Platoon splits have become a major part of baseball today.  The Athletics have ridden a split of Jaso and Norris to production from their catcher position.  Many left-handed starters have had success against righties and many have struggled.  Over the course of his career Cole Hamels has had more success against RHB than LHB (.294 vs .301 wOBA).  Hamels is known for his best pitch — his change up — which has helped him neutralize RHB throughout his career.

So often when a LHP struggles against RHBs the common fix is to use a change up more often or to improve the change up.  However, for some pitchers this model does not work.  Dallas Keuchel, a pitcher who used a change up as his primary offspeed pitch against RHBs in the beginning of his career struggled still against righties.  As shown in this article, from the time his career began until May 31st of this season Keuchel was one of the worst starting pitchers against RHB.  However, by breaking this down season by season it can be seen that Keuchel’s numbers have actually improved as his career’s progressed.

The key to Keuchel’s increased success against opposite-handed hitters seems to be found in his pitch selection.

Keuchel has been an often-discussed topic on this site this season.  The key to his success this season has been his increased use of his rapidly-improving slider which was covered by Eno Sarris here. As Sarris states the slider will allow Keuchel to have increased success against lefties.  However, looking at Keuchel’s splits this season shows he has improved his numbers against righties significantly. According to PitchF/x data Keuchel has used the slider significantly more against righties this season.  He has done this at the expense of four-seam fastballs opting to throw more two-seamers and sliders.

While he is still using the changeup at around his career averages, his heavy increase of sliders in the biggest difference in his way of attacking hitters.  As his numbers for the season have shown he is limiting home runs and striking out the highest percentage of right-handed hitters in his career.  This has also lead to a significant improvement in his wOBA allowed. This season against righties the slider has produced a better than MLB average whiff rate (18% vs 13.%).

Keuchel provides an blueprint for other left-handed starters who struggle against righties.  Contrary to typical belief that in order to improve against opposite-handed batters pitchers must develop their change up, Keuchel has begun using his best offspeed pitch — the slider — more as a putaway pitch against off-handed batters.  Keuchel has become the poster boy for pitching to strengths, riding his sinking two-seam and slider to a breakout season while significantly improving his platoon splits.

Another pitcher who was mentioned as the worst in the league against righties was Eric Stults.  Stults, a lefty like Keuchel, features both a slider and a change up.  Additionally, much like Keuchel, Stults’s best offspeed pitch according to pitch values is his slider.  However, looking at his pitch selection to RHB he has used the change more than twice as much as the slider since 2007 (25.6% vs 10.8%).  If Stults followed in the footsteps of Keuchel and began to use his best pitch more against opposite-handed hitters it could cause him to minimize his platoon split and make him a better all-around starting pitcher.

Earlier this month the Yankees took a chance on Brandon McCarthy, trading Vidal Nuno to the Diamondbacks for the sinker-balling right hander.  While McCarthy’s numbers in Arizona were ugly (5.01 ERA), his FIP was much better (3.82).  Through 4 starts the investment the Yankees made has paid off.  McCarthy has pitched to a 3-0 record with a 2.55 ERA.   Since the trade many of McCarthy’s peripherals have not changed much however, there have been a few differences.

Two keys factors for pitcher success — K% and BB% — have not changed much with K% decreasing slightly and BB% increasing sightly, although neither can be looked at as the reason McCarthy has been so much better since the deal.  Another important stat to look at is his BABIP, which, has improved a few percentage points since the beginning of the season but is still above his career average of .297.  However, a major difference that can be seen in McCarthy’s numbers since the trade is his GB%.  In recent seasons as McCarthy has began featuring his sinker more his groundball percentage has increased significantly.  the 55.3% he showed with the Diamondbacks was more than 7 percentage points higher than the career high he set in 2013.  Seeing this major change in GB% opens the question of what exactly McCarthy has been doing differently with the Yankees.

During a few of McCarthy’s starts with the Yankees, New York broadcaster Michael Kay has mentioned that McCarthy did not throw his cutter as frequently with the Diamondbacks compared to how often he has used it since the trade.  Looking at his PitchF/x pitch selection data does show an increase in the use of his cutter but it also shows several other interesting trends.

As Kay has noted McCarthy has used his cutter more frequently but the increase is minimal compared to several other big changes McCarthy has made.  With the Dbacks McCarthy used his curveball more that a quarter of his pitches making it his second most frequently used pitch.  However, once he was traded McCarthy had been using the cutter as his second most common pitch.  However, the significant drop in his curveball usage did not get added to his cutter usage it instead was added to a pitch he did not use in Arizona, a four seam fastball.

Since his trade to New York from Arizona Brandon McCarthy has been a completely different pitcher.  While his ability has not changed and the park has not been much of an improvement (103 for NY 104 for ARI) the biggest difference in McCarthy as a pitcher has been in his pitch selection, once again featuring a four seam fastball while reducing the usage of his curveball.  To this point the move to the Yankees may have been exactly what McCarthy’s career needed simply because it allowed him to change the way he attacks hitters.

Stats in this post are current up to right before the July 31, 2014 PIT-ARZ game.

The MLB non-waiver trade deadline just passed. I’m not interesting in debating what teams should or should not have done except to say the price for quality players was very high this year. The whole supply & demand, free market thing really worked in the favor of teams that were already out of the post season race. It was suggested that the Pirates needed a right-handed batter (RHB), since they don’t do well against left-handed pitching (LHP). I had my doubts this was really true believing adding an additional RHB won’t improve the team much. MLB teams generally do better against LHP, since most batters are RHB and the RHB/LHP split favors the batter.

Before getting into this, LHP make up only 21% of the Pirates’ season-to-date plate appearances, out of all the problems the Pirates could have making a roster move to address this isn’t necessary unless you are looking to platoon. More on that later.

Looking at the team batting splits, the Pirates have an overall .722 OPS and a LHP .670 OPS. On the surface, it appears they are performing worse against LHP, and I will concede the argument the Pirates HAVE performed worse against LHP so far in 2014, but this shouldn’t continue going forward.

The Pirates have 4,152 plate appearances racked up thru July 30th, but only 867 of them have occurred against LHP (~21%). To put this in perspective, that is equivalent to less than one month of games. How accurate are batting statistics at the end of April? They aren’t. Put simply the Pirates ‘struggles’ against LHP can mostly be attributed to a small sample size.

I went and laid out all the outcomes (1B, BB, 2B, etc.) in a vector of plate appearances and had the computer randomly draw 900 samples from the entire Pirates season and computed the OPS 1000 different times. Then I plotted them below.

Due to the central limit theorem the mean should hover around .720 (the overall OPS) and the data should be normally distributed. Because of this I constructed the normal distribution curve and then used that to calculate the probability that a 900 plate-appearance sample can be drawn from the Pirates’ total plate appearances. It turns out 9% of the time the program will select plate appearances that total a < .670 OPS. 9% isn’t that likely, but it is not outrageous to conclude the Pirates’ low vsLHP OPS is due to small sample size.

This is not just applicable to LHP vs overall splits, but any low-percentage split including RISP. I wrote about this previously and came to a similar conclusion.

The composite distribution curves below illustrate what happens when sample size increases and why small small sizes are problematic. The vertical line is the .670 OPS mark. On the 900-sample distribution (vs LHP) there is a 9% probability of drawing a .670 OPS from the Pirates’ total plate appearances. This is the area underneath the curve to the left of the red line. Using the 3000-sample distribution curve, it’s 0.0016%. There is barely any area under the 3000-PA curve at that point, and this is a huge difference. (3000 samples are approximately how many the team has had against RHP.)

One more graph! This is a histogram of the differences between the LHP OPS and the overall OPS. The Pirates are on the low end of it. Not great, but there’s a lot of variation there.

Switching from statistics to baseball, the Pirates have the second-fewest plate appearances against LHP in MLB. They are 11-9 in games started by a LHP. That alone should discount the poor-performance-against-LHP argument, but obviously the team batting stats suggests that they are and it has been woven into a narrative.

Looking closely at the Pirates’ roster there are many solid RHBs, McCutchen (their best hitter), Martin, Marte, Sanchez, and Mercer/Harrison are pretty good against lefties. Now, some of these player are underperforming against LHP this year, but this is where the small sample size comes in again. You wouldn’t determine any of these batters lost their platoon advantage after only 80 plate appearances. Going forward almost all of these bats should regress to their normal platoon splits.

Pedro Alvarez, Gregory Polanco, Ike Davis. Their platoon splits are pretty atrocious both for 2014 and career-wise. For example, Alvarez has a .787 OPS vs RHP and a .517 OPS against LHP this year. I don’t want to get into analyzing what’s wrong with the Pirates’ left-handed bats, except to say they are terrible against LHP. The argument should change from the Pirates don’t do well against LHP to the Pirates’ left-handed batters are terrible against LHP.

What can be done about this? The simple answer is to get better left-handed batters. Since that’s not really possible, the next best option would be platooning the left-handed batters. Ike Davis is already platooned with Gaby Sanchez, and Pedro Alvarez is barely starting any games. Polanco has regressed from his debut, but I think the best idea is for him to play everyday and deal with LOOGY relievers. I also don’t know how many fans actually want to see or are suggesting that he’s should be platooned. With all this in mind I’m not quite sure what acquiring a right-handed bat would accomplish. The Pirates are already trying to find a place for RHB Josh Harrison to play. He’s been having a good season, no matter what you think about Harrison. Furthermore, the Pirates have a guy who’s been killing LHP this year and has decent splits against them for his career. And that’s Jose Tabata.

Bottom line, adding a RHB wouldn’t help much because the team splits are still a small sample size against LHP. Beyond the statistics, the two big left-handed bats have terrible splits against LHP, and these problems have been already addressed by platooning and benching.

Introduction

A couple months back, I introduced a new method of calculating pitch values using a FIP-based WAR methodology.  That post details the basic framework of these calculations and  can be found here .  The May and June updates can be found here and here respectively.  This post is simply the July 2014 update of the same data.  What follows is predominantly data-heavy but should still provide useful talking points for discussion.  Let’s dive in and see what we can find.  Please note that the same caveats apply as previous months.  We’re at the mercy of pitch classification.  I’m sure your favorite pitcher doesn’t throw that pitch that has been rated as incredibly below average, but we have to go off of the data that is available.  Also, Baseball Prospectus’s PitchF/x leaderboards list only nine pitches (Four-Seam Fastball, Sinker, Cutter, Splitter, Curveball, Slider, Changeup, Screwball, and Knuckleball).  Anything that may be classified outside of these categories is not included.  Also, anything classified as a “slow curve” is not included in Baseball Prospectus’s curveball data.

Constants

Before we begin, we must first update the constants used in calculation for Jule.  As a refresher, we need three different constants for calculation: strikes per strikeout, balls per walk, and a FIP constant to bring the values onto the right scale.  We will tackle them each individually.

First, let’s discuss the strikeout constant.  In July, there were 47,449 strikes thrown by starting pitchers.  Of these 47,449 strikes, 4,585 were turned into hits and 13,750 outs were recorded.  Of these 13,750 outs, 3,725 were converted via the strikeout, leaving us with 10,025 ball-in-play outs.  10,025 ball-in-play strikes and 4,585 hits sum to 14,610 balls-in-play.  Subtracting 14,610 balls-in-play from our original 47,449 strikes leaves us with 32,839 strikes to distribute over our 3,725 strikeouts.  That’s a ratio of 8.82 strikes per strikeout.  This is exactly the same as our from 8.82 strikes per strikeout in June.

The next two constants are much easier to ascertain.  In July, there were 26,244 balls thrown by starters and 1,328 walked batters.  That’s a ratio of 19.76 balls per walk, up from 19.36 balls per walk in June.  This data would suggest that hitters were slightly less likely to walk in July than previously.  The FIP subtotal for all pitches in July was 0.52.  The MLB Run Average for July was 4.17, meaning our FIP constant for May is 3.65.

The following table details how the constants have changed month-to-month.

Pitch Values – July 2014

For reference, the following table details the FIP for each pitch type in the month of July.

As we can see, only three pitches would be classified as below average for the month of July: sinkers, cutters, and changeups.  Four-Seam Fastballs and curveballs also came in right around league average.  Pitchers that were able to stand out in these categories tended to have better overall months than pitchers who excelled at the other pitches.  Now, let’s proceed to the data for the month of July.

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Pitch Ratings – July 2014

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Monthly Discussion

As we can see, Cole Hamels takes the top for this month due to the  strength of his overall repertoire.  Hamels was classified as throwing five different pitches in July (Four-Seam, Sinker, Cutter, Curveball, and Changeup) and managed to earn at least 0.1 WAR from all five.  The most valuable pitch overall in July was Ian Kennedy’s Four-Seam Fastball.  The least valuable was Drew Hutchison’s Changeup.  As far as offspeed pitches, Garrett Richards’s 0.5 WAR from his slider lead the way.  The least valuable fastball was Hector Noesi’s cutter.

On our 20-80 scale pitch ratings, the highest rated qualifying pitch was Chase Whitley’s changeup.  The lowest rated pitches were the changeups thrown by Drew Hutchison and Mike Minor, Hector Noesi’s cutter, and Josh Beckett’s curveball.  The highest rated fastball was Drew Hutchison’s four-seam fastball.

Pitch Values – 2014 Season

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Year-to-Date Discussion

If we look at the year-to-date numbers, AL FIP and MLB WAR leader Felix Hernandez still sits in the top spot.  Current MLB FIP leader Clayton Kershaw ranks ninth.  The least valuable starter has been Marco Estrada.  On a per-pitch basis, the most valuable pitch has been Ian Kennedy’s four-seam fastball.  The most valuable offspeed pitch has been Garrett Richards’s slider.  The least valuable pitch has been Marco Estrada’s four-seam fastball.  The least value offspeed pitch has been Marco Estrada’s changeup.  Needless to say, it’s been a rough year for Marco.  Qualitatively, I feel fairly encouraged by the year-to-date results so far.  The leaderboard is topped by two no-doubt aces, both of whom currently in the top two in their respect leagues in FIP, and Marco Estrada comes in at the bottom after posting the highest FIP among qualified starters so far.  For reference, the top five in the year-to-date overall rankings are currently 1st, 12th, 10th, 2nd, and 9th on the FanGraphs WAR leaderboards respectively.

I am currently in the process of finalizing a MLB/MLBPA licensed board game, in the realm of Strat-o-matic.  However, with the license, I will be able to offer player cards with player photos, team names and logos.

The game has been about 90% developed, and I’d like to hire someone to review all of the statistical calculations, to ensure that they make sense for providing the most accurate and realistic gameplay.  In addition, there are a handful of statistical elements within the game (primarily defensive ratings, etc), for which I could use your thoughts on the best way to calculate the rating.

I’m looking to hire someone (or a group of people, if you’d like to play the game and test it amongst yourselves) for approximately $2000 for this project.

Please email me at pdurkee7528@msn.com if you’re interested in the job.

Thanks!

Over the last couple of weeks, I’ve been looking into how a player’s 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. In the future, I plan to engineer an alternative methodology to go along with this one, that takes into account how a player performs in the majors, rather than his just getting there.

For hitters in Low-A and High-A, age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America all played a role in forecasting future success. And walk rate, while not predictive for players in A-ball, added a little bit to the model for Double-A and Triple-A hitters. Today, I’ll look into what KATOH has to say about players in Rookie leagues. Due to varying offensive environments in different years and leagues, all players’ stats were adjusted to reflect his league’s average for that year. For those interested, here’s the R output based on all players with at least 200 plate appearances in a season in Rookie ball from 1995-2007.

Just like we saw with hitters in the A-ball leagues, a player’s walk rate is not at all predictive of whether or not he’ll crack the majors. Unlike all of the other levels I’ve looked at so far, a player’s Baseball America prospect status couldn’t tell us anything about his future as a big-leaguer. This was entirely due the scarcity of top-100 prospects in the sample, as only a handful of players spent the year in rookie ball after making BA’s top-100 list.

The season is less than 40 games old for most rookie league teams, which makes it a little premature to start analyzing players’ stats. But just for kicks, here’s a look at what KATOH says about this year’s crop of rookie-ballers with at least 80 plate appearances through July 28th. This only considers players in the American rookie leagues — the Appalachian, Arizona, Gulf Coast, and Pioneer Leagues, meaning it excludes the Dominican and Venezuelan Summer Leagues. The full list of players can be found here, and you’ll find an excerpt of those who broke the 40% barrier below:

What stands out most is that KATOH doesn’t think any of these players are shoo-ins to make it to the majors. Even those who are hitting the snot out of the ball get probabilities that fall short of what we saw for unremarkable performances in Double-A. Kevin Padlo, for example, gets just a 73%, despite hitting a ridiculous .317/.463/.619 as a 17-year-old. Its hard to do much better than that. I think this really speaks to how little rookie ball stats matter in the grand scheme of things. A good offensive showing is obviously better than a poor one, but numbers from this level need to be taken with a huge grain of salt. A hitter’s performance against pitchers who are fresh out of high school just can’t tell us much about how he’ll fare when matched up against more advanced pitching at the higher levels.

Next up, I’ll complete the series by looking at stats from short-season A-ball. Teams at that level are also only a few weeks into their season, but at the very least, it will be interesting to see how KATOH feels about SS A-ballers in general. Next week, I’ll apply the KATOH model to historical prospects and highlight some of its biggest “hits” and “misses” from the past.

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

Tip: Click on any acronyms for an explanation in the FanGraphs glossary of terms.

With his final turn in the rotation for July completed, we’ve now had almost exactly one full year of Sonny Gray – one year of the 24-year-old starting pitcher, the up-and-coming staff ace, the dueler of Playoff Verlanders. In that year, we’ve seen him do some great things, like going eight innings with nine Ks and no runs against the Tigers in Game 2 of the 2013 ALDS. We’ve also seen MLB Fan Cave forcing him to prank New Yorkers as a result of some unknown fine print embedded in his rookie contract. Above all else, the one thing we’ve always known is that Sonny Gray has a really good curveball. Let’s take a look at it for all of its 12 to 6, 80-MPH Uncle Charlie glory, from a game against the Astros in August of last year:

How good is his curveball? He has never given up a home run off of the pitch, with the only extra-base hits against the curve in his career being four doubles. In the past calendar year, Sonny Gray has saved more runs with his curveball than any other pitcher in baseball, and is behind only Corey Kluber and Yu Darvish in Runs Saved/100 curveballs. Having watched Kluber a lot, I suspect his slider/slurve is actually being classified as a curveball; I think it looks like a slider, but PITCHf/x doesn’t, so I will defer to the all-knowing pitch computer. Regardless, with the metrics we’re about to examine, Sonny Gray has one of the best curveballs in the game. What we’re going to focus on specifically are the advances in his curve’s effectiveness, spurred on by an adjustment in the way he throws the pitch.

To start, let’s take a look at the top-15 starters by wCB and wCB/C for the past calendar year:

As stated before, Gray is at the top in both of these categories. We should put a little more stock into wCB/C, as it normalizes all pitchers to runs saved per 100 pitches, taking away the advantage that one player might have due to throwing a certain pitch more frequently than another player. This is important for what we’re looking at, because Sonny Gray throws a lot of curveballs. How frequently does he throw curveballs? Here are the leaders for percentage of curveballs thrown over the last calendar year:

The words “second only to Scott Feldman” don’t come up very often, but here they are. Gray throws his curveball a ton. Not only has he always leaned on the curve as a major weapon in his arsenal, but he has actually increased his number of curves thrown since he came into the league every month except for May (when he maintained his % thrown) and June of this year, when he seemed to temporarily lose a feel for the pitch and threw more changeups. However, his first start of July had Gray saying this after holding Toronto to one run over seven innings:

“That was the idea, to really get (it) going again,” Gray said of the curveball. “I think the last five or six starts it’s been OK, but it hasn’t been a big factor. We did some things a little different this week and I was able to find that again.”

Over the last 30 days, Gray has thrown the curveball more than ever, up to over 32% for the month. Not only that, he has found more effectiveness in the pitch, with his whiff % on the curve up to a career-best 19.2% during July. There’s also reason to believe that this isn’t simply a good month for Sonny Gray’s curveball – what we are now seeing is the fruition of a change of approach with the way he throws the pitch that has been coming for some time now. Let’s take a look.

Here we have the release speed of Sonny Gray’s curveball for every start since he was called up:

He’s throwing the curve harder than he ever has, adding over three miles per hour since he started pitching in the majors. That’s not a small change. On top of the speed increase, he’s cut about 2.5 inches of vertical movement off his curve between his first start in the majors and now:

Finally, he’s added more three-dimensional depth to his curve in the form of a top-3 best horizontal movement over the past calendar year. Only Corey Kluber and Charlie Morton have had better horizontal movement on their curves in that time period.

Add all of that up, and we have this 84-MPH curve from his last start against the Orioles:

It now looks more like a slurve, with its high release speed and nasty late break away from right-handed hitters. As Eno Sarris included in his great article from October of last year, Gray said he “adds and subtracts” with the same grip on his curve to move between the 12-to-6 and slurve (which is sometimes classified as a slider) varieties. However, it seems as if he has leaned more toward the slurve option as time has gone on.

One question that arises out of this is “why throw the slurve more?”

Given his whiff % on the curve has increased as he has added velocity, I’d say that fact alone has supported the move to the slurve over the 12-to-6. However, there’s another potential reason that isn’t strictly rooted in statistics, and could be more about what goes into an elite pitching approach: by increasing his arm speed and flattening out the vertical movement of his curve, Gray can further deceive batters into thinking he’s throwing hard pitches before the bottom drops out. His struggles to find consistency with the changeup are well documented, so why shouldn’t he adjust his best breaking pitch to better fool hitters for whiffs and weak contact? As we’ve seen with Yu Darvish, the pinnacle of an ace approach may be one that includes a “great convergence” of arm slots and release points, in which every pitch looks hard until it’s not, or until it is.

Gray’s horizontal release points for all of his pitches are closer to one another than they ever have been during his major league career. Not surprisingly, his curveball and fastball were released on average at the almost identical horizontal point during his May and July starts, when he posted career-best whiff rates on his curveball (18.6% & 19.2%, respectively). June was an aberration, as Gray seemed to lose his release point in general and was tinkering with his delivery, leaning more on the changeup:

Sonny Gray has work to do on parts of his game before he takes the next step into the true elite of starting pitchers. His walk rate has actually increased this year to 8.5%, owing mostly to a lack of fastball command in deep counts, and his changeup is still very much a work in progress as a third pitch. However, his adoption of the hard curve and syncing of arm angles is a positive step toward dominance, and is a sign that he knows what works; he’s now perfecting it.

And now, my first go at a DShep Darvish-like GIF of Sonny Gray’s 12-to-6 curve from last August along with his harder slurve from his last start to compare:

Earlier this week, Hall of Fame outfielder Carl Yastrzemski told the media that he believes that DH David Ortiz is second to only Ted Williams as the greatest hitter in Red Sox history. Many people believe that Yaz is the next-best hitter after Teddy Ballgame. I want to determine who is the better hitter.

To do this, we have to look at the wOBA or weighted on base average which weighs the values of the many different ways a player gets on base based on each way’s ability to produce a run and puts them into a single analytical number. The formula for this statistic is listed below:

wOBA = (0.690×uBB + 0.722×HBP + 0.888×1B + 1.271×2B + 1.616×3B +
2.101×HR) / (AB + BB – IBB + SF + HBP)

The graph of the wOBA for Ortiz, Yaz, and the average player at each of the ages that they have played at can be seen in the graph below:

Source: FanGraphs — David Ortiz, Carl Yastrzemski

Although it was only a slightly better wOBA in his best ten seasons (non-consecutive), Ortiz’s .409 is superior to Yaz’s wOBA of .404. I only used their best 10 seasons because Ortiz’s career is not over yet so Yastrzemski would have a larger sample size of seasons. However, these numbers are just the beginning. Below is a graph of each player’s wOBA for a specific year compared to the league average of that year:

Source: FanGraphs — David Ortiz, Carl Yastrzemski

Using this graph, I determined each player’s ten seasons in which they had the greatest range between their wOBA and the league’s wOBA. In this situation, Yaz had a .107 greater wOBA than the league did in those ten seasons, compared to Ortiz’s .092. That is a .15 difference, which is greater than the .05 difference for the wOBA for each player’s ages shown in the first graph.

If I could take either one of these players based solely on offensive production, I would choose Yaz because his production compared to the league average of the era that he played in is greater than that of Ortiz.

Thanks for the selfless comments Yaz, but you are the second-best hitter in Red Sox history.