Author Archive

Our Playoff Odds Have a New Look

The Playoff Odds page looks different! The playoff-odds data and simulation method remains the same; however, we have revamped our reporting page to make it easier to understand and more powerful.

The most noticeable changes are the table layout and the mobile layout. We’ve tried to make it easier to understand what the columns mean for users who are new to the site. The goal of the mobile layout is to allow users to reach the most important information more quickly. Every column on the desktop page is viewable on the mobile layout by clicking the “Full” button.

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The Homepage Has Been Redesigned!

As you may have noticed when you clicked on this post, the FanGraphs homepage has received a makeover. The previous front page worked well enough for a number of years. The way we all use the web has changed, however, so it was time for some adjustments, intended to help you find more of what you want in an easier-to-use manner. The new layout features a number of improvements, and while change is never enjoyable, we hope you’ll find these tweaks will help you get to the content you’re looking for more quickly, as well as highlight content that you might have missed previously.

The biggest change is that the new design is responsive, meaning it will work well on mobile devices, not just desktops and laptops. We’ve also designed the new front page to highlight our daily written content, the outstanding prospect work being put together by Eric Longenhagen and Chris Mitchell, and provide access to the tools that let you utilize all the great data here on the site.

More specifically, we have:

  • included the daily Hardball Times article in the featured section, highlighting one of the best-kept secrets on FanGraphs; the daily THT piece isn’t to be missed. We also changed the featured-article area to better highlight popular content, including pieces from RotoGraphs, as well as identify the most recent and most read posts of the day.
  • merged InstaGraphs into the FanGraphs article flow. InstaGraphs posts are still shorter, quicker articles, but they won’t be off in the corner any longer. They will be noted by an InstaGraphs tag, so you’ll still know to expect something a bit shorter than the usual FanGraphs post.
  • included our standard chat schedule and a chat-alert banner for when they are happening.
  • improved the Top Prospects box, so all 30 teams are accessible instead of just the last five articles Eric Longenhagen has written.
  • improved our “Essential” section to include evergreen articles and site news, and to highlight some of our data tools.

The new layout will be more dynamic, including more features as the season progresses and as certain content becomes more topical, such as during the draft or the trade deadline. We hope that these improvements will let you navigate the numerous articles we publish each day, as well as better find reference pieces that you’ll want to go back and read multiple times.

Note that not every page on the site has been made mobile-friendly yet. As we become accustomed to the new design, please don’t hesitate to let us know about any questions, comments, or further improvements that you’d like to see integrated into the new homepage.

Thanks for being loyal readers and supporting FanGraphs through the years. We hope this new front page makes your visits even more enjoyable.

Introducing Team Pages!

We now have landing pages for all 30 Major League Baseball teams. These pages can be accessed through the Teams menu on the navigation bar above, simply by clicking on a team’s name. You can also access the pages directly, like so (no spaces in team names):

Many of the stats and features on the team pages are available in similar forms elsewhere on the site. We’ve now collected them into one place, however, so that readers can more quickly access team-specific information and analysis. As with any new addition to FanGraphs, we plan on expanding and adding features to the team pages as time goes on.

There are five different tabs on each page: Summary, Stats, Schedule, Pitcher Usage, and Depth Chart.


This is a quick overview of the team’s season. It includes boxes for the next and most recent games, division standings, team stats, depth chart, and roster notes.

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2017 MLB Arbitration Visualization

It’s that time of year again! This past Friday was the filing deadline for arbitration-eligible player contract offers. Once these numbers are published, I like to create a data visualization showing the difference between the team and player contract filings. (See the 2016 version here.) If you are unfamiliar with the arbitration process here’s the quick explanation from last year:

Teams and players file salary figures for one-year contracts, then an arbitration panel awards the player either with the contract offered by the team or the contract for which the player filed. More details of the arbitration process can be found here. Most players will sign a contract before numbers are exchanged or before the hearing, so only a handful of players actually go through the entire arbitration process each year.

The compiled team and player contract-filings data used in the graph can be found at MLB Trade Rumors.

Three colored dots represent a different type of signing: yellow represents a mutually-agreed contract signed to avoid arbitration, red represents the award of the team’s offer in arbitration, and blue represents the award of the player’s offer. A gray line represents the difference in player and team filings. Only players with whom teams exchanged numbers on January 13, 2017 will have grey lines. These can be filtered by clicking the “Filed” button.

The “Signed” button filters out players who have signed a contract for 2017; this will change as arbitration hearings occur. Finally, “All” includes every player represented in the graph. This year Jake Arrieta and Bryce Harper had the two largest contracts ($15.367M and $13.625M, respectively), but they both signed contracts before the filing deadline. This causes changes on the x-axis scale on the “Signed” and “All” tabs compared to the “Filed” tab, which is scaled to contracts under $10M.

The chart is sorted either by contract value or by the midpoint of the arbitration filings. The midpoint is the average of the two contracts and determines which contract the arbitrator awards based on his assessment of the relevant player’s value. The final contract value takes precedent over the midpoint since this represents the resolved value. Contract extension details will be written out over the data points. For our purposes, an extension is a multiyear deal that can’t be shown on the graph, since we are looking only single-year contracts for 2017.

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Splits Leaderboards!

Here it is: the split leaderboards! Now, you can create custom splits using multiple splits, much like you can on the player pages — except now in the form of an entire leaderboard, and accessible directly from the leaderboard menu.


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New Interactive Splits Tool!

We’ve created an interactive splits tool that allows you to create your own custom reports by combining splits of various metrics. All the splits that FanGraphs hosts are featured here, along with some new ones — including times through the order, outs and day/night.

The controls have three different sections: stats, splits and group by.

Kris Bryant Splits Tool Overview

The “Stats” bar allows you to toggle between the three different groups of stats we currently host on a player’s split page. This isn’t too different from the standard, advanced and batted-ball tabs we feature elsewhere on the site.

The “Splits” bar is the most important control within the splits tool; this is where you can select which splits are applied. When no splits are applied, you’ll get the full season stats. When a split is applied like “vs. LHP,” you’ll get only the plate appearances against a left-handed pitcher. If you add another split like “Groundballs,” you’ll get all ground balls against left-handed pitchers. As you add splits from different categories, you’ll narrow the number of plate appearances.

Kris Bryant Splits Menu

The splits which are applied appear as blue blocks above the table. If you wish to remove a split, either click the “X” on the split or unselect it within its menu.

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Updated Player Graphs!

We have updated the graphs on our player pages that have been a part of the site since FanGraphs was founded in 2005. The player graphs are now much more interactive and have been updated to feature some of the most popular and commonly used advanced stats on FanGraphs, such as WAR, wRC+, wOBA, OPS and FIP. We are also retaining the left/right and home/away splits options. These new graphs are interactive and have tool tips available on some data points.

Updated Player Graphs Season

There are four modes that represent different ways to delineate time: By Year, By Age, By Day and By Game. By Year and By Age are similar to each other; they replicate what has previously been available on the player pages showing season stats on a line graph with a league-average line. The league-average line is the most noticeable difference between Year and Age. Since the league average for a season is different than the average production for a given age.

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Job Postings Word Cloud

Over the past year, we have posted 32 different job postings from 20 different Major League Baseball teams and 15 job postings from TrackMan, Baseball Information Solutions, Inside Edge, STATS Inc, TruMedia, Wasserman Media Group and the Sydney Blue Sox. At Paul Swydan’s suggestion, I created word clouds to summarize these postings. These give a quick overview of what those jobs entail and the required qualifications. For those not familiar with the research and data science side of baseball, I’ll explain a few of the software tools which are prominent in the job postings and can be found in the word cloud.

To make the word cloud, I collected all the pieces we’ve published since January 2015 that contained “Job Posting” in the title. I separated the text content of each post into two different categories: job description and qualifications. From there, I took those two documents into R and used the tm package to clean the text, removing punctuation and unnecessary words like articles and prepositions. The package also tabulated the words. Additionally, I removed some other words like baseball, experience and strong. These words occurred frequently in the posts, but they were either obvious or not helpful. Then with the processed text data, I constructed the graphic using the aptly named wordcloud package. If you are unfamiliar with word clouds, larger words indicate that the specific word was found more often in the job postings.

Job Posting Descriptions

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Win Probability Added Leaders Through the LDS

During the postseason, Win Expectancy charts become ubiquitous, because each play, misplay, decision and comeback is magnified in its importance in front of a national TV audience. While Win Expectancy (WE) and Win Probability Added (WPA) aren’t great stats to evaluate players, they are a tool to understand how the dynamics of how a game changes from the first pitch to the last out.

For those not all too familiar with Win Expectancy, our library has a good entry and the interpretation can be boiled down to

If a team is losing and has a 24% win expectancy, only 24% of teams in similar situations in the past have ever come back to win.

So using historical data and the current inning, score, outs and runners on base, WE tells you what percentage of teams have won given those circumstances. These numbers aren’t a prognostication, since anything can still happen, but they give an estimate of what you might expect from the situation.

Win Probability Added is derived from Win Expectancy — being the difference from one play to the next. For example, The batter/runner is given credit for a hit, while the pitcher on the mound will be debited an equal amount for that hit. Plays that dramatically swing the score late in the game with two outs in the inning generally have the highest WPA. WPA is written out like batting average (.000), but it should be interpreted in the same way as win expectancy (0.0%). A play with a .360 WPA increases the WE +36.0%.

Below is our standard WE chart combined with the signed* WPA chart. The WE chart is the running total of the WPA chart. The top chart shows the sum of all the plays until a certain point in the game, and the bottom chart shows the change in WE for each play, which is also the signed WPA.
Royals-Astros Game Graph

Top Players


Now with the basics out of the way, we can make some WPA leaderboards for this postseason. First, batters through the end of the LDS.

LDS Postseason Batter WPA

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The Use and Frequency of Emoji in MLB Twitter Engagement

As I have done a few times before, I’m going to present Twitter analytics for Major League Baseball team twitter accounts concerning fan engagement. In the initial off-season analysis, the Mariners had the most fan engagement over the off-season. In May, the Cubs blew all the other teams away by responding to fans, and the Yankees scored at the bottom both times.

This post will expand upon the original engagement metrics (retweets, replies, media and favorites) and add emoji metrics. I’ve addressed emojis before, albeit briefly in regard to which emojis different fanbases used, but this analysis will look specifically at team’s social media accounts.

The interaction metrics, replies, retweets and favorites, measure how often the team interacts with fans. A reply requires the most time and retweets are a form of endorsement. These both create more engagement than a favorite. The inclusion of media and emoji does not denote a personal interaction, but they communicate in a different way than text does. Images and video can show behind-the-scenes actions, lineup cards, or highlights. Emoji, while sometimes criticized for being silly, are continually changing digital media, facilitating the communication of emotion. The fire emoji in particular is use to denote “hot” players, strikeouts or outstanding plays.

The tweets used in this analysis were collected from June 15, 2015 to the All-Star break (July 16, 2015). I detailed the original collections methods the first post. I added to these methods by counting the number of tweets that contain emojis and denoted if there was a fire emoji used. I omitted any retweets from both the emojis metrics in order to capture the emoji use of each specific team. The general emoji metric is a count of tweets with any emojis in it, and the fire emoji metric is the count of tweets with a fire emoji present.

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