Trade Deadline Season

Ah yes, the most wonderful time of the year; at least for baseball fans. Trade deadline season is here and the rumors are swirling, the trade winds are picking up and the grass always seems greener on the other side of the trade market. As the deadline approaches, front offices are searching for the final pieces that could help their teams blossom into October contenders. Meanwhile, fans are continuously refreshing their feeds to see which players may soon be planted someplace new.

Expect the Unexpected

However, unlike the changing of seasons, there is nothing predictable about trade deadline week. All it takes is one injury, one market shifting trade or one losing streak to alter a team’s trade deadline approach. This is why I don’t spend much time reading the latest articles about who will be traded where or what players are the best fits for contenders. Simply put, there are too many ever-evolving factors to predict exactly what will unfold. Sure, it’s fun to speculate and join the conversation, but the real intrigue lies in everything teams must weigh before a deal crosses the finish line.

Not So Simple

The uncertainty is what makes deadline decisions much more complex than the simple “buyer or seller” labels often attached to teams this time of year. It seems quite surface level, Team A is a contender with a glaring hole at shortstop, while Team B is 10 games under .500 with a star shortstop. Obvious trade partners, right? Not necessarily. First of all, Team A has to decide whether the shortstop is a significant enough upgrade to justify the acquisition cost, while Team B has to determine whether trading him brings back more long-term value than keeping him. There are many factors to consider, including his current contract, remaining years of control and how he fits into each team’s plans, both for the present and the future. Beyond those internal considerations, there are external forces shaping the market. If the player is in high demand, it may create a bidding war, leading to a massive overpay in terms of overall value. That deal can then influence the rest of the market, as other “sellers” use Team B’s return as a new benchmark and raise their own asking prices.

Setting the Threshold

This is where the deadline becomes less about identifying the right player and more about determining what that player is actually worth within a constantly shifting market. The same upgrade may feel reasonable at one price and reckless at another, especially when teams are weighing a short-term postseason push against the long-term value of the players they would have to give up. Every front office has a different threshold for when an opportunity becomes worth the risk, which is why two teams in similar positions can approach the deadline in completely different ways. The point at which a move becomes worth the risk depends not only on the players involved, but also on where each organization stands in their competitive cycle and how much prospect depth they can afford to sacrifice.

The Unpredictability Factor

Even after teams establish that threshold, baseball has a way of disrupting the most carefully constructed deadline plans. The Marlins recently went from holding a three-game advantage in the National League Wild Card race to enduring a franchise-record 12-game losing streak, creating an entirely different outlook as the deadline approaches. Shea Langeliers offers another example. After appearing in trade discussions as a potential catching upgrade, Langeliers suffered a torn meniscus that required season-ending surgery, removing one of the market’s best possible options altogether. These developments are impossible to fully account for in advance, yet they can instantly change who is available, what a team needs and how aggressively a front office is prepared to act. Deadline planning may involve months of evaluation, but the final approach can still be rewritten by what happens over the course of a single game or one disastrous stretch of the season.

Before the Clock Strikes Six

This is ultimately what makes the trade deadline so compelling. Completed deals are eventually judged by the players exchanged and the results that follow, even though those evaluations rarely capture how uncertain the situation was when the decisions were made. Front offices may spend months studying the market and preparing for different possibilities, only for the direction of the deadline to shift within a matter of days. At the deadline, the strongest decisions are often the ones that make sense in the moment while leaving enough flexibility to respond when the situation changes. Here’s to seeing which plans hold up, which ones change and how everything plays out before the clock strikes 6:00 p.m. on Monday.

Jordan Walker

The next analysis in the Tool Dynamics series felt like an obvious choice. After being named an All-Star and putting on a historic performance in the Home Run Derby this week, it feels like the perfect time to dive into Jordan Walker’s slugging performance this season. It is not exactly a secret, especially not anymore, that Walker is having a breakout season after not fully living up to expectations in prior seasons. I have worked a good number of Cardinals games this season and I have personally come to appreciate his overall approach to the game. He is calm, collected and rather reminiscent of an old-school ballplayer, all while displaying a quiet confidence. Generally speaking, that type of steady approach will set a player up for sustained success in a game that is so volatile. Now, let’s take a closer look at how Walker’s calm, steady approach has translated into his consistency and adjustment trends throughout the first half of the season.

Results

With the first half of the season complete, we are left with a 97-game sample from the beginning of the season on 3/27 up until the final game before the All-Star Break on 7/12, where he has set a baseline SLG of .506 for himself. That is already an impressive baseline to work with, but let’s look at how that translates into consistency and adjustment. His consistency score comes in at 0.69, placing him at the top of the moderate range, while his adjustment score of 0.59 also falls within the moderate range. These scores tell the story of a player whose overall production has become increasingly stable, even if the path to that production has not always been perfectly smooth. Walker has experienced the natural game-to-game swings that come with a power-driven stat like slugging percentage, but his performance has not been defined by those fluctuations. Instead, he has continued to return to a strong level of production over the larger sample, which is an encouraging sign for a player putting together a breakout season.

Consistency Trend

Taking a closer look at the consistency trend, the visual aligns closely with what the score is indicating. Walker begins the sample around 0.50 before gradually climbing into the upper portion of the moderate range, where he remains for most of the first half. There are a few brief dips along the way, but none turn into an extended slump. Instead, the trend repeatedly settles back into a relatively controlled range between roughly 0.60 and 0.70. That stability is especially encouraging when evaluating slugging percentage, a stat that can change quickly depending on how often a hitter drives the ball for extra bases. Walker has naturally gone through games without much damage at the plate, but those quieter performances have not caused the broader trend to unravel. He has continued to return to his power production rather than allowing a short cold stretch to become something more prolonged. By the end of the sample, his consistency score sits at 0.69, right on the edge of the high range, suggesting that the extra-base impact behind his breakout is becoming a more dependable part of his offensive profile.

Adjustment Trend

The adjustment trend provides a clearer look at how Walker has responded when his slugging production has dipped. The line moves sharply throughout the sample, which shows that his ability to adjust has not followed one steady path. There are games where he evidently responds quickly, but there are also stretches where the recovery takes longer. That unevenness is what keeps his overall adjustment score at 0.59 in the moderate range. What stands out, however, is that Walker rarely remains at the bottom of the trend for long. Several of the sharp declines are followed almost immediately by rebounds, suggesting that he has generally been able to identify when his timing or quality of contact is off and make the necessary corrections. For a power hitter, that may mean getting back on time against velocity, improving his pitch selection or making the necessary mechanical adjustments to consistently produce hard contact. The trend does not suggest that Walker has completely solved every adjustment pitchers have presented him with, but it does show a hitter who has repeatedly found ways to respond. That is an important distinction, as adjustment is not about avoiding every cold game or difficult matchup. Rather, it is about recognizing what opponents are doing, preventing a poor stretch from carrying into an extended stretch and finding a way to consistently produce productive contact. Walker’s first half reflects that process and shows an encouraging ability to make corrections as the season progresses into the second half.

Getting Back Into Rhythm

I’ve been thinking a lot lately about what it means to get back into a rhythm. I’m someone who functions best when I’m in the flow of things, but the past few months have made that harder to maintain. Between my full-time role as a Baseball Data Scout with Sports Info Solutions, finishing my accelerated MBA, working an unconventional schedule, and everything else that comes in between, writing for Diamond Dynamics unfortunately had to take a back seat for a bit. There’s been a lot along the way I wanted to write about and analyze, but sometimes getting back into the rhythm of something takes a little time. I recently wrapped up my MBA, which is something I am extremely proud of, and now that I have a little more room to breathe, I am excited to start building Diamond Dynamics back into my routine.

The Rhythm of a Long Season

Without further ado, let’s get back into it. I chose to focus on rhythm in this article because it’s something that applies to baseball as a whole. There is a certain flow that comes with being locked in, and over the course of a long season, that flow can be hard to hold onto. This is especially relevant with the All-Star Break looming around the corner, as this part of the season naturally creates a shift in pace. The grind does not only affect the players on the field, but it echoes throughout an organization, from the people making decisions to the staff working behind the scenes, and that overall energy can eventually show up in the way a team plays.

There are many ways that rhythm, or lack thereof, shows up throughout the course of a season, but the All-Star Break is definitely the biggest disruptor for both teams and players alike. For some teams, it can come at the perfect time, giving them a chance to reset after a stretch where the games have started to feel heavier. For others, it can interrupt momentum right when the team was starting to gel. That same idea applies to players individually, because the break can mean something completely different depending on where they are in their own season. A few days away can be exactly what one player needs to physically and mentally reset, while another player may be trying to hold onto a rhythm he was finally starting to find.

Returning to Rhythm

Hunter Greene is a good example of how complicated rhythm can be when a player is returning at a rather awkward time in the season. Greene made his season debut for the Reds on July 4th after undergoing offseason elbow surgery, which makes for an inconvenient situation. Coming back after missing half the season is difficult enough, but doing so right before the season pauses again adds another layer to the challenge. His first start showed how difficult that can be, as he lasted just 3.1 innings, allowing eight earned runs on seven hits and four walks. That line is obviously not what he or the Reds wanted, but he did have seven strikeouts, which makes the outing more interesting than the final result alone. The swing-and-miss ability was clearly still there, which gives him something to build on, but the traffic on the bases showed how hard it can be to command a game when a pitcher is still trying to find his rhythm. However, since Greene is still scheduled to have one more start before the All-Star Break, it will give him two starts under his belt to evaluate and game plan for the second half of the season.

When Team Rhythm Gets Disrupted

From a team standpoint, the Yankees are a good example of how rhythm can shift before the All-Star Break. Their recent seven-game losing streak has shown how quickly a team can start to feel disconnected when multiple parts of the game are being adjusted at once, especially when injuries force the team to operate differently than they normally would. With Aaron Judge out, the impact is not only felt through the missing production in the lineup, but also through the way the roster has to be managed around his absence. Different players are asked to take on different roles, the lineup has to be pieced together differently, and defensive alignments can shift in ways that affect the overall flow of the game beyond simply who is playing where on the field. When players are moved around, the rhythm of the team changes with it, as factors such as communication, comfort, and responsibilities all become part of the adjustment. It becomes an entire process that has been disrupted, and when that happens during a rough stretch before the break, it can make it even harder for a team to feel fully connected on the field.

Rhythm Dynamics Moving Forward

There are many other examples I could give about how rhythm plays a role in the baseball season, which is why it is so important to have an understanding of how performance changes over time. Rhythm is not always something that shows up clearly in one stat line, but it can be felt in the way a player settles back in, the way a team responds to disruption, or the way a season starts to shift as the schedule wears on. With the All-Star Break right around the corner, that rhythm becomes even more important to watch, because the pause can either help teams and players reset or force them to rebuild the flow they were just starting to find. As the second half begins, the teams and players who adjust best may not simply be the ones with the strongest first-half numbers, but the ones who can find their rhythm again as the season keeps moving forward. As for me, I’m excited to keep moving forward with Diamond Dynamics and continue finding my own rhythm again along the way.

Gavin Williams

For the next breakdown in the Tool Dynamics series, I wanted to shift to the mound and analyze a pitcher using the Diamond Dynamics consistency and adjustment tool. When I asked around what stat people tend to look at first when evaluating a pitcher, the answer was pretty consistent: ERA. Now, ERA doesn’t always tell the full story, but honestly, that’s true with almost every stat, and the reality is it’s still widely used across the league when evaluating a pitcher’s performance.

After working two of Gavin Williams’ games for the Cleveland Guardians this season, including his 4/24 start against the Toronto Blue Jays, there was a natural curiosity to dive deeper into his consistency and adjustment trends. Speaking of, that outing stands out right away. He went 6 innings pitched, gave up 6 earned runs, but still walked away with the Win, thanks to his offense picking him up. From a results standpoint, it’s messy, but from a Diamond Dynamics standpoint, it’s exactly what we want to analyze. It provides a clear outlier and allows us to see firsthand how Gavin Williams responds to a not so favorable outing on his ERA, despite earning the Win.

Results

Across the 7-game sample from the beginning of the season 3/27 to his most recent outing on 4/29, Gavin Williams has set a baseline ERA of 2.983 for himself. This is a strong baseline to work with, but let’s look at how this translates to consistency and adjustment. His consistency score comes in at 0.59 and adjustment score at 0.57, both landing in the moderate range. Although that might not jump at first, this actually tells an important story as we’re not evaluating a volatile profile but one that reflects general stability. Even with the 4/24 outing included, his performance isn’t bouncing all over the place. It’s relatively controlled across the sample, which is exactly what you want from a frontline starter over time. One bad outing here and there does not define a pitcher. It is his response to those outings that will ultimately define him.

Consistency Trend

Taking a closer look at the consistency trend, the visual trend aligns almost perfectly with what the score is indicating. The trendline remains relatively stable across the sample with only slight upward movement, displaying a moderate range throughout. Ultimately, this reflects a pitcher who is putting out a consistent version of himself most of the time when he takes the mound. There aren’t any major swings in performance from start to start and even when results fluctuate, they’re not coming from a place of volatility. What stands out most is how that 4/24 outing is handled within the trend. A 6 earned run start could easily create a sharp disruption, but instead it gets absorbed into the larger sample without causing a major drop. That’s usually a strong indicator that the outing was more of an outlier than a shift in overall performance, reinforcing the idea that Williams has been operating within a controlled and steady range overall, hence the moderate adjustment score.

Adjustment Trend

Now this is where that 4/24 outing really comes into play, as the adjustment trend shows a clear dip immediately following that start, which is exactly what you’d expect after giving up 6 earned runs. Something was clearly off that day, whether it was execution, command, pitch mix, or simply just great execution from the Blue Jays hitters. However, what matters most isn’t the dip itself, it’s how he responded to it, and that response showed up right away in the next outing. On 4/29 against the Rays, Williams pitched 7.2 innings and allowed 0 earned runs, which is about as strong of a response as one can ask for, and you see that reflected in the sharp jump back up in the adjustment trend. This tells us he didn’t overcorrect or let that outing carry over, but instead made the necessary adjustments and got right back to his baseline. This is exactly what you want to see from a frontline starter, not constant fluctuation, but the ability to recognize when something goes wrong and clean it up immediately. This is what ultimately kept Williams in that moderate adjustment range as he clearly showed consistency prior to that poor start. It is also a reflection of the Diamond Dynamics tool not overly penalizing players for occasional poor performances because the reality is that it will happen every now and then in a 162-game season. It is about the bigger picture of analyzing how each game adds up to provide us with an accurate analysis of how a player evolves as the season progresses.

Ben Rice

My first analysis using the Diamond Dynamics Tool to evaluate consistency and adjustment felt like a natural choice: Ben Rice. I may receive a text tomorrow that this was due to my Yankees bias (you know who you are) but anybody who has been keeping up with the MLB knows Ben Rice is putting up incredible numbers to start the season. Now this may come as a surprise to some baseball fans, or even a “fluke” but anyone who follows the Yankees knows this does not come as a surprise and it is certainly not a fluke. One look at his Baseball Savant page will show you exactly why he is currently leading the league with a 1.215 OPS. Red, red, red and more red. Exactly what an organization wants to see from their player’s Baseball Savant page as underlying metrics tend to go hand in hand with results. Wait let’s stop right there. Results?

Results

I used the Diamond Dynamics Tool to measure Ben Rice’s results, and the analysis did not come as a surprise. The analysis focuses on a 21-game sample, from 3/21 to 4/19. Setting his baseline at a whopping 1.158 over that span, it is safe to say he has set the bar high for himself. His consistency score comes in at 0.77 demonstrating a high level of consistency and in turn a stable adjustment score of 0.49. This analysis is a perfect example of how high consistency will often correlate with stable adjustment, and that’s mostly because there is simply less volatility present across the sample. With performance staying within a tighter range relative to such an elevated baseline, there are fewer deviations that would require a corrective response.

Consistency Trend

Now, let’s take a closer look at how the consistency score appears visually as a trend, showing the development from a game-by-game standpoint. As the note says below “The player’s consistency trend shows gradual improvement over the sample”, and that is an accurate statement. The sample starts off displaying a relatively moderate consistency trend, but the trend begins to shift upwards approximately around the first 3-5 games of the season, reaching a high level of consistency on 4/10 and that level has only ticked upwards ever since. So, what exactly do these levels mean? Essentially, they are telling you that Ben Rice has settled in and found his swing. Early in the sample, that moderate consistency trend reflected a hitter who was still getting his timing down, seeing different pitch mixes, and not quite locked into his approach yet. Once that trend started climbing after those first few games, that’s when you’d say he started to barrel the ball more consistently, controlling the zone better, and started putting together more competitive at-bats. By the time you hit that 4/10 mark where consistency reached a high level, it’s basically the equivalent of a hitter being “locked in.” He didn’t just start getting results, he started having quality plate appearances almost every game. The fact that it keeps trending upward from there means he’s maintaining that approach. He’s laying off bad pitches, getting into good counts, and consistently driving the ball rather than mixing in a lot of weak contact or off days. In other words, less streaky and more steady production, which is exactly what you want from a hitter over a stretch.

Adjustment Trend

We’ll do a quick analysis of Ben Rice’s adjustment trend but as noted earlier, his high consistency score doesn’t correlate very strongly with a need for a deep dive into his adjustment trend. That said, the early dip reflects what you’d expect from a hitter who’s still settling in, making small timing and approach tweaks as he faces live pitching. However once that stabilizes, the gradual climb tells you he was making the right in-game adjustments without overcorrecting. Simply put, he’s not chasing drastic changes, he’s just tightening things up, pitch by pitch and at-bat by at-bat. It also lines up with the consistency level we’ve already seen. Since he’s already producing a steady OPS, there isn’t a need for much adjustment. Instead, this trend shows controlled refinement, which is exactly what an organization wants to see. The stabilization here reinforces that he’s locked into an approach that works, and he’s sticking with it rather than constantly searching for answers.

Introduction: Tool Dynamics

I’m excited to introduce a new series within Diamond Dynamics called “Tool Dynamics”. This series will focus on player analyses using a new tool I recently created to track consistency and adjustment trends on a game-by-game basis. This idea was inspired by the concept of stability that I explored previously, but I wanted to expand on it by looking at how consistency develops in-season and how players make adjustments, especially during slumps or recovering from hot streaks.

Consistency Tool

The first part of the tool is focused on measuring how stable a player’s performance is over time. Simply put, consistency is about how much a player’s performance varies game-by-game. However, this tool goes a step further by also accounting for the level of that performance. A player who is consistently producing strong results should not be viewed the same way as a player who is consistently underperforming, even if both appear “consistent” on the surface. With that, the tool weighs both consistency and performance quality together. It looks at how far each game’s performance deviates from the player’s evolving baseline, while also adjusting for whether those performances are strong or weak relative to the rest of the sample size. It’s also important to note that this tool can be used to evaluate any rate-based stat, but unfortunately does not account for counting stats. Metrics such as OPS, OBP, SLG, ERA, or WHIP work because they evolve game by game, allowing performance trends to naturally develop over time. Counting stats don’t translate because they accumulate over time rather than fluctuate. Since they are always increasing, they don’t deviate in a way that reflects game-by-game performance. Stats such as Hits, HR’s, RBIs, or even defensive metrics like DRS will continue to build as more games are played, which makes it difficult to capture true consistency or variation. This is what makes the consistency tool meaningful in identifying true stability and change over time.

Adjustment Tool

The second part of the tool is focused on how a player responds as their performance changes over time. While consistency looks at stability, adjustment is more about response. How exactly is a player responding after facing adversity? Or even, how is a player responding after a long streak of productivity? Instead of looking at each game on its own, the tool compares each new performance to what the player had been doing up to that point. This means every game is being evaluated relative to the player’s recent levels. If a player goes through a slump and begins to recover, that response will show up in the adjustment score. On the other hand, if performance starts to decline after a strong stretch, that will be reflected as well. This allows the tool to capture how a player is responding in real time, not just what their results look like overall. Just like the Consistency Tool, adjustment is calculated on a game-by-game basis and evolves throughout the sample. This is what allows the tool to highlight when a player is actively adjusting rather than just maintaining a level of performance.

Features

Now that the main components of the tool have been laid out, let’s dive into the features that help define how the tool is used. In addition to the tool itself, there features “How It Works” and “Data Format” sections. The How It Works section is essential to understanding the step-by-step process of how to upload data into the tool, while the data format section provides a visual representation to help the user understand exactly how the CSV file should be structured for the tool to understand it. The tool requires a CSV file with either a Game or Date column, along with a single rate-based stat, chosen by the user. Once the data is uploaded, everything updates automatically as each game is processed, allowing the model to build and adjust in real time. In addition, there is a score guide and built-in insights included to help interpret results. The score guide provides a range for what qualifies as high, moderate, or low consistency and adjustment, giving context to how a player is performing within the sample. From there, the tool generates built-in insights that help bring the trends to life. Instead of just displaying scores and visuals, these insights provide context around what’s happening within the sample, whether that’s identifying shifts in performance, highlighting trends, or showing how a player is responding over time.

Access the Diamond Dynamics Tool

Overall, this tool is meant to provide more context around how performance is developing over time, not just where it ends up. By focusing on consistency and adjustment on a game-by-game basis, it offers a different way to look at player performance as a season unfolds.

Now that the Diamond Dynamics Tool has been established, player analyses using this tool will be coming soon!

If you want to explore the tool yourself, you can access it below:

https://diamonddynamicstool-g8un2yafdaotpa8qsu2csu.streamlit.app/

Where Decisions Replace Evaluations

As final pitches are being thrown at Spring Training, teams are moving on from evaluating and tackling the next stage, decision-making. Not just any decisions, as far as players, fans and front offices are concerned, but the ones that define who breaks camp and how a roster materializes heading into Opening Day. Now of course, there are the staples of the team who already have their spots locked in, but some of the most meaningful decisions come from the spots that aren’t already decided on. We all know Aaron Judge is the starting right fielder for the Yankees but it could be the no-name bullpen piece that barely scraped his way on to the roster that ends up making a real impact over the course of the season.

The Puzzle Behind the Roster

At this point, roster construction becomes less about what looks best on paper and more about what fits. The puzzle may still have missing pieces but decisions have to be made with the bigger picture in mind. It’s not just about identifying the best individual players, but about how each piece complements the rest of the roster. The final spots often come down to specific roles. A team might carry an extra bullpen arm over a position player for flexibility, or prioritize defensive versatility over offensive upside off the bench. Even decisions that may seem minor at the surface level can reflect a larger strategy at hand in how a team chooses to operate over a full season.

Decision-Making Through Uncertainty

There’s also a level of uncertainty built into all of this. Spring Training can influence decisions, but it rarely tells the full story. Performances come in a small sample size, competition isn’t always equal, and the classic Spring Training over-performance that doesn’t necessarily translate to the regular season. Teams have to weigh what they’re seeing in the moment alongside historical performance and what they expect over the long run. With that being said, these final decisions aren’t just about who performed best in camp, but about which players a team trusts to fill specific roles once the season begins.

More Than Just a Roster Spot

While this is a significant time for decision-making, it also carries a lot of meaning for the players involved. For many, it’s the result of years spent working towards a greater goal: to simply make a big league roster. For others, it’s trying to make the final spot on the roster of a very competitive team, where they may easily be a starter on another team. It’s all about where teams see value, but at the same time, these decisions come down to moments that players remember for the rest of their careers. Players finding out they made the roster isn’t just another transaction, it’s something that reflects everything it took to get there. Whether it’s a veteran holding onto a spot or a player hearing their name for the first time, those moments carry a different kind of weight. One of the best examples this spring was Skip Schumaker telling Rule 5 pick Carter Baumler he made the roster during a mound visit in the middle of a game. Nothing over the top, just a quick moment that meant everything to a player that worked hard to make it to this treasured moment in his career.

The Season Starts Here

Roster Dynamics isn’t just about who makes the team. It’s about how those decisions reflect a team’s approach to building a roster that can hold up over a full season. Every choice, even at the margins, plays a role in how a team manages depth, adjusts to change, and handles the unpredictability that is inevitable. Right now, those decisions are being finalized, and with that, the foundation for everything that follows. From here, it’s no longer just about projections and evaluation, it’s about results. Opening Day is up next, let’s get ready to play ball!

From Stability to Risk Dynamics

Leveraging off the concept of Stability Dynamics, which as a reminder focuses on how consistent a player’s performance is over time, the next question begs how that consistency translates into payroll decision-making in the front office. In the MLB, teams are not just evaluating performance, they are investing in it. Every dollar a team puts into their roster reflects a level of confidence in how that production will hold up, making payroll less about spending and more about how teams choose to manage uncertainty. This is where Risk Dynamics comes into play. By connecting performance stability to payroll, this process looks at how teams distribute financial resources across players with varying levels of consistency. Rather than only asking how much value a player produces, it shifts the focus towards how reliable that value is relative to cost.

Applying Stability to Payroll

To make sense of this concept, I created a scatter plot using an 8 player sample to visualize the relationship between payroll and performance, with stability tiers used to highlight differences in consistency. In alignment with Stability Dynamics, this analysis uses the same 2021–2025 time period to evaluate performance over time, rather than in a single season. Stability is measured using WAR range, which captures the difference between a player’s highest and lowest WAR during that stretch and serves as a proxy for consistency. On the financial side, average salary is used to reflect how teams commit to players over multiple seasons instead of just evaluating a single season. Keeping both performance and payroll within the same time period allows for a more accurate comparison of consistency relative to cost. Looking at the data this way adds another layer to the conversation. Two players may produce similar overall value, but the path they take to get there can look very different. By incorporating stability tiers, the visualization helps highlight those differences and provides more context behind how that value is actually generated. With that context in place, the visualization becomes easier to interpret. It should be noted that players with similar overall value can reach it in very different ways, which becomes an important distinction when evaluating performance relative to cost.

What the Data Shows

Now let’s break down the results seen in the scatter plot. One of the first things that stands out is that higher payroll does not necessarily translate to lower risk. For example, Aaron Judge and Mookie Betts are among the highest-paid players in the sample, but also display some of the greatest volatility in performance. In contrast, players such as Jose Altuve and Xander Bogaerts show more stability while sitting at slightly lower to the same average salary, suggesting that stability is not limited to the highest levels of spending. Meanwhile, the moderate tier tends to show less extreme swings in performance, pointing to a more controlled level of risk. This suggests that some level of consistency can be achieved without needing to operate at the very top of the payroll scale.

Where Risk Meets Decisions

Ultimately, this reshapes payroll as more than just a measure of spending. It becomes a reflection of how teams choose to balance production with variability. Whether targeting consistency or embracing volatility, each decision carries a different level of risk, making roster construction just as much about risk management as it is about talent evaluation. In that sense, payroll strategy becomes a direct reflection of how a team chooses to manage uncertainty, and ultimately where they’re willing to take a risk.

Attached below is the interactive visualization on Tableau Public:

https://public.tableau.com/app/profile/grace.maretsky6140/viz/RiskDynamics/RiskDynamics

The Inspiration Behind Stability Dynamics

Ideas in baseball analytics often come from conversations and shared perspectives, and sometimes all it takes is one discussion to spark a new idea worth exploring. This past weekend, I had the opportunity to attend the SABR Analytics Conference as part of the MLB sponsored cohort. The conference included front office staff such as analysts, as well as independent researchers who shared ideas about how performance in baseball is evaluated. After listening to different perspectives from people across the industry, it sparked an idea I wanted to explore further. Baseball is an industry where it’s difficult to be the first one to think of an idea, but it never hurts to conduct your own research anyway.

Stability Dynamics Defined and the Data Behind the Analysis

I decided to explore a topic that I call “Stability Dynamics”, which focuses on how consistent a hitter’s performance is over time rather than only evaluating their overall production. For this study, I chose to evaluate the following four common metrics: WAR, OBP, SLG, and OPS. Each of these metrics captures a different type of value, ranging from overall contribution to more specific measures of reaching base and generating power. While these stats are commonly used to measure a player’s performance, they are typically viewed through season averages or total production.

To build the dataset, I pulled player statistics using Stathead through Baseball-Reference. The analysis focuses on the 2021–2025 seasons, which provides a recent window of performance while still allowing multiple years of data to evaluate consistency. By looking across this five-season stretch, the goal is to better understand which hitters maintain steady offensive production and which players experience more noticeable fluctuations in their results over time.

Stability Dynamics: Hitter Performance Consistency Dashboard (2021-2025)

Above, you will see the dashboard I created to showcase my analysis as a whole. While it may be overwhelming initially, I’m here to break it all down. The goal is that you will understand the dashboard in its entirety once you reach the end of this write-up. As you can see, I tried my best to keep the Diamond Dynamics theme as consistent as possible. If you’re going to analyze stability, the visuals might as well be stable too. Although, there might be some fluctuation in consistency due to different color schemes available in Tableau. I guess you can say I’m no different than an MLB player in terms of volatility.

WAR Stability

With that out of the way, let’s dive into how WAR varies across these hitters and see which players show the most consistency in production over the study period. To measure WAR stability, I calculated the range of WAR for each hitter from 2021–2025 by taking each player’s highest WAR season and subtracting their lowest WAR season. In simple terms, the calculation shows how much a player’s value fluctuated across the study period. A smaller range indicates more consistent production, while a larger range suggests greater variation from season to season. To make the results easier to interpret, players were grouped into three stability tiers: Stable (≤1 WAR range), Moderate (≤3), and Volatile (>3). This allows us to quickly see which hitters maintained steady value over time and which experienced larger swings in consistent performance.

Now, it is important to note there are many factors that need to be taken into consideration as I only have so much data available. For example, it is certainly possible that a player displayed their lowest WAR value due to a shortened season from injury. As a matter of fact, that can be seen with Aaron Judge who came in as the most volatile. However, context matters and it should be noted that Aaron Judge produced a 4.6 WAR in 2023, largely due to a toe injury that kept him sidelined the last 42 games of the season. Remember, WAR is cumulative, so it is natural to assume Aaron Judge would’ve ended with a drastically higher WAR that season if not for the Dodgers outfield wall. Meanwhile, his highest WAR comes in at 10.8, hence the extreme volatility. So how do we account for factors such as injuries or missed time when evaluating WAR stability? The key is to recognize that this analysis measures observed production, not necessarily a player’s true talent level. Stability Dynamics is meant to highlight how much a player’s recorded value fluctuated across seasons, while the surrounding context helps explain why those fluctuations occurred. In other words, the metric identifies the variability, but interpretation requires understanding the circumstances behind the numbers. If you’re a true stat analyzer like me, you enjoy putting context with the numbers. Numbers are only part of the story, context helps explain why numbers look the way they do.

OBP Stability

To analyze OBP stability, I used the same approach as the WAR calculation by measuring the range of each hitter’s OBP from 2021–2025. This was calculated by taking the maximum OBP value for each player and subtracting their minimum OBP value across the study period. This allows us to see how much a player’s ability to reach base fluctuated from season to season. A smaller OBP range suggests a more consistent on-base profile, while a larger range indicates greater variability in plate discipline or offensive performance.

Looking at the chart, several hitters such as Aaron Judge and Mookie Betts appear toward the higher end of the range, meaning their OBP values fluctuated more across seasons. This does not necessarily mean they were inconsistent hitters, but rather that their offensive production reached both very high peaks and lower points during the study period. On the other end of the spectrum, players such as Bryce Harper and Andrés Giménez show smaller OBP ranges, suggesting their ability to get on base remained relatively steady across multiple seasons. Similar to the WAR analysis, this metric highlights variability in performance while the surrounding context helps explain why those changes occurred. However, unlike WAR, OBP is not a cumulative stat. Sure, it can be skewed if a hitter played in more or less games due to difference in reps but overall, it provides a fairly clear view of a hitter’s ability to reach base. Instead, we can draw conclusions from OBP stability to figure out how consistent a player is with factors such as plate discipline, contact ability, and overall approach at the plate, all of which contribute to a hitter’s ability to reach base consistently.

SLG Stability

For the SLG portion of the analysis, I wanted to examine power consistency across the study period. Instead of calculating a single range metric like the previous sections, this visualization shows each player’s minimum SLG, average SLG, and maximum SLG over that span. These values were calculated using fixed level-of-detail (LOD) expressions to isolate each hitter, identifying the lowest SLG season, highest SLG season, and the average SLG across all seasons in the dataset. Visually, the bars represent the range between a player’s minimum and maximum SLG, while the point marker highlights their average slugging percentage during the period.

This approach allows us to see not only how much a hitter’s power production fluctuates, but also where their typical power level sits within that range. Players such as Aaron Judge and Corey Seager show wider ranges, which reflects the natural volatility that can come with elite power production across multiple seasons. Meanwhile, hitters whose minimum and maximum SLG values sit closer together demonstrate a more stable power profile, indicating that their extra-base production remained relatively consistent from year to year. Since slugging percentage is tied closely to power output, it is expected to show slightly more variation than OBP, but examining the spread between these values still provides a useful look at which hitters consistently do damage at the plate.

OPS Stability

For the final metric, I examined OPS stability, which combines the two previous components of offensive production: On-base percentage (OBP) Plus Slugging percentage (SLG). OPS provides a quick way to evaluate a hitter’s overall offensive profile by capturing both how often a player reaches base and how much power they generate when they do make contact. Since it blends these two elements, OPS serves as a highly used modern-day stat to evaluate overall hitting performance.

To measure stability, I calculated each player’s average OPS from 2021–2025 along with their OPS range, which represents the difference between their highest and lowest OPS during that span. This allows the visualization to show both performance level and consistency at the same time. On the chart, the horizontal axis represents a player’s average OPS, while the vertical axis represents how much that value fluctuated across seasons. Players who appear lower on the chart have smaller OPS ranges, meaning their offensive production remained more consistent year to year.

To highlight the most consistently elite hitters, I created an “Elite Stable” classification, defined as players with an average OPS of .850 or higher and an OPS range of .08 or less. This threshold identifies hitters who not only produce strong offensive numbers but also maintain that production consistently across seasons. Players such as José Ramírez, Yordan Alvarez, Austin Riley, Rafael Devers, and Corbin Carroll fall into this category, demonstrating a combination of high-level performance and relatively stable offensive output. In the context of Stability Dynamics, these hitters represent the ideal blend of production and consistency, showing that their impact at the plate remains dependable from season to season.

Stability Dynamics Conclusion

Before wrapping up our Stability Dynamics study, it is important to reiterate this key principle: context always matters when analyzing data. Numbers provide a strong foundation for evaluating performance, but they do not always capture the full story on their own. Factors such as injuries, playing time, lineup roles, ballpark environments, and natural player development can all influence statistical outcomes. Without considering these elements, metrics can sometimes create misleading conclusions. Stability Dynamics is designed to highlight patterns in performance, but interpretation should always be paired with an understanding of the circumstances behind the numbers.

Another important takeaway is that volatility is not necessarily a bad thing. In many cases, volatility simply reflects the reality that elite players often reach extremely high peaks. For example, Aaron Judge frequently appeared in the more volatile categories throughout this analysis. However, that volatility is largely the result of the massive ceiling he has created during his best seasons. When a player is capable of producing historic offensive numbers, the range between their best and worst seasons (in a sample size) will naturally widen. In other words, volatility can sometimes signal elite upside rather than inconsistency.

At the same time, the analysis also highlighted hitters who consistently appeared near the more stable regions of the data. However, it is difficult to pinpoint a single “most stable” player across the entire study, as each metric captures a different aspect of offensive performance. Some hitters may demonstrate steady on-base ability, while others maintain more consistent power production. Due to these differences, stability often depends on which part of a player’s offensive profile is being examined. That being said, players such as José Ramírez, Yordan Alvarez, and Austin Riley frequently appeared in the more stable portions of multiple visuals, suggesting a strong balance between high-level production and relatively steady performance over time. Ultimately, the takeaway is that stability in baseball is rarely one-dimensional. Different players achieve it in different ways depending on their skill sets, making it important to evaluate performance through multiple lenses rather than relying on a single metric.

You can explore the full Stability Dynamics dashboard interactively below, where you can hover over each player and metric to dive deeper into the data.

https://public.tableau.com/app/profile/grace.maretsky6140/viz/StabilityDynamicsDashboard/StabilityDynamicsDashboard

We have officially reached a new era in baseball; the Automated Ball-Strike (ABS) system has arrived to Major League Baseball. Previously, the ABS system had been in the experimental stage throughout the minor leagues and spring training. Well, it’s safe to say the experiment was a success, as the ABS system will now be making its official big league debut on Opening Day. ABS represents the league’s first real large-scale integration of automated officiating into live game play. Understanding how the system works is essential to evaluating its impact on the game and, more importantly, how it will affect the players.

What is the ABS System?

To simply explain what the ABS system is, it is essentially a technology-assisted challenge system used to correct missed ball-and-strike calls. Many baseball fans will say this change was a long time coming, while others still prefer the traditional human only umpire approach to calling the strike zone. While both perspectives are valid, there is no denying that the hybrid approach provides a much smoother transition than moving directly to a fully automated strike zone. Beyond redefining ball-and-strike accuracy, ABS also introduces formal challenge rules that directly shape in-game decision-making. How and when teams deploy those challenges will quickly become part of modern baseball strategy.

How ABS Challenges Work

With that in mind, it is important to establish the key rules that will define MLB’s use of the ABS system. First and foremost, each team is allotted two challenges per game and will retain each challenge if successful. Only the pitcher, catcher, or batter involved in the pitch may initiate a challenge, and it must be done immediately following the umpire’s call. Once the challenge is initiated, the pitch is then reviewed by the Hawk-Eye tracking system, a network of high-speed cameras installed in MLB stadiums that tracks the baseball’s exact location as it crosses home plate. With that, the correct ruling is shown almost instantaneously. These rules ensure that ABS functions as a limited correction mechanism rather than a replacement for the umpire, preserving both game flow and traditional officiating structure.

ABS as the Future

As with many technological evolutions in baseball, ABS is likely to move quickly from an innovation to the new norm. What initially feels like a shift in the officiating dynamic we all know will soon, simply become another accepted part of the game’s framework, much like instant replay before it. Its presence will subtly reshape the competitive dynamics of the sport, influencing how players interact with the strike zone and how critical moments are decided. In many ways, ABS will also represent the first of several automated officiating elements to enter the game’s dynamic, signaling the beginning of a broader technological era that could eventually extend to other judgment calls on the field. Eventually, ABS will no longer feel like innovation at all, but rather a necessary component of the game; the kind that begs the inevitable question: how did the game function without it?

Ahh yes, we’ve finally reached that long-awaited moment after what felt like an endless offseason: pitchers and catchers have reported to camp, and the rest of the squad is to follow. The weather is warming up, practice fields are active again, and optimism settles in across fanbases as every team still feels within reach of what is possible. It’s the calm before the storm, when rosters are mostly healthy, roles are still being established, and players arrive motivated by the promise of a fresh start. As for fans, it’s a rather content stretch of the calendar, when belief is built more on expectation than on any games actually played. At this stage, the scorebooks are empty and last year’s numbers are closed; the only glimpse of the season ahead lives in projection models forecasting what players and teams are expected to produce.

Why Projections Matter Before Opening Day

That reality is exactly why projections carry so much weight at this point in the baseball calendar. With no new games played and no current season stats yet recorded, projections become the closest thing the sport has to measurable performance. These projections are drawn using factors such as recent seasons performances, aging patterns, expected roles, and various skill trends to estimate what players are most likely to produce over the season ahead. For fans, these forecasts shape early expectations and fuel optimism about the roster. For coaches and executives, they help frame lineup construction, role decisions, and evaluations of offseason moves before results exist on the field. In this pre-stat phase of the season, projections function as baseball’s working version of reality, offering a data-driven picture of what the coming months might hold. Those expectations take shape across a range of public and media outlets, from model-driven forecasts to expert interpretation, which together form the projection landscape that defines the sport’s early spring conversation.

Where Projections Take Shape

Many of these preseason expectations take form through widely referenced projection systems and expert analysis that translate data into accessible forecasts. Public models such as ZiPS and Steamer, published on FanGraphs, provide projected player stat lines and team outlooks focused on historical performance and skill trends. At the same time, media outlets and analysts interpret those projections within roster context, discussing breakout candidates, regression risks, and team trajectories across platforms like MLB Network and digital coverage. A popular system used specifically by MLB Network is preseason rankings such as their Top 10 MLB players at each position. These rankings often spark wider discourse from analysts to fans, as differing interpretations of projected values and future performances shape the broader conversation about the season ahead. Together, these examples represent just a few of the many projection-based tools and perspectives that shape how the baseball world envisions the season before it begins. Understanding where these projections originate is only part of their value; interpreting what they actually represent is just as valuable.

How Projections Are Interpreted

When interpreting projections, it is important to remember these are by no means exactly what will unfold once the season begins. They are instead rough estimations of expected performance, and there will always be variation between projected and actual results. There are far too many non-measurable factors that occur during the season for anyone to fully project. Projections are therefore best understood as estimates of a player’s most likely level of performance given what is currently known. At the same time, they inevitably leave room for interpretation, and how they are viewed often depends on the perspective of the interpreter. Each projection is filtered through the beliefs, reasoning, and biases a person brings to it. A forecast that aligns with those expectations tends to feel validating, while one that conflicts with them can instill doubt or surprise, even when the underlying estimate is reasonable. In that sense, projections do not simply communicate expected performance; they interact with perception. The projection itself may be fixed, but its meaning is not, because each person assigns significance through a slightly different lens.

When Projections Meet Reality

As spring training progresses and preseason expectations begin to meet on-field evaluation, projections gradually shift from anticipation to context. What once shaped how the season was imagined is reconsidered through what begins to take shape in camp, where readiness, role clarity, and overall fit on the big-league roster come into clearer view. Some expectations hold, others subtly shift, as the composition of the regular season roster starts to reveal itself. From here, the season will begin to pan out beyond projection and into reality.

For many decades, baseball did not have a clearly defined way to place a specific value on a player. Most player value was based on surface-level stats such as batting average, on-base percentage, home runs, RBIs etc. while a pitchers value was based on ERA, WHIP, Wins etc. While these are all very important stats, they all focus on something different and don’t provide the entire value a player brings, just bits and pieces. This is why sabermetrics has made such an impact on the game of baseball by providing us with modern stats that evolve to tell a bigger story about any given players value. So, what is the biggest inidicator a team places value in? Wins.

That simple truth is exactly why Wins Above Replacement (WAR) was created. WAR provides a way to translate individual player performance into the outcome that matters most in baseball: how many additional wins a player contributes compared to a readily available alternative. What makes WAR especially powerful is that it applies to all players, regardless of position. Pitchers, catchers, infielders, and outfielders are all evaluated on the same scale, allowing teams to compare value across roles that traditionally relied on very different stats. This creates a more complete picture of how each player contributes to winning, no matter where they play on the field. It also takes both offense and defense in to consideration. As a result, some players who are strong hitters but poor defenders, or strong defenders with limited offensive production, may see their overall WAR offset by weaknesses on the other side of the ball.

WAR and Modern Contract Negotiations

Given the wide range of value captured by the WAR stat, modern MLB teams rely heavily on the metric when negotiating player contracts. By translating on-field performance into expected wins, teams can more effectively estimate how much a player should be worth on the open market. This approach helps teams compare players across positions, project future value, and evaluate whether a contract aligns with the wins a player is expected to provide. To connect WAR directly to contracts, teams often think in terms of dollars per WAR. While the exact figure changes from year to year, a commonly accepted estimate in today’s market places the value of one WAR at roughly eight to ten million dollars on the open market. Using this framework, a player projected to produce a 4.0 WAR in a season could reasonably be valued at thirty-two to forty million dollars annually. Teams then adjust this estimate taking various factors in to consideration such as age, injury history, and long-term projections. Younger players who consistently produce WAR at a lower salary generate surplus value, while free agents are often paid closer to full market value for each win they are expected to provide.

Value in Context: Interpreting WAR Totals

So, now that we’ve established what WAR is and the effect it plays in placing value on a player, what exactly is considered a valuable WAR level in today’s MLB? Well, the average MLB player generates roughly a 2.0 WAR over a full season. Also to note, WAR is a cumulative stat meaning a player adds to their WAR each game based on how they perform. Therefore, a high level of performance over a small sample of games may result in a WAR total that does not accurately reflect a player’s value over a full season. WAR doesn’t just reflect quality of performance, it reflects availibility and in turn, the level of performance given in that availability. For example, Aaron Judge played in 152 games in 2025 generating a 9.7 WAR across those games. Even though he missed 10 games, he is still an extreme outlier in terms of excellence displayed through WAR. In contrary, there are players who are outliers in the opposite direction. Players who post a negative WAR create value below that of a replacement-level player, meaning their on-field performance actively costs their team wins rather than contributing to them. I won’t give specific examples to spare those players, but there are far more negative WAR players than one might expect.

Value Dynamics in Perspective

Ultimately, WAR has changed the way value is defined in modern baseball by aligning player evaluation with what matters most: winning games. While no single stat can fully capture everything a player brings to the field, WAR offers teams a consistent way to evaluate overall impact. By framing performance in terms of wins and connecting those wins to financial decisions, WAR has become central to how teams build rosters and allocate payroll. In a game where small margins often separate contenders from the rest of the league, WAR provides clarity in a sport that has long struggled to define value.

Beyond the competitive landscape in baseball, there is a stronger force shaping it: the financial market. Just like any market, it is affected by big spenders, low spenders, and those who quite frankly don’t participate at all. Recent free agent cycles have made this clearer than ever, with teams such as the Los Angeles Dodgers, New York Mets, and New York Yankees spending drastically more than their league counterparts. The Dodgers 2026 opening-day payroll is currently projected to be north of $400 million, which would be the highest opening-day payroll in MLB history. This comes after the Dodgers recently set the record in 2025 for total team investment in a single season at $515 million (including tax).

However, even these astonishing figures fail to tell the full story because they do not fully reflect the impact of contract deferrals. A significant amount of recent superstar contracts, specifically those signed by the Dodgers, push large payments far into the future, some for decades. The only superstar signing in the last last three years without deferrals is Juan Soto, who coincidentally also has the largest contract in MLB history at 15-years, $765 million. This is an interesting one when considering Soto is often thought of as a one dimensional player. Although he does play right field, he is a below average defender and thought to become strictly a DH in the future. I say this is interesting because what does it mean for the future of other superstars hitting free agency? There has already been an increase in extensions for young players that have potential to be special players. For example, the Red Sox extended top prospect Roman Anthony to an eight-year, $130 million contract back in August which could be a steal in the near future (despite Anthony having not played a full MLB season yet). This was also just seen with the Athletics extending shortstop Jacob Wilson to a seven-year, $70 million contract after Wilson has only played one full season. It is highly likely that the trend of young players receiving extensions will continue as an attempt to combat the market.

Due to the current state of the market, many people in the baseball world beyond just fans (including owners) are calling for a salary cap to put a stop to what they deem is creating a competitive disadvantage. Sure, there is absolutely a case to be made there. Yet, there is also a case to be made for a salary floor. This doesn’t get talked about as much because it’s easy to jump on the Dodgers, Mets and Yankees hate train but there is just as much of an argument to be made. Teams spending very little to no money at all are just as at fault as the teams spending at the top of the market as they are also contributing to the competitive imbalance in baseball. Not to mention how unfair it is for fanbases to have to continuously endure losing baseball while simultaneously spending their money on tickets and merchandise to support teams that show little financial commitment to winning. Teams such as the Pittsburgh Pirates, Miami Marlins, and notoriously the Athletics are known for putting their trust in player development and hoping that will eventually grow in to a winning team. However, even if a team is successful in developing a team of cheap young players, they will most likely still have holes in other spots of their roster. These holes can be simply resolved in free agency or even the trade market, but these players will likely cost money either way you slice it.

The most realistic outcome to ensure competitive balance is to instill both a salary cap as well as a salary floor. One cannot exist without the other because each addresses a different failure within the same financial market. Together, a cap and a floor create structural balance. The cap controls excess, while the floor enforces participation. One limits financial domination, the other discourages financial disengagement. This dual framework stabilizes the market by ensuring that all franchises contribute to demand for talent. Most importantly, it protects the integrity of competition by requiring every team to operate within a shared commitment to winning rather than within vastly different financial landscapes.

Hi there, fellow baseball fans!

My name is Grace Maretsky and I’m so excited to introduce my new publication Diamond Dynamics. I would first like to start off by telling you all a little bit about myself and my background.

Growing up in central Florida, I became infatuated with the game of baseball from a very young age. My family moved down to Florida from Long Island, New York when I was just a toddler. However, my family never moved away from their love of the Yankees. With most of my family still living in New York when I was a child, I believe the Yankees provided me with a connection to where I was from. Even to this day, I try to watch as many Yankees games as I can and there’s always a feeling of nostalgia that goes through my head when I hear the YES network music start playing. I also played baseball as a little girl, yes as a girl and quite frankly it was my one true passion. I was constantly bombarded to move over to softball but I did not let that stop me. Well, until I was 12 years old at least. I had made the move over to softball once the boys started getting to the point where they were bigger and stronger than me and honestly, it became difficult for me to keep up. I then enjoyed my time playing softball where I played travel softball for my teenage years as well as playing for my high school team. I will admit, there was a time in there where I started to question my love for the game and fell in to a bit of a hiatus where I stopped focusing on both baseball and softball as I pondered where my passion really lied. I started exploring my hidden love for things like music and fashion, but a true passion just wasn’t there. It wasn’t until about 3-4 years ago when I got back in touch with the game of baseball. I believe being away for some time made my heart grow fonder for the game. I also feel less pressure now as I no longer play the game and can now strictly focus on analyzing it. I’ve now gotten back in to collecting baseball cards, reading the latest free agent signings, looking at player stats, watching highlights etc.

Now that I’ve given you some background on how this all started for me, I want to provide you with some information about my educational and professional background. I recently graduated from LSU with a Bachelors degree in Business Analytics. Immediately following that, I started my MBA with a concentration in Data Analytics. I apologize if reading that bored you but it’s important to note because I am now pursing a career in baseball analytics. As a matter of fact, I will be starting a role as a Baseball Data Scout for Sports Info Solutions, starting in March! I decided to start this blog as a way to connect with other baseball enjoyers and people in the industry who don’t just see it as a job but as a passion.

This publication will be focusing on trends across baseball, from player performance to broader factors shaping the game on and off the field. I approach the game with a methodical mindset, which means I will be focusing on patterns and how different pieces of the game evolve over time. Baseball rarely changes all at once, it evolves gradually, often in ways that are easy to miss unless you’re paying attention. This is a space for connecting the details of each game to the bigger picture of the state of baseball.

Thanks for reading! More to come.