The 2020, 2021, and 2022 MLB pennant race charts using Retrosheet data have been updated on the Exploratory Server: https://exploratory.io/dashboard/kc2519/Pennant-Races-1901-current-aUu5vDT1EW. All seasons from 1901-2022 are now available using the simple parameter selection (just make sure it’s set to the interactive mode).
Here’s a screenshot:
Meanwhile, I’m struggling with some JSON output for my traditional version, so no updates there yet.
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One of the primary source data sets I use to create baseball visualizations is the amazingly detailed information captured by the Retrosheet project, a dedicated group of volunteers providing play-by-play and game level information for each MLB season. They have recently passed the 100-year milestone, with data from the 1921 & 1922 seasons now available. I have some catching up to do on the older seasons, but just downloaded the 2022 season for adding to my databases.
The data comes in two distinct sets – game logs being the much easier of the two to work with, due to the smaller data size. Each game played in a season is captured at a summary level (~ 2,400 records), with information pertaining to the score, players, umpires, attendance, and much more. This information is used to feed my game summary visualizations:
As you can see, these are bite-sized summaries of every game, showing some of the important summary data for a game. They can be filtered to find specific teams, pitchers, scores, and much more. These visualizations are currently available covering the 1955-2019 seasons; one of my immediate goals is to add the 2020, 2021, and 2022 seasons, before starting to work in reverse with pre-1955 campaigns.
Fortunately, I have lots of SQL code built up over the years to make the data update process fairly simple; the 2022 game logs have already been added, and now I’ll get to work on the play-by-play data. Stay tuned for updates, and thanks for reading!
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The final 10 MLB WAR Trade Networks have now been published, bringing the total number of graphs to 31 – 30 teams and one overall network with all teams and transactions. For more information on the trade networks, click here. Here are the remaining networks:
Find your favorite teams and enjoy!
I’ve added 11 new MLB WAR Trade Networks to the site, bringing us to 21 in all. One more round of updates should get us to the full complement of graphs. For more information on using the graphs, see here.
Here are the new adds:
All the networks can be found here. Enjoy!by 
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The first 10 WAR (Wins Above Replacement) Trade Networks are now available for exploring! This initial group includes nine team networks and one overall graph with all teams included. Here’s a list of the 10 graphs:
Each of these and any upcoming WAR trade networks can be found on this page.
Let’s walk through how the graphs work, using the Detroit Tigers network as an example. We’ll begin with an anatomy of the graph display:
As the image shows, the primary focus will be the main graph area in the center of the window. This is where all nodes (transactions, teams, and players) will reside, connected by edges based on common relationships. Transaction nodes will vary in size based on the total value of a trade with the largest nodes indicating a trade that created significant future WAR for one or both teams. Team and player nodes are set to constant sizes so that the initial visual focus will be on the transaction nodes. The size differences become more noticeable when we zoom in to the network. More on that shortly.
Edges are also sized based on WAR value; this is where we see the value provided to a team and by specific players. Edge sizes (weights) will be more easily seen when we zoom in to the network.
On the left are some graph controls to assist in navigating the graph. We can zoom in using the slider control or the plus/minus buttons adjacent to the slider. Zooming can also be done with a mouse scroll if you prefer that option. The fisheye lens can be toggled on or off and can be used to highlight certain areas of the graph by hovering over a selected region. Finally, the edges button will enable showing or hiding edges and connected nodes. This is useful when you wish to reduce surrounding nodes and focus on specific transactions. We can also pan the graph by dragging it using a mouse – this is helpful in centering a network or viewing specific regions of the graph.
At the upper left of the window is a color legend for each node type, and hidden on the left (not shown in our image) is an information pane that will show specifics about the network. More on that in a bit.
Now let’s examine the information window – this is what makes the network truly powerful. When the network is first displayed or the browser window is refreshed the information pane displays information about the graph (open it by clicking on the arrows icon at the top left):
You can see the simple overview of the graph, the source data, and what it aims to accomplish. Here’s an enlarged version for easier reading:
If we zoom in and select a specific transaction the pane displays the relevant details for that selection:
Now we have the details for the transaction – the season, teams, and players involved. Here’s the enlarged view:
You can do this for any transaction in a graph, or you could choose to select a team or player to see how they fit into the network. The possibilities are nearly endless and it’s a fun way to understand the relationships between teams, players, and trades.
We’ll do more exploring of the networks in upcoming posts; I’ll also be adding more teams until we have a complete set of trade networks. In the meantime, feel free to explore the graphs to learn more about the best (and worst) trades your favorite team has made over the last 120 years. Enjoy, and thanks for reading!
Happy day! Just finished uploading the 2021 baseball dataset from the Lahman baseball archive and Baseball-Databank, just in time for the 2022 season. Next step is inserting and updating the existing tables (with data back to 1901!) with the 2021 season stats. I can then move on to the fun side of the equation – updating existing visualizations and creating some new analyses and visuals. Stay tuned!by by
I for one am looking forward to 2022 after a couple of interesting, often challenging years affected my desire to generate interesting analytics and data visualizations. The less said the better – simply excited to get back to updating some existing visuals and adding a host of new ones.
I’ll be doing a lot of work using the Exploratory toolkit which keeps improving by the day. It is simply a great tool for handling large (or small) data sets from start to finish; I especially love it’s data wrangling capabilities.
On the data source side, Retrosheet and the Lahman database will continue to feed my analysis and visuals; none of what I create would be possible without these great resources. Retrosheet data (used for game level and play level detail) is already updated through the 2021 season; part of this year’s plan is to add older years (pre-1955) to my local database. The Lahman data (season level) is typically available around February and I’ll be downloading it to my databases at that time.
Stay tuned for updates throughout 2022 – they should be a lot more frequent than the last two years. Happy New Year!
A few years back, I created network graphs for many MLB franchises, using data from the Lahman Baseball Database. These graphs displayed the connections between teammates throughout the history of a given franchise from 1901-2013. Any players who were on a roster within the same season (or seasons) were connected to one another, with each node in the network representing a single player. These were then sized to reflect the number of seasons played with that franchise. Every graph was customized by using one or more of their official team colors, resulting in a visualization like this:
A full roster of the live versions can be found here.
Now that 2019 data is available, I thought it was about time to update the graphs, but this time with a new look that might make the graphs a bit more intuitive and perhaps even more visually attractive. Out of this was born the radial axis franchise graph, as shown for the 1901-2019 Detroit Tigers:
I’m pretty excited about the look the Radial Axis layout provides for this sort of data, and I think you’ll see why it is an effective method for visualizing all the players over the course of 119 seasons. Let’s have a look at the anatomy of this graph.
The graph runs in a counter-clockwise manner, starting with 1901 at the bottom of the graph, and working all the way around until we get to 2019, also at the bottom of the graph. Each set of nodes along the way represents the collection of players with their first Tigers season in that radian. We can see some years where there were many new players (1912 has an exceptional number), while other years had very few new players, 1915 being an example. Here’s a general diagram to help with this concept:
We can also identify a handful of players with especially large nodes, which indicate the number of seasons with the Tigers. These are sorted to make the graph clean and easy to interpret; players with the most seasons will all be closest to the center of the graph, with teammates from the same starting year sorted from longest to shortest tenure. The perimeter of the graph will be populated almost exclusively with one-season players.
For context, let’s examine a few of the large nodes, and identify who they are:
I hope this is starting to make sense. Each radian represents a season, and each node on a radian depicts a player, sized by their longevity with the team. The third critical aspect of the graph is the connectivity between players, represented by the thin gray lines running between them. These are called edges, and are at the heart of a network graph. Let’s have a closer look at the edges for Alan Trammell, as one example.
If we click on the Alan Trammell node, the graph is reduced to him and the players he played with over his career – or at least those who were on the roster in those seasons. This is the fun part of the graphs, as it facilitates exploration and pattern discovery. Here is a portion of the Trammell network, zoomed in so we can see the connections:
Now the edges are a bit more visible, and the graph detail begins to reveal itself. Notice the multiple large nodes in line behind Trammell; it turns out that this is the celebrated 1977 class, many of whom would ultimately be members of the great 1984 World Series champs. So while the 1977 Tigers were not a good team, they were beginning to see the fruits of a strong minor league pipeline. In order, here are the players in that group, and their connection to the 1984 team:
Trammell is obviously connected to many other players who started in different seasons, given his 20 years with the team. In fact, he has a degree measure of 333, representing the number of players with a connection to Trammell. Thus, we can also see many connections to players who started their Tigers journey in other seasons. The three large nodes to the upper right of Trammell are interesting – who are they?
What we have here are the three remaining holdovers from the 1968 World Series champs, finishing up their careers just as Trammell was starting his long tenure with the Tigers. Discovering patterns like this make network graphs very interesting for me, and I hope you will also find them interesting. I’m currently in the process of refining the Tigers graph, which will be followed by graphs for each MLB franchise, once again using their team colors to provide some visual context. Hope you found this informative, and we’ll see you soon.by 
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Welcome to Visual-baseball.com! I’ve just completed an update where data for the 2019 season is now part of the Batting Explorer 2010-2019 interactive visualization. This is a tool where we have every batter in a given season depicted via a baseball card type of framework, showing their key stats for the season, as well as the positions played in the field (by number of games), using a visual of a baseball diamond. It’s a highly interactive way to filter through multiple seasons worth of data by team, player, position, and more.
So while you’re waiting for on the field action to start, have a look at the Batting Explorer to answer some of the questions on your baseball mind. For example, here’s a quick look at all of the left fielders who played at least 110 games at the position in 2019 (by using the filters pill at the top left):
We can take the same results and sort it by the number of home runs, to see who the power hitters in the group were for 2019, by sorting from high to low:
Now we see Kyle Schwarber and Juan Soto at the top of the display. Let’s look at Schwarber’s details by simply hovering over his card:
We now have a pop-up within Schwarber’s card telling a mini-story about his batting stats for the 2019 season. Here’s a closer look:
From this, we learn that Schwarber hit 38 home runs, batted .250, and had an OPS (on-Base + Slugging) of .871, as well as multiple other details. Additionally, you likely noticed the “View the full stats at Baseball-Reference.com” pop-up tag. To get there, simply click on Schwarber’s card, and you’ll be transported (in a new tab) to his page at Baseball-Reference:
Pretty cool, right? Give it a try, or pick your own filters, look at specific teams and seasons, and so on. Here’s the Batting Explorer page, with every decade back to 1900-1910 available for your curiosity.
More updates to come soon on the 2019 data, and thanks for reading!by 
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I’m pleased to share that the Batting Explorer visualization for the 2010s decade has now been updated with 2016 & 2017 statistics. This ongoing project captures batting statistics at the season level for every major league batter, and visualizes them in a baseball card type of format, as seen below:
A number of filters are provided to make it easy to browse across a wide range of attributes, including all of the major batting categories:
One of my favorite aspects of the Batting Explorer is the ability to link to greater detail by clicking on a specific player card, which will transport you to the Baseball-Reference page for that player:
This project uses the Exhibit project software originally developed years ago as part of the MIT Simile project, as well as a lot of HTML & CSS for styling purposes. Give it a try, and thanks for reading.by 
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