseball, he is a top talent and any AL team like Boston who would plan to put him at DH should be willing to put up 150 million without thinking about it.

Eric Hosmer: Overrated
Potential Suitors: Padres, Royals
With Boston resigning Moreland and St. Louis picking up Ozuna, Hosmer has seen his market evaporate, and for good reason. Since his first full season in 2011, the Royals’ first baseman has put up a fWAR lower than 1.0 for 4 out of 7 seasons, and has put up a negative fWAR (that’s right, below replacement level), in two full seasons, most recently in 2016. In his best year so far, 2017, he was barely top 25 in offense alone, and that’s with the help of a .351 BABIP. Top 25 is not bad, but keep in mind that J.D. Martinez has been the #4 offensive player since he got good. Furthermore, Eric’s ceiling is limited to about what he did last year by his 53% career ground ball rate (55% last season). I’m not saying that Hosmer is a bad baseball player, but I am saying that he should not be considered by any team while Martinez is still on the market. When he is signed, I advise it be by an AL team so that he doesn’t have to wreck the defense on a daily basis.

Carlos Santana: Underrated
Signed with the Phillies for 3yrs/$60MM
Carlos Santana is the proud owner of 23.0 career WAR. He has about 10% more career at-bats than Hosmer, but Hosmer, who is expected to get at least double Santana’s $60 million, only has a mere 9.9 career WAR. Age is certainly a factor in that salary differential, but it should be nowhere near that large, especially considering the fact that Santana has shown no sign of aging. Carlos is an on-base machine, with a career 15.2% walk rate (13% last season), which raises his floor, and is probably the reason that he is so underrated (career batting average .249, career OBP .365). That .365 mark is 28th in baseball since 2011, and goes along with consistently strong power that maxed out at 34 home runs in 2016. Santana is incredibly durable and consistent (his on-base has hovered between .351 and .377 every year since 2011, and he has played 152 games 6 out of those 7 years, the exception being a 143-game 2012), and his first base defense has improved greatly since he starting playing there permanently (10 DRS last season). The $20MM AAV he got from the Phillies is probably only a little under value and about right, but the Phils are lucky to have him on such a short commitment.

Billy Hamilton: Overrated
Rumored Suitor: Giants
A few weeks ago it seemed like Billy Hamilton to the Giants was hours away from occurring. San Francisco is lucky that it didn’t. Hamilton is, and probably always will be (there are no signs that a change is possible), a weak hitter. His career walk rate is under 7%, and has one of the lowest average exit velocities in baseball. Dee Gordon can get away with those shortcomings (at least to some extent) because at least he can hit singles, but I don’t see any base hits coming from Hamilton either. Speed is nice, but it’s only really useful to players who can get on base consistently. Hamilton’s defense is great too, but it’s not enough to even make him an average player (2017 WAR 1.2). If I were the Giants, or any team, I would stay away.

Jose Abreu: Underrated
Rumored Suitor: Red Sox
Maybe it’s just that he’s on a rebuilding team, but most people I talk to seem to think Abreu is more of a league average first baseman than an All-Star. But, since joining the White Sox as an international rookie in 2014, Abreu has been the model of consistency. After knocking 25 points of BABIP off his 2014 numbers to be in line with the rest of his career, Jose’s OBP through the years has gone .358, .347, .353, .354. His home runs have been incredibly steady as well, progressing at 36, 30, 25, 33. Looking at WAR, until last year, there was some thought that Abreu’s career was going downhill each season, but improvements in 2017 (4.1 WAR) seemed to have quelled that fear. Abreu is the rock that any streaky team, Boston included , should be looking for.

Lance Lynn: Overrated
Potential Suitors: Cubs, Twins, Orioles, Rangers
Lynn had once shown promise of being at least a solid MLB #2 starter, but now at 30 years of age, that hope seems to have faded. Since his best full season in 2012, Lynn has always had a high walk rate, but as of 2017 his strikeouts are decreasing too. Lynn’s fastball has lost speed three years in a row, and the low home run rate that once gave him such promise blew up to 1.30 in 2017. The year ended for Lance with a 4.82 FIP, 4.75 xFIP, and 4.85 SIERA (the 3.43 ERA is deceiving). Don’t count on Lynn being a mid-rotation starter for a contender in 2017.

Chris Archer: Underrated
Potential Suitors: Twins, Brewers, Yankees, Phillies
The strikeouts keep on coming for the now 29-year-old Chris Archer, who put up the 5th-best K-Rate in baseball last season from the AL. His walk and home run rates are not spectacular, but they work fine to supplement the strikeouts, and helped lead him to a 3.35 xFIP last year. 19 losses for a terrible 2016 Rays team took Archer off the map for many box-score-only baseball fans, but his 3.41 xFIP in 2017 tells the truer tale. Under one of, if not the most team friendly contract in baseball, Chris is worth the prospect price for any team that wants to contend for a while.

Michael Fulmer: Overrated
Potential Suitors: Yankees, Brewers, Cubs
Michael Fulmer should be labeled among the most overrated young pitchers in MLB. A late-season surge in 2016 netted Fulmer the Rookie of the Year Award, but in his short career, he has averaged only 6.84 K/9, near the bottom of baseball in this age of power pitching. A low BABIP has also helped the Tiger sustain a low ERA, even thought in both 2016 and 2017 he registered a hard hit rate over 30%. This does goes along with a lot of ground balls, which would help the sinkerballer create something of a floor, except for the fact that he’s now fallen victim to elbow problems. If any team thinks he will continue to be an ace or even a #2, they’re kidding themselves.

Tyler Chatwood
Signed With the Cubs for 3yrs/$39MM
I wrote about Chatwood in my recent study of the Rockies overall, which you’ll have to read, but I’ll tell you one thing: for the Cubs, this was either a really great bargain, or it’ll be a rough and rocky three years ahead.

 

Sources:
Fangraphs
Baseball Savant
Brooks Baseball
MLB Trade Rumors

The Correlation Conundrum

The K Zone

December 21st, 2017

The Correlation Conundrum, by Ian Joffe

If there is one existential truth about baseball players, it’s that they’re wildly inconsistent. Sometimes players will be league average or below, and then suddenly break out, for one year or for the rest of their career. Other times a strong player will have an off year or two before bouncing back (hopefully). These patterns in season-to-season output can be the result of a myriad of factors, including mechanical changes, aging curves, psychology, and luck, each of which teams employ small armies to study. While each player will be affected by each factor differently, the average player, over a large enough sample size, should experience similar trends in year-to-year production. My goal here is to try and measure those trends, and figure out the likelihood of a contextless player having a breakout, bounceback, or fallout season.

In this article, you’re going to see a ton of scatter plots. For a multitude of statistics, I will compare players’ output in a forth year with their stats from each of the previous three, and from averages of the previous three. I did this by writing a Python program to sift through Fangraphs leaderboards for players that qualified four years in a row, and then comparing their numbers. While this requirement gives my group a significant bias in talent level (average WAR 3.7), the year to year difference in talent with these players should be similar to that of the average player, especially considering that my data goes back to 2010 and covers a large sample of 200 hitters and 113 pitchers.

One tool I will use a lot is Pearson’s Correlation Coefficient. You can see the wikipedia page for Pearson’s to get a grasp of the formula and mathematical intricacies, but basically it measure how similar one set of data is to another. In this study’s context, it will dictate how much players’ stats from one year match up with their stats from another. Each Pearson’s “P-Value” is on a scale from zero to one, lower decimals representing a smaller correlation, and higher ones representing a larger correlation. Two identical data sets will have a P-Value of one, and two truly random data sets will have a P-Value of zero. Another way to look at Pearson’s is as a numeric representation of each graph (or one could look at the graph as a visual for Pearson’s). A more stable scatter plot, where the points make a straight line, will have a higher P-Value, and a more random plot, with points scattered all over the place, will have a lower P-Value. So, without further ado, here is the data I collected:

For Batters:

And for the Pitchers:

So, let’s start by examining the overall trends. As predicted, between the three individual years, numbers from one year ago can best predict the output for this year. But, in almost all cases, stats from two or three years prior actually have about equal value. In the few exceptions, like batter WAR, the previous two years had similar correlations, with a significant drop-off after that. But, with every statistic, the averages were the most predictive of coming season’s outcome. While the three-year averages were better than the two-year averages, they were also consistently only very slightly better. In fact, I’d say that for the everyday fan, taking a two-year average is your best bet with minimal effort. The ideal player predictor, then, would probably be a two year average with a slight weight on the most recent season, but when I tried to use machine learning to determine the weight, I got some really weird results. For example, on the three-year-average, it always gave the most recent seasons the same weights, and the stats from three years ago the biggest weights. Perhaps the machines are already smarter than us, and my computer is working in ways I cannot understand. But, a computer uprising notwithstanding, it’s a safe bet to say that my machine learning algorithm was a failure.  So, I can’t provide you with exact weights, but as a general rule, my research shows that a two-year average is best for predicting whether or not a player will succeed in a coming season.

A few statistics had more irregular results than others. WAR, for example, had similar correlations between one year ago and two years ago. I would probably attribute this to the fact that it is composed of so many different statistics. If each of those stats has its own pattern of positive and negative regression, at least in part independent of the other components of WAR, then it would stand to reason that WAR as a whole is more variable year-to-year than each of those individual components. Therefore, last year’s WAR makes a worse predictor of the coming year, and a more similar predictor to two years ago, at least in comparison to any of those components. Fangraphs’ defense metric also had a somewhat strange outcome, where each year was a similar predictor, instead of last year being the best predictor, but that makes sense for defense in particular, because it is more ability-driven than hitting or pitching, which are more luck-driven. Earned Run Average also experienced strange correlation patterns. This could have a similar explanation to WAR, a run being composed of many different factors, but all in all I would attribute this strange pattern, along with overall low correlations, to ERA being a garbage statistic, even in my large sample size. However, among all of these different exceptions, it still held true that a two-year average is the best tool to use, by a meaningful margin over any single year. One stat that surprised me was HR/9 for pitchers. Sabermatricians have long heralded homers as one of the “three true outcomes,” but it showed an both overall low correlation and a weird pattern, with the one-year and three-year P-Values being equal, and the two-year value being the lowest. Additionally, the averages only improved this P-Value by a little. Unlike the other exceptions, I can’t easily explain away why this is happening. Maybe statisticians should consider relying less on home runs given up by pitchers to evaluate an arm. HR/9 turned out to be a notable exception, but for predicting most any other stat – power, contact, speed, control, or command – to gain an advantage, take a two-year average.

Sources:

Fangraphs

Image Attributed To:

Wikipedia

The Road Rockies

The K Zone

December 7th, 2017

by Ian Joffe

I often find myself pondering two what-if scenarios in regard to the Colorado Rockies. My questions rise out of the well-documented fact that the Rockies play half their games a mile in the sky, at their home in Coors Field. The thin air allows baseballs to be driven harder and with more consistency than any other ballpark. Considering that my first fantasy, Giancarlo Stanton being traded to Colorado, was killed earlier this offseason, in this article I will turn to the question of what kind of team the Rockies would be in a normal ballpark. Expectedly, in 2017, the Rockies fell in the middle pack in terms of runs allowed, but scored the third most runs in baseball, and the most in the National League. This combination led them to the 8^th best record in MLB, and a wild card berth.

Looking strictly from a team-wide perspective, “the Coors Field Effect” appeared to have a positive impact on the Rockies. The team went just over .500 on the road (41-40), ranking 25^th in total runs scored. Colorado’s pitching rose up to 9^th in baseball, but this was not enough to remedy the weakened hitting. Under these changes, the road Rockies compare extremely well to the Tampa Bay Rays, who went 80-82 over the regular season – not a bad team, but certainly not a playoff roster either. Based on this information, it’s easy to claim that the Rockies are just lucky to make Coors Field home, but I would instead give credit to their front office. They figured out how to build a good team based on the conditions they were given, which is not an easy task.

Just as interesting as the performance of the overall team, however, is the performance of individual Rockies players on the road. Looking at the hitters, the first one who stands out is Charlie Blackmon. Most recently, Blackmon was subject to controversy over MVP balloting. Some argued that park should be counted against him, while others said wanted to let his numbers be. Those who argued that his stats were inflated by park certainly had a point. 12 of Blackmon’s 13 triples were at home, a huge driver of his .601 total slugging percentage, which fell under .450 on the road. Blackmon still put up respectable numbers, but not MVP level. His 102 wRC+ suggests he is more of a league average hitter, and his defense has never been spectacular. Only one Rocky (or is it Rockie?) ended up being an above average offensive player on the road: Nolan Arenado. The third baseman’s wRC+ only fell by 4 points when away, a trend he has exhibited throughout his career.

Five Rockies starting pitchers threw at least 50 innings on the road. Strangely, Kyle Freeland got worse, while German Marquez improved only a little. Antonio Senzatela experienced significant improvement on the road, but remained a league average pitcher. Jon Gray is the first of the important Rockies pitchers on the road. His 10 K/9 would put him in the upper tier of starting pitching, as would his low walk and home run rates. These total up to a 3.05 road FIP, certainly an enviable mark. Even more impressive, however, are Tyler Chatwood’s numbers. Disappointingly, Chatwood finished 2017 with only mediocre road numbers, but career, he owns a 3.31 road ERA with a 0.71 HR/9. In 2016, Chatwood boasted a mere 1.69 road ERA, and in 2013 he put up a 2.72 mark (Tyler missed most of 2014 and 2015 with injuries). Both home and away, Chatwood is an extreme ground baller, approaching a 60% rate, which, combined with a 24% soft hit rate and 26% hard hit rate on the road, is likely responsible for the elite road years. It is very worthy of note that none of Chatwood’s more sabermetric numbers stand out on the road, but it would still be interesting to see how he would fare in a whole season. And, the best part is, soon, we will be able to see. Chatwood was a free agent this offseason, and just last week he signed with the Cubs, so the dream of seeing a full road season out of Tyler Chatwood is will, at long last, become a reality.

 

Sources:

Fangraphs

MLB.com

Images Attributed to:

ESPN

stadiumparkingguides.come

A Look at Luck: Hitter Edition

-The K Zone-

July 6th, 2016

A Look At Luck: Hitter Edition by Ian Joffe

Today is June 6th. It’s an arbitrary day – I chose it because today I happened to be bored – but this time always proves to be fascinating for player evaluation. With a couple of months and change under most players’ belts, we both have a large enough sample size to start judging which breakout stars are for real, but also have a small enough sample size that a ton of luck is still involved.

As in all statistics, a larger sample size means more accuracy. In baseball, that sample size generally applies to plate appearances (at-bats excludes walks and such) and innings pitched. The inverse of this is that a smaller sample size creates more randomness, or luck. In baseball, luck can refer to a multitude of factors, from ballpark dimensions to weather, but the most important factor to luck is opposing defense. Defensive luck is a critical component to early and current sabermetrics, as statistical studies by early SABR experts have suggested that the outcome of all balls in play are almost entirely up to the defense, rather than the hitter. This is not to suggest that balls in play and entirely random, but rather that most are, and the very hard hit balls tend to even out with the very soft hit balls. Based on this, we can conclude that most hitters should have a similar BABIP, or Batting Average on Balls in Play, because the defenses they face over a large sample will be similar to one another. That BABIP is expected to be around .300. There are certainly exceptions, including incredibly speedy hitters and power hitters, but even the exceptions shouldn’t have a BABIP over .330, and even Hall of Fame level exceptions will not achieve a BABIP over .350. Thus, if a player has a BABIP far over the 300-330 range, they have experienced high defensive luck, and we can expect that luck to even out over a larger sample size, as the luck of a coin flip would, causing their numbers to regress. Similarly, a batter who has encountered great defense may have a BABIP far below the 270-300 range, meaning their stats will likely improve, or experience positive regression (I know, it’s an oxymoron, but that’s the proper term). In this article, I will examine the highest and lowest BABIPs in the league, and attempt to determine how sustainable that makes their current statistics. Whose hot starts can you trust, and whose will wash away?

Hitting BABIP Leaders:

1. Miguel Sano (.465)
2. Ryan Zimmerman (.411)
3. Aaron Judge (.408)
4. Avisail Garcia (.396)
5. Jean Segura (.395)
6. Zack Cozart (.393)
7. Xander Bogaerts (.387)
8. Corey Dickerson (.387)
9. Keon Broxton (.385)
10. Matt Kemp (.379)

Miguel Sano has amazing peripherals so far. His soft hit rate is 7%, with a hard hit rate over 50% and league leading 96.6 average exit velocity to accompany it. It is those peripherals that make his BABIP somewhat believable for the time being, but they are also what makes his numbers unsustainable. He won’t finish the season with those kinds of batted ball metrics, and considering he is currently hitting barely over .300, his batting average could turn out dismal. But, if he continues his three-outcomes approach, I would not be surprised if his massive power continues, along with big walks and really big strikeouts.

Ryan Zimmerman is perhaps the most exciting player on this list. His 2016 return from/return to injury was disappointing on the surface, but deeper Statcast analysis told a more complex story. Zimmerman was killing the baseball, but hitting it on the ground almost 50% of the time, with one of the worst average launch angles in baseball. This year, however, he has embraced the new, league wide ball-elevation mentality, and has had terrific results as the potential league MVP so far. Thanks to his high current BABIP, Zimmerman will not maintain his current, ridiculous 1.111 OPS, but should maintain good numbers throughout the season thanks to his new alchemy of velocity and launch angle. I would expect to see a batting average around .300 with good power.

Avisail Garcia is the most unfortunately standout of the group. After falling from top prospect status years ago, he has disappointed scouts and fans alike. Yet, this 2017, he went off to a fiery start, all on the heels of a nearly .500 BABIP. And, he did all of that despite hitting 50% ground balls with normal exit velocities. In May, the BABIP slowly began to regress, and so has the batting average along with it. The two will continue to decrease together until Garcia reaches a more natural .300 BABIP, which should, for him, match a low average (I’m talking .220 or so) with few walks.

Aaron Judge is one of the greatest success stories of the year, getting off to an incredibly hot start after largely failing in last year’s small sample. He hit nearly .400 in April and continues to lead the league in home runs, but his batting average has since fallen to the low .300s. That batting average will continue to fall, even if his good batted ball numbers allow him to maintain a somewhat higher BABIP. He may hit .250 at the end of the year, but with good on-base instincts and continued power (maybe not to the extent he has hit so far, but he could go deep 45 times total), expect Judge to keep high value and be a Rookie of the Year favorite.

Jean Segura had similar BABIP issues last year, when he hit well over .300 in April, but he somehow managed to maintain a strong, .319 batting average throughout the season. Segura has the speed to maintain an above average BABIP, but Usain Bolt wouldn’t get close to .400 over a large sample, and neither will Segura. I would estimate his current .341 batting average at the end of the year to drop significantly below .290, unless, like last year, he can manage to defy the odds. If he does continue to hit, it would throw an interesting punch in the face of DIPS theory.

Zack Cozart might be a pleasant surprise in terms of walk rate, but like Garcia, his batting average seems doomed. He has shown us nothing to prove that his BABIP can even remain above average, telling me it should fall about 100 points, bringing his batting average to the mid-200s. That would make sense, considering it would match his career mean.

Xander Bogaerts‘ high batting average may have canceled out some Boston fans’ fears over the lack of power, but I would worry about both hitting tools. Xander has not hit the ball hard, in fact he is hitting it particularly soft, and has only good speed. Like last year, he could go on a massive cold spell at any moment, and his batting average may drop below .280, dare I say .270.

Corey Dickerson seemed like half of a terrible trade for both teams, when the Rockies dealt him to Tampa Bay for Jake McGee a couple years back. Critics blamed park factors for his miserable 2015, when his OBP dipped below .300, but he seems to have adjusted at this point. Much of his .330 batting average is BABIP fueled, but he has shown us the ability to hit for a moderately high BABIP and average before, and may end the year hitting a very productive .280 or so.

Keon Broxton may need a demotion after the BABIP regresses, making his story one of the more disappointing ones on this list. After a hot start last year (although that may have been BABIP-fueled as well). He’s hitting only .240 despite the luck, and may be batting somewhere in the .100’s without the inflated BABIP. Additionally, he has struck out out 40% of the time. Broxton should not be expected to hit for power either. He will steal his 30 bases, but he’s not fast enough to sustain such a high BABIP. If I were a fantasy owner, I would try to trade him while I still could.

Matt Kemp, the former MVP candidate, was traded to San Diego and later Atlanta after falling from stardom through numerous injuries, but seems to be putting up respectable production once again. His average could fall to .275 or so, but like Dickerson, Kemp has shown the ability to put up an above average BABIP before. Kemp’s days of speed and defense are over, but he could still be a middle-of-the-order bat for the rebuilding Braves, with good power.

Along with looking at BABIP to see who will regress (and who may keep a reasonably high average), one could also use the stat to encourage people to not give up on certain players who have had really bad luck. Rizzo (.216) and Machado (.229) immediately come to mind. Don’t worry Cubs and Orioles fans, they will bounce back to All-Star production when their BABIP improves. The young Kyle Schwarber and Dansby Swanson also have BABIPs under .230. Matt Carpenter will get better, as his BABIP is .238. I also want to say that fantasy owners and White Sox fans shouldn’t worry about the ToddFather — his BABIP is under .200 — but the fact that he managed to keep a .200 BABIP all last year, and continues to do so this year, makes him appear to be a candidate to be a reverse Segura-type. Only time will tell. Padres hitter Ryan Schimpf, who has the worst BABIP in the league, is little-known among everyday fans, but to communities that embrace the three true outcomes approach and launch angle strategy (Schmipf proudly owns a 64.6% launch angle), he may be the face of the movements. His batting average is in the mid-.100s despite 14 home runs so far. He has far too many strikeouts and fly balls for the batting average to improve very much, but it should rise at least above .210 by the end of the season.

If you liked this, you may want to check out my look at the WAR statistic, or you can look at any of Mike’s player interviews. Follows on  Twitter and Instagram are greatly appreciated, and you’ll be the first to know when new content comes out!

Top 20 Baseball Players

K Zone Master Ranking	Mike’s Ranking	Ian’s Ranking	Mojo’s Ranking
1) CF Mike Trout (LAA)	CF Mike Trout (LAA)	SP Clayton Kershaw (LAD)	SP Clayton Kershaw (LAD)
2) SP Clayton Kershaw (LAD)	3B Kris Bryant (CHC)	CF Mike Trout (LAA)	CF Mike Trout (LAA)
3) 3B Kris Bryant (CHC)	RF Mookie Betts (BOS)	RF Mookie Betts (BOS)	1B Joey Votto (CIN)
4) RF Mookie Betts (BOS)	SP Clayton Kershaw (LAD)	3B Kris Bryant (CHC)	2B Daniel Murphy (WSH)
5) 3B Nolan Arenado (COL)	3B Manny Machado (BAL)	3B Josh Donaldson (TOR)	3B Kris Bryant (CHC)
6) SS Corey Seager (LAD)	3B Nolan Arenado (COL)	3B Nolan Arenado (COL)	SS Corey Seager (LAD)
7) 3B Manny Machado (BAL)	SS Corey Seager (LAD)	2B Jose Altuve (HOU)	1B Miguel Cabrera (DET)
8) 1B Joey Votto (CIN)	2B Robinson Cano (SEA)	1B Paul Goldschmidt (ARI)	RF Mookie Betts (BOS)
9) 3B Josh Donaldson (TOR)	SS Francisco Lindor (CLE)	1B Miguel Cabrera (DET)	3B Nolan Arenado (COL)
10) 2B Jose Altuve (HOU)	SS Carlos Correa (HOU)	1B Joey Votto (CIN)	CL Kenley Jansen (LAD)
11) 2B Daniel Murphy (WSH)	SP Chris Sale (BOS)	3B Manny Machado (BAL)	3B Josh Donaldson (TOR)
12) 1B Paul Goldschmidt (ARI)	SP Justin Verlander (DET)	SS Corey Seager (LAD)	1B Paul Goldshmidt (ARI)
13) 2B Robinson Cano (SEA)	2B Daniel Murphy (WSH)	SS Trea Turner (WSH)	CF Charlie Blackmon (COL)
14) SS Trea Turner (WSH)	2B Jose Altuve (HOU)	SP Max Scherzer (WSH)	C Buster Posey (SF)
15) SS Francisco Lindor (CLE)	C Buster Posey (SF)	LF Nelson Cruz (SEA)	1B Freddie Freeman (ATL)
16) SS Carlos Correa (HOU)	SS Trea Turner (WSH)	1B Anthony Rizzo (CHC)	SP Kyle Hendricks (CHC)
17) C Buster Posey (SF)	SP Corey Kluber (CLE)	1B Freddie Freeman (ATL)	RP Andrew Miller (CLE)
18) CL Kenley Jansen (LAD)	1B Edwin Encarnacion (CLE)	2B Robinson Cano (SEA)	CL Zach Britton (BAL)
19) 1B Freddie Freeman (ATL)	1B Joey Votto (CIN)	LF Ryan Braun (MIL)	2B Jose Altuve (HOU)
20) SP Chris Sale (BOS)	3B Evan Longoria (TB)	CF Charlie Blackmon (COL)	2B Robinson Cano (SEA)

Well, here it is. The top 20 Players in all of Baseball. For explanation on why each individual is so great, look at their individual lists here (I’m not going to write it all again). Our top 10 Series may be finished, but we still come out with great new content on a weekly basis. Be the first to see it all by following us on Twitter and Instagram. For now, you can see projections for division and award winners. Enjoy the season, hopefully accompanied with our writing!