Archive for Player Analysis

Over the last couple of weeks, I’ve been looking into how a players’ stats, age, and prospect status can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there.

Things that were predictive for players in low-A and high-A included age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America in the pre-season. However, a player’s walk rate was not significant in predicting a player’s ascension to the majors. Today, I’ll look into what KATOH has to say about players in double-A leagues. For those interested, here’s the R output based on all players with at least 400 plate appearances in a season in double-A from 1995-2010. Due to varying offensive environments in different years and leagues, all players’ stats were adjusted to reflect his league’s average for that year.

Unlike in the A-ball iterations of KATOH, a player’s double-A walk rate is predictive — albeit only slightly — of whether or not he’ll make it to the show. While walk rate is statistically significant, it still matters much less than the other stats: it takes 3 or 4 percentage points on a player’s walk rate to match what 1 percentage point of strikeout rate does to a player’s MLB probability.

This version is also different in that there are a couple of significant interaction terms, signified by the last two coefficients in the above output. The “I(Age^2)” term adds a little bit of nuance into how a players’ age can predict his future success. While the “ISO:BA.Top.100.Prospect” term basically says that if you’re a top 100 prospect, hitting for power is slightly less important than it would be otherwise. Hitting for power and making Baseball America’s top 100 list both make a player much more likely to make it to the majors, but if he does both, he’s a tad less likely to make it than his power output and prospect status would suggest independently. Put another way, a few top 100 prospects hit for power in double-A, but never cracked the majors — such as Jason Stokes (.241 ISO), Nick Weglarz (.204 ISO) and Eric Duncan (.173 ISO). But virtually all of the low-power guys made it, including Elvis Andrus (.073 ISO), Luis Castillo (.076 ISO), and Carl Crawford (.078). For non-top 100 guys, many more punchless hitters topped out in double-A and triple-A.

By clicking here, you can see what KATOH spits out for all current prospects who logged at least 250 PA’s in double-A as of July 7th, as well as a few that fell short of the cutoff — most notably Joey Gallo, Kevin Plawecki, and Robert Refsnyder. Topping the list is Mookie Betts with a probability of 99.95%, and of course the prophesy was fulfilled when the Red Sox called up the 21-year-old last month. Here’s an excerpt of the top players from double-A this year:

Keep an eye out for the next installment, which will dive into what KATOH says about hitters at the triple-A level.

Statistics courtesy of FanGraphs, Baseball-Reference, and The Baseball Cube; Pre-season prospect lists courtesy of Baseball America.

Last week, I looked into how a player’s low-A stats — along with his age and prospect status at the time — can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there.

Things that were predictive for players in low-A included: age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America in the pre-season. However, a player’s walk rate was not significant in predicting a player’s ascension to the majors. Today, I’ll analyze what KATOH has to say about players in class-A-advanced leagues. Here’s the R output based on all players with at least 400 plate appearances in a season in high-A from 1995-2009:

This looks very similar to what I found for low-A players: Walk rate isn’t significant, and everything else has very similar effects on the final probability. However, the coefficients from this model are all a tad bigger than those from the low-A version, implying that high-A stats might be a bit more telling of a player’s future. Intuitively, this makes sense: The closer a player is to the big leagues, the more his stats start to reflect his future potential.

By clicking here, you can see what KATOH spits out for all current prospects who logged at least 250 PA’s in high-A as of July 7th. I also included a few notable players who fell short of the threshold, namely Joey Gallo (who checks in at a remarkable 99.8%), Peter O’Brien, and Jesse Winker. Here’s an excerpt of the top-ranking players:

Next up will be double-A. Unlike A-ball, double-A tends to be a random mishmash of prospects and minor-league lifers, so it will be interesting to see how KATOH handles this wide array of players. And perhaps double-A is where a player’s walk rate finally starts to tell us something about his future success.

Statistics courtesy of Fangraphs, Baseball-Reference, and The Baseball Cube; Pre-season prospect lists courtesy of Baseball America.

In the 2008 season the Yankees started the year with two young pitching prospects in their rotation: Phil Hughes and Ian Kennedy. These two pitchers were expected to be the future of the Yankees rotation. That didn’t really go as planned. The two pitchers struggled, and they both earned demotions as they combined for an ERA of 7.44. Hughes and Kennedy were simply not ready for major-league action. They gave up too many walks, didn’t strike out enough guys, and didn’t keep the ball in the ballpark. That’s a recipe for disaster when it comes to trying to succeed as a pitcher at the major-league level.

Nonetheless, these two pitchers showed enough promise as prospects for the Yankees to actually wait on them. In fact, after their demotions, Hughes and Kennedy spent most of their 2008 season in the minors due to mediocrity and injuries. The  Yankees were patient for a year with their young talent, however there is only so much time that goes by before you go from being a developing prospect to struggling major leaguer. The Yankees quickly gave up on Kennedy, and traded him to Arizona, where he showed decent success as a starter. In three seasons with Arizona, Kennedy compiled a WAR of 10.2.

The Yankees saw something in Phil Hughes. Hughes showed some promise in 2009 as a reliever, and then in 2010 as a starter who compiled a WAR of 2.5. However, there was the problem of Yankee Stadium not suiting Hughes’s skill set. Hughes was a fly-ball pitcher in a stadium that was known for being a hitter’s haven. Hughes always struggled as a Yankee when it came to keeping the ball in the park. The lowest home run rate that Hughes posted as a full season Yankee starter was 1.28 in 2010.

Both Kennedy and Hughes had some success over the  years; one could even argue that Kennedy was one of the best pitchers in the league in 2011. However, for the most part their careers have been a mixed bag. But times have now changed. Kennedy is now with his third team, the Padres, and Hughes is with his second team, the Twins. After mediocre 2013 seasons, the two pitchers are actually performing well.

As of right now, Hughes is fourth in the league for FIP among qualified pitchers. The only pitchers who have been better are John Lester, Adam Wainwright, and Felix Hernandez. Hughes is third in the league for WAR, right behind Lester and Hernandez. For the first half of the season Hughes has pitched like an ace.

Hughes has had the second best walk rate among qualified starters. Any walk rate below two is considered to be good, and Hughes’s rate right now is downright ridiculous. We can’t expect Hughes to be this good at not walking people, however the ZiPs/Steamer projections have him finishing the year with a walk rate between 1.31-1.38. That’s a pretty good projection, considering Hughes has never had a walk rate lower than 2.16. Hughes has also improved his home run problem, as he isn’t letting an egregious number of baseballs leave the park. The main change in Hughes approach has been his implementation of the cutter. Between 2012 and 2013, Hughes had dropped his cutter. This year, he reintroduced the pitch — throwing it 23% of the time — and dropped his usage of a slider. The change has proven to be useful for Hughes, and he no longer needs to rely on his fastball.

Then there is Kennedy. Kennedy has turned himself into the ace of the Padres staff this year. The main difference in Kennedy is that he has actually gained velocity on his pitches. Throughout his career he has always been a soft tosser. For most of his career, Kennedy averaged 89-90 MPH on his fastball. In 2013 he was up to 90 MPH. This year he is averaging 92 MPH.

Not only does Kennedy’s fastball have more velocity, but he’s also throwing the pitch more than he ever has since 2009. He has thrown his fastball 48% of the time this year. The last time he threw it more than 40% was 2010.

While it may be good to have more velocity, it also could be a little bit of concern when it comes to Kennedy because his secondary offering don’t appear to be very good. In fact, all of his pitches have negative wRAA values except for his fastball, which has a wRAA of 12.8. Most of Kennedy’s strikeouts have come off of his fastball. Having a good fastball is nice, but when Kennedy gets older — and his velocity starts to decline — he’s going to have a hard time being successful if he doesn’t have good secondary offerings.

Overall, the changes for these pitchers seemed to have worked. They’re succeeding in their own environments. While the Yankees never were able to see their prized prospects come into fruition, these two pitchers have found success away from New York. Learning to pitch at the major-league level is a learning curve. Some pitchers dominate right away. Other pitchers struggle for their first couple of years, and then things somehow start to click for them. I’m not suggesting that Kennedy and Hughes have figured out pitching, nor are they the best pitchers in the majors. However, they have proved that they are  at least very average starters, or maybe even above average major-league pitchers. Only time will tell.

On June 3rd Marlins pitcher Henderson Alvarez threw an 88-pitch shutout against the Rays scattering eight hits while not issuing a walk. On July 11th Marlins pitcher Henderson Alvarez also gave up eight hits while not issuing a walk but only made it five innings after surrendering 6 runs. While the circumstances surrounding these two starts aren’t completely the same they do a good job illustrating the phenomena of cluster luck.

Cluster luck, originally discovered and coined by Joe Peta in his book Trading Bases, essentially tells us how lucky teams have been by measuring the difference in the expected number of runs scored by a team based on its power (total bases), and base runners (hits/walks) and its actual number of runs scored. In Alvarez’s July start above he was a victim of poor sequencing, allowing his hits in bunches rather than spreading them out over the course of his start. For a more complete (and easier to understand) definition and some real world examples check out this and this.

What I will be attempting to do in this article is figure out a way to accurately estimate how many runs a pitcher should have allowed, and subsequently what his run average should look like, and then pinpoint certain pitchers who have been lucky or unlucky so far this season. Basically I am trying to normalize a pitcher’s RA by adjusting for sequencing and cluster luck.

Fortunately for me the heavy lifting for part one has already been done thanks to Dan Smyth. His metric, Base Runs (BsR), was developed and popularized in the early 1990’s and is an extraordinarily simple yet accurate way of estimating runs allowed using standard box score statistics. Base Runs for pitchers takes four inputs, innings pitched, hits, walks, and home runs, which are converted into four factors, A, B, C, and D. The final formula looks like A*B/(B+C)+D. For a lengthier piece on Base Runs, it’s properties, and it’s pros and cons consult this and this.

I took these statistics, including run average, for every pitcher in the majors through July 12th and figured his expected runs allowed by Base Runs, then converted it to Base Run Average or BsRA and took the difference between BsRA and his actual RA. I also calculated the pitchers’ RA- and BsRA- by taking the pitcher’s RA or BsRA and divided it by the league RA or BsRA (for reference the league RA is 4.14 and the league BsRA is 4.19). By taking the difference between the two, (BsRA-)-(RA-), we can figure out the percentage of extra runs compared to league average the pitcher should have allowed.

In the tables below you’ll see I’ve given this stat the name Luck%, a poor name admittedly since we’re dealing with percentages and I’m sure the differences aren’t completely due to luck but the name will have to do until I think of something better. For example Max Scherzer’s RA- is 80.92 (RA of 3.35/league RA of 4.14) meaning he has allowed runs at around 81% of the league average, but his BsRA- is 88.62 (BsRA of 3.71/league BsRA of 4.19) meaning he should have allowed runs at around 89% of the league average. We then get a Luck% of 88.62-80.92=7.71, so Scherzer should have allowed 7.71% more runs compared to league average, he has a Luck% of 7.71.

Whew. Now we can get to the names.

First the top ten qualified pitchers who have had their numbers most positively affected by cluster luck.

I like this list since it is very diverse. We have pitchers who have been pleasant surprises this season but who we all know aren’t really that good (Vargas and Simon). Older pitchers experiencing a late career resurgence (Beckett and Buehrle). Great pitchers (Greinke and Tanaka) and not so great pitchers (Chen). Hard throwing (Alvarez) and soft throwing (Young). High strikeout and low strikeout etc. etc. It’s good to see that not just one type of pitcher is affected giving me confidence that cluster luck does play a factor in a pitchers numbers to such a degree even this late in the season.

Now on to the top ten pitchers who have had their numbers most negatively affected by cluster luck.

This is a slightly less diverse list. Most of these guys are having disappointing seasons, but perhaps they haven’t been as bad as we think. Four of these guys have a below average RA, but an above average BsRA (or perfectly average in the case of Kuroda). Then there’s Anibal Sanchez who might just be one of the most underrated pitchers in baseball as his BsRA is seventh in all of baseball.

So what does Luck% end up telling us about a pitcher? We know that pitchers have little control over what happens after a ball is put in play, but what we’re doing here is figuring out which pitchers have been victimized by poor sequencing. Perhaps we can look at Luck% the same way we look at BABIP. If the measure is abnormally high compared to a pitcher’s career rate and the pitcher hasn’t made a substantial improvement in his mechanics or pitch repertoire perhaps some regression is in order.

So is Anibal Sanchez due for a spectacular second half? Maybe not. A myriad of factors could be influencing his low Luck%. We know that in general offense goes up when runners are on base and Sanchez could be especially susceptible to allowing runs to score in bunches. He has a slow move to the plate potentially allowing more runners to steal and get in scoring position. Perhaps his stuff is less effective from the stretch due to a breakdown in mechanics. Maybe he focuses too much attention the runners on base and not enough on the one at the plate, I really don’t know.

I only have half a season of data on 100 or so pitchers so obviously more research is needed. One could find the correlation between Luck% and peripheral stats such as K% and BB%, or find year to year correlations for Luck% to find out how much variation is actually luck and how much is skill. I’d definitely be intrigued by those results and I’ll likely revisit these numbers when the season ends.

I’m still relatively new to performing this kind of analysis so any constructive criticism would be greatly appreciated or if you’ve seen something like this done elsewhere on the internet. If you have suggestions for any improvements (especially the name) or further research I’d love to here it. If you think I majorly screwed up somehow I’d love to hear about too.

Lists of baseball’s most underrated players are often interesting and thought-provoking exercises, because by definition they focus on players that tend to get less attention than they should. However, there isn’t an easy way to definitively say how players are “rated” by baseball followers. Writers often just list off players who have the attributes that they are looking for (grit, plate discipline, small market players, etc.), which isn’t a bad way of doing it.

However, there is a more scientific way of approaching a list like this. We could look at how many people are doing Google searches for specific players. It wouldn’t exactly tell us what players are most underrated, but it can tell us which players should be getting more attention; these two things are very tightly correlated. The key difference is that plenty of players get attention for things that don’t necessarily mean they are considered good players. Ryan Braun got a lot of attention during his steroid drama, Robinson Cano was heavily talked about during free agency, and people search for Carlos Santana because of this and this. But when good players draw very little interest from fans, they’re probably underrated. But the term I’ll use is under-popular.

Using Google’s Adwords Keyword Tool, I gathered the data on every player who has achieved a WAR of at least 3.0 since the beginning of the 2013 season. A regression model with those 132 players showed that an additional 1 WAR was worth 6,000 Google searches per month – not too shabby.

Here is a plot of these players, with the expected amount of Google searches on the horizontal axis, and the actual amount of searches on the vertical. While the keyword tool was incredibly useful, it rounds numbers when they get too high, and you can see a handful of players were rounded off to exactly 165,000 searches per month (FYI, these players were Mike Trout, Miguel Cabrera, David Ortiz, Robinson Cano, Bryce Harper, and Yasiel Puig). Derek Jeter has roughly double that amount, but his WAR did not qualify him for this list.

There are a lot of players who have played very well the last two years who are by no means household names. Welington Castillo has put up 3.8 WAR since the start of 2013, A.J. Pollock has been worth 6.1 wins, and Brian Dozier 5.8. In order to really measure who the most under-popular players are, I’ll use two methods. The first is just to simply subtract how many Google searches were expected and how many there really were.

According to this measurement, Josh Donaldson is the most under-popular player in baseball, because he should have been looked up 53,000 times per month more often than he was (68k vs. 15k). That’s a big difference. There are some excellent players on this list, with many players who have an argument as the best or one of the few best players at their position. But for the most part, these are well known players who should just be more well known.

A different way to measure under-popularity, and the way I think is more telling, is to find the ratio between expected and actual searches, as opposed to just subtracting. For instance, is Edwin Encarnacion more under-popular than, say, Luis Valbuena? Encarnacion should have gotten 41,000 searches per month, but actually only got 18,000. Valbuena, however, played like someone who should have been searched 20,000 times, but was only Googled 2,400 per month. Since I believe Valbuena’s numbers are more out of whack, I prefer the second method.

Here are the top 20 players using that measurement, where we see how many times a player was searched as a percentage of how many times you would expect them to be:

Jarrod Dyson has quietly become a well above average baseball player. In about 800 career PA, Dyson has a WAR of 6.8. That is All-Star level production. His elite fielding and baserunning skills (which have combined to be worth more than 3 wins these last two years) make his wRC+ of 91 more than acceptable.

A.J. Pollock appears high on both lists, and for great reason. This year he is quietly hitting .316/.366/.554, after putting up 3.6 WAR last year.

This method of establishing players who deserve more credit for their play certainly has some flaws. WAR is not the only way to measure how good a player is, and Google searches are not a perfect representation of how popular or famous players are. However, it takes away the guess work and opinions from the standard underrated player lists, and in that there is some value.

I. Introduction

In the statistics driven sport of baseball, the fans who once enjoyed recording each game in their scorecard have become less accepting of what they observe and now seek to validate each observation with statistics.  If the current statistics cannot support these observations, then they will seek new and authenticated statistics.

The following sections contain formulas for statistics I have not encountered, yet piqued my curiosity, regarding the 2010 Giants’ World Series starting rotation.  Built around Tim Lincecum, Matt Cain, Jonathan Sanchez, and Madison Bumgarner, the 2010 Giants’ strength was indeed starting pitching.  Each player was picked from the Giants farm system, three of them would throw a no-hitter (or perfecto) as a Giant, and of course they were the 2010 World Series champions.  Throw in a pair of Cy Young awards (Lincecum), another championship two years later (Cain, Bumgarner, Lincecum), eight all-star appearances between them (Cain, Bumgarner, Lincecum), and this rotation is highly decorated.  But were they an elite rotation?

II. Perfectos & No-No’s

It certainly seems rare to have a trio of no-hit pitchers on the same team, let alone home-grown and on the same championship team.  No-hitters and perfect games factor in the tangible (a pitcher’s ability to get a batter out and the range of the defense behind him) and the intangible (the fortitude to not buckle with each accumulated out).  Tim Lincecum, Matt Cain, and Jonathan Sanchez each accomplished this feat before reaching 217th career starts, but how many starts would we have expected from each pitcher to throw a no-hitter or perfect game?  What is the probability of a no-hitter or perfect game for each pitcher?  We definitely need to savor these rare feats.  Based on the history of starting pitchers with career multiple no-hitters, it is unlikely that any of them will throw a no-hitter or perfect game again.  Nevermind, it happened again for Lincecum a few days ago.

First we deduce the probability of a perfect game from the probability of 27 consecutive outs:

Table 2.1: Perfect Game Probabilities by Pitcher

The probability of a perfect game is calculated for each pitcher (above) using their exact career on-base percentage (OBP rounded to three digits) through the 2013 season.  Based on these calculations, we would expect 1 in 12,152 of Matt Cains starts to be perfect.  Although it didn’t take 12,152 starts to reach this plateau, he achieved his perfecto by his 216th start.  For Tim Lincecum, we would expect 1 in 19,622 starts to be perfect; but starting even 800 starts in a career is very farfetched.   Durable pitchers like Roger Clemens and Greg Maddux only started as many as 707 and 740 games respectively in their careers and neither threw a perfect game nor a no-hitter.  No matter how elite or if Hall of Fame bound, throwing a perfect game for any starting pitcher is very unlikely and never guaranteed.  However, that infinitesimal chance does exist.  The probability that Jonathan Sanchez would throw a perfect game is a barely existent chance of 1 in 94,488, but he was one error away from a throwing a perfect game during his no-hitter.

The structure of a no-hitter is very similar to a perfect game with the requirement of 27 outs, but we include the possibility of bb walks and hbp hit-by-pitches (where bb+hbp≥1) randomly interspersed between these outs (with the 27th out the last occurrence of the game).  We exclude the chance of an error because it is not directly attributed to any ability of the pitcher.  In total, a starting pitcher will face 27+bb+hbp batters in a no-hitter.  Using these guidelines, the probability of a no-hitter can be constructed into a calculable formula based on a starting pitcher’s on-base percentage, the probability of a walk, and the probability of a hit-by-pitch.  Later we will see that this probability can be reduced into a simpler and more intuitive formula.

Let h, bb, hbp be random variables for hits, walks, and hit-by-pitches and let P(H), P(BB), P(HBP) be their respective probabilities for a specific starting pitcher, such that OBP = P(H) + P(BB) + P(HBP).  The probability of a no-hitter or perfect game for a specific pitcher can be constructed from the following negative multinomial distribution (with proof included):

This formula easily reduces to the probability of a no-hitter by subtracting the probability of a perfect game:

The no-hitter probability may not be immediately intuitive, but we just need to make sense of the derived formula. Let’s first deconstruct what we do know… The no-hitter or perfect game probability is built from 27 consecutive “events” similar to how the perfect game probability is built from 27 consecutive outs.  These “event” and out probabilities can both broken down into a more rudimentary formulas. The out probability has the following basic derivation:

The “event” probability shares a comparable derivation that utilizes the derived out probability and the assumption that sacrifice flies are usually negligible per starting pitcher per season:

From this breakdown it becomes clear that the no-hitter (or perfect game) probability is logically constructed from 27 consecutive at bats that do not result in a hit, whose frequency we can calculate by using the batting average (BA). Recall that a walk, hit-by-pitch, or sacrifice fly does not count as an at bat, so we only need to account for hits in the no-hitter or perfect game probability. Hence, the batting average in conjunction with the on-base percentage, which does include walks and hit-by-pitches, will provide an accurate approximation of our original no-hitter probability:

Comparing the approximate no-hitter probabilities to their respective exact no-hitter probabilities in Table 2.2, we see that these approximations are indeed in the same ball park as their exact counterparts.

Table 2.2: No-Hitter Probabilities by Pitcher

The probability of a no-hitter is calculated for each pitcher (above) using their exact career on-base percentage, walk probability, and hit-by-pitch probability through the 2013 season.  Notice that the likelihood of throwing a no-no is significantly greater than that of a perfecto for each pitcher.  For example, Lincecum and Cain’s chances of making no-no history are far easier than being perfect by the respective factors of 15.9 and 11.5.  Although Lincecum and Cain are still both unlikely to accumulate the 1,231 and 1,055 starts necessary to ascertain these no-hitter probabilities.  If it’s any consolation, Lincecum already achieved his no-hitter by his 207th start (and another by his 236th start) and Cain already has a perfecto instead.

Furthermore, it’s possible for two pitchers with disparate perfect game probabilities to have very similar no-hitter probabilities, as we see with Sanchez and Bumgarner.  Sanchez has a no-hitter probability of 1 in 1,681 that is 56.2 times greater than his perfect game probability, while Bumgarner’s 1 in 1,772 probability is a mere 6.1 times greater.  This discrepancy can be attributed to Sanchez’ improved ability to not induce hits versus his tendency to walk batters, while Bumgarner’s improvement is of a lesser degree.  Regardless, Sanchez’ early no-hitter, achieved by his 54th start, can instill hope in Bumgarner to also beat the odds and join his 2010 rotation mates in the perfect game or no-hitter’s club.  Adding Bumgarner to the brotherhood would greatly support the claim that the Giants 2010 starting rotation was extraordinary.  However, the odds still fall in my favor that I will not need to rewrite this section of this study due to another unexpected no-no or perfecto by Lincecum, Cain, Sanchez, or Bumgarner.

Before the 2012 season, some folks drafted J.D. Martinez as a deep sleeper, coming off a decent debut with the Astros in 2011 and a solid minor league profile. He went on to slug only 11 HR in 439 PA and hit a disappointing .241/.311/.375.  What went wrong? Well, he pounded the ball into the ground at a 51.8 % clip. His line drive % dropped to 16.6 % and he hit only 31.6 % flyballs. It’s hard to hit HR’s and hit for average with that kind of batted ball profile.

He got demoted to AA after failing to impress in 2013 and got injured. This year, for the Tigers, he mashed in AAA, was called up in late April, and has already hit 7 HR’s in only 117 PA with a .312/.342/.596 batting line. So what has changed? Read the rest of this entry »

The struggles of Starlin Castro and Anthony Rizzo during the 2013 season were well documented. Chicago Cubs fans’ hopes and dreams rested on these two young players to be the cornerstones of the long and painful “rebuild” on the North Side and it appeared that maybe they were not cut out for such lofty expectations. The lineup around them offered little in the way of quality. Pitchers shifted most of their focus on these two and they struggled terribly. Starlin Castro owned a triple slash of .245/.284./.347. which led to the questioning of his focus and ability. Anthony Rizzo did not exactly turn any heads either, batting .233/.323/.419. At least Rizzo’s peripherals offered some hope that some positive regression was in store for the 2014 season. To say the least, 2013 was a down year for both young players.

When the 2014 season arrived, the script was quite different. Castro and Rizzo set out to silence the critics. With the disappointing 2013 season in the rearview mirror, both are producing at all-star levels so far this season. Castro’s mainstream statistics look spectacular, with a triple slash of .287/.331/.484 including 11 home runs and 43 RBIs (already matching his 2012 counting stats). That production at the premium position of shortstop makes it all the better. Here’s a look at Castro’s underlying statistics from 2013 and 2014:

Castro has improved greatly across the board. He is swinging at less pitches out of the zone which is paying dividends towards his BB% and K%. He ranks 3rd in both wOBA and wRC+ among all shortstops, behind Troy Tulowitzki and Hanley Ramirez. It is amazing to think that he is still pre-peak in the power category since he has been in the MLB for almost five full seasons. He is on pace for a career high in home runs this year collecting 11 so far. I think that it is safe to say that last year’s Castro was an illusion. He appears to be on his way to stardom just as the Cubs rebuild comes to a close.

Over at first base, Anthony Rizzo looks like the player Theo Epstein and Jed Hoyer thought he was going to be when they traded for him. This year, his production is nothing short of spectacular with a .278/.400/.506 triple slash including 15 home runs and 48 RBIs. That production is drawing comparisons to Joey Votto. The growth in his game can also be seen in his sabermetric stat line from 2013 and so far in 2014:

Just like Castro, Anthony Rizzo drastically improved across the board (minus K%). Rizzo ranks 4th in wOBA and 5th in wRC+ among all first basemen. He has improved his defense and looks very comfortable at the plate. He too is on pace for a career high in home runs, racking up 15 already. Rizzo is showing that he can be a huge threat at the plate for years to come.

This was a crucial season for both Castro and Rizzo. The Cubs organization, having given out long term contracts to both, depended on them becoming mainstays in the lineup when they finally become threats in the NL Central. With Rizzo on pace for 4+ WAR this season and Starlin on pace for 3+ WAR, it looks like they really are the budding stars that Epstein and Hoyer believed they would be. With these two all-stars, Javier Baez, Kris Bryant, and the other top prospect talent the Cubs possess, the future looks very bright on the North Side of Chicago.

Before coming to the MLB and smashing 20 home runs in just his first 58 games, Jose Abreu had a prolific career in the Cuban Baseball National Series (Cuba’s top championship), starting at the very early age of 16, when he would play at first, second, third or in the outfield. While doing so, he averaged .271 with five homers and 21 RBIs in 71 games. He seemed like a very hot prospect, taking into account how old he was (or how young, for that matter), and for that very short stretch (say for the 2003-04 and the 2004-05 seasons) he seemed overpowered by pitchers, some of whom were old enough to be his father. From then on, he owned them.

For his career in Cuba, Abreu fell shy 16 homers of 200 in ten seasons. Yet, it was his youth that kept him from getting them early season-wise. Up to his 21-year-old season, his career-high in dingers was 13 (that very year) and had collected more than ten only once (11 in 2005-06), when he had what could be called his breakthrough year, hitting .337, with 105 hits and 64 RBIs in 84 games. Read the rest of this entry »