Major League Baseball is a marathon of outputs: 162 games in under 190 days, layered with cross-country travel, overnight flights, and relentless skill demands. To keep athletes healthy and performing, baseball strength and conditioning and performance staff need more than isolated drills or siloed testing. They need systems.
That is the philosophy of Brad Lawson, the Chicago White Sox Director of Strength and Conditioning (and former Giants, Cubs, and Nationals coach), who has spent more than 16 years inside MLB. In this FYTT session, Lawson laid out a framework for system-based player development: one that aligns staff, organizes data, and automates decisions without replacing the art of coaching.
Baseball is chaotic: dense schedules, inconsistent travel, and constant stressors. Without structure, strength and conditioning departments risk fragmented methods that make it impossible to evaluate what is working.
Lawson compares it to the U.S. highway system: everyone is heading toward the same destination, winning, but without a roadmap, teams take inconsistent paths that waste time and resources.
A systems-based approach ensures:
Athlete profiling is the cornerstone of Lawson’s system. It is more than running athletes through a battery of tests at the start of camp; it is about creating a living snapshot of what each player needs to perform their role in games. For MLB athletes, this means mapping game demands (for example, a center fielder’s sprint and agility needs vs. a pitcher’s power and recovery profile) and then testing against those benchmarks.
Brad relies on serial testing protocols to align with the concept of residual training effects. Speed qualities are retested weekly with countermovement jumps on force plates; strength qualities every four to six weeks with isometric belt squats; aerobic capacity every eight weeks using Wattbike tests. By spacing tests according to the shelf life of each quality, coaches avoid overtesting while still catching meaningful changes.
Other coaches at the pro and NCAA level follow similar philosophies, often with technology like Vald Performance’s NordBord for hamstring strength or Catapult GPS units for external load tracking. The difference with Lawson’s approach is how he ties these disparate data points together into one system; instead of collecting data for reports alone, he uses it as a real-time driver of programming.
FYTT supports this by letting staff build athlete profiles that automatically update with each new data input, whether from Catapult sensors or athlete surveys. Once data is entered, the platform triggers logic that adapts training prescriptions in minutes, not days.
Infrastructure is the bridge between raw data and actionable programming. Without it, test results become static PDFs sitting in staff inboxes. Lawson emphasizes two key pillars of infrastructure: a structured exercise library and periodization models tied to organizational values.
An exercise library is not just a catalog of lifts. In Lawson’s system, each exercise is mapped with substitutions and injury contingencies. For instance, if a pitcher suffers a wrist injury, the system can automatically swap a barbell front squat with a neutral-grip safety bar squat. This ensures that athletes stay on track with development plans instead of losing weeks to ad hoc modifications.
Periodization models form the second pillar. MLB’s dense season means traditional block periodization is impractical. Lawson prefers vertical integration, where all biomotor qualities (strength, speed, power, hypertrophy) are trained year-round but at variable intensities. Other practitioners, such as those in NCAA football, lean on high-low training models from Charlie Francis, consolidating high CNS days with heavy practices and saving low-load days for restoration. Brad’s infrastructure blends both: high CNS training paired with high-intensity practice days, and low CNS work paired with lighter sessions, ensuring stress is consolidated rather than spread haphazardly.
In FYTT, infrastructure takes the form of pre-built templates that codify periodization rules and substitution logic. Once set, these templates are applied dynamically to each athlete profile, ensuring consistency across hundreds of players.
Automation is where Lawson’s system achieves scale. Managing 200+ athletes across MLB affiliates is impossible with spreadsheets alone. Instead of rewriting programs for each player after every test, Brad uses conditional logic and decision trees to adjust programming automatically.
Conditional logic is simple: if a player’s NordBord score falls below a set threshold, they move into a hamstring risk group. The system then serves them a pre-programmed corrective or strengthening protocol without manual intervention. Decision trees take this further by layering multiple inputs: for example, combining countermovement jump asymmetry, wellness questionnaire scores, and Catapult workload data to decide whether an athlete should receive a full session, modified volume, or a recovery day.
This approach is not unique to Lawson; some NBA and Premier League performance departments use similar rules-based models, often with in-house data scientists coding workflows. The challenge is accessibility: most college and high school programs cannot afford analysts and custom software. FYTT lowers the barrier by embedding decision trees and automation directly into the platform. Coaches can codify their logic once, and FYTT applies it automatically across the roster whenever new data is entered.
KPIs are the compass of Lawson’s system. Rather than tracking every available metric, he focuses on a handful that consistently correlate with performance outcomes: lean mass, impulse (via force plates), and biomechanical efficiency for pitchers.
Lean mass links strongly to velocity-based outcomes like exit velocity and sprint speed. Impulse captures the interplay of peak force and rate of force development, offering a clearer picture of power than a 1RM squat alone. For pitchers, lead-leg bracing efficiency often predicts velocity better than weight-room strength tests.
Other coaches emphasize different KPIs depending on context. College coaches may track squat or clean PRs as key indicators of strength transfer, while high school coaches often focus on simple, low-cost measures like vertical jump height or timed sprints. The trend across elite sport, however, is moving toward KPIs that combine biomechanical insight with sport-specific transfer.
FYTT makes KPI tracking actionable by aligning them with athlete profiles and decision trees. If a player’s KPI trendline dips, FYTT automatically modifies their training plan to address the deficit, rather than leaving it for staff to notice manually.
Lawson pushes against the idea that in-season training is just maintenance. With smart testing and microdosing, players can improve strength and power during the grind of 162 games.
For relievers, fatigue scores integrate pitch count, velocity, and workload history to adjust training. For position players, Lawson often schedules a single heavy lift day furthest from games, paired with micro-dose sessions earlier in the week.
This mirrors trends in other sports. Rugby and soccer teams, for example, use GPS data to microdose sprinting throughout congested schedules. The shared lesson: small exposures sustain qualities that would otherwise decline under competition density.
Does automation threaten S&C jobs? Lawson says no.
In Brad’s view, the next decade of performance coaching will belong to those who can blend objective data systems with subjective coaching art. FYTT positions itself squarely in this middle ground: automating what can be automated, freeing coaches to do what only they can do.
Brad Lawson’s framework is a reminder that systems scale impact:
In a sport defined by density, travel, and stress, this model provides clarity, consistency, and adaptability across a 200+ athlete organization.
FYTT: Why do you emphasize systems so much in baseball?
Brad Lawson: MLB is chaotic: dense schedules, heavy travel, and constant stress. Without an organized framework, methods get inconsistent and you cannot evaluate impact. A system ensures efficiency, effectiveness, reliability, and repeatability.
FYTT: What big constraints do all clubs face?
Brad: Two universal ones: schedule density (162 games in ~190 days) and travel (cross-country, overnight flights, disrupted sleep). Then add stress from life, anxiety, and inevitable injuries.
FYTT: What is step one in your system?
Brad: Athlete profiling and monitoring. Build a profile against game demands and track serially based on residual training effects. Speed and power may change weekly; strength adapts over months.
FYTT: Can you give an example?
Brad: For an MLB center fielder, KPIs might be sprint speed, exit velocity, and bat speed. If they are below average in max velocity sprinting, training emphasizes speed and absolute power.
Audience: Which KPIs have been most useful?
Brad: Lean mass correlates with sprint speed, exit velocity, and pitch velocity. Force plate impulse is the strongest predictor of power. Pitch biomechanics, especially lead-leg bracing, matter for velocity.
Audience: How do you test in-season?
Brad: CMJ weekly; isometric belt squat every 6–8 weeks; aerobic measures sparingly. In spring training, full intake testing. Whenever possible, the test is the training.
FYTT: Can players improve in-season?
Brad: Yes. With microdosing, players can improve strength and power even during 162 games. The “grind” is often more psychological than physiological.
Audience: When do you microdose?
Brad: For position players and relievers: small, frequent exposures (for example, sprints or concise circuits). Starters can tolerate larger doses in a 5-day rhythm.
Audience: What is your go-to conditioning test?
Brad: Wattbike plus HR monitors for VO2max estimates, since everyone can complete it. Position players also do 20-yard sprints with lasers.
FYTT: How do you connect data to programs at scale?
Brad: Build exercise libraries with substitutions and periodized templates. Add automation: conditional logic and decision trees. For example, NordBord results auto-bucket players into risk groups with pre-set programs.
FYTT: How complex can decision trees get?
Brad: With the Giants we built a mobility tree using 15–20 inputs that created more than 500 possible program outputs. Testing data went in and within seconds players had individualized plans on their phones.
Audience: Does automation threaten coaching jobs?
Brad: Absolutely not. Automation scales coaching decisions; it does not replace coaching. Players still need hands-on teaching and buy-in.
