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Coaching & Development, Performance | Aug 4, 2020
Brian Selman of the Pittsburgh Pirates explains how improvement science can enable a systematized approach to unlocking a team’s time, talents, and resources to reach their potential.

Brian Selman serves as the Assistant Director of Minor League Operations for the Pittsburgh Pirates helping oversee the day-to-day operations with a quality control lens on people, processes, and strategy. Brian is currently pursuing a Doctor of Education (Ed.D.) in Leadership and Learning in Organizations degree at Vanderbilt University.


By Brian Selman

Big data baseball, meet improvement science. Improvement science, meet big data baseball.

The baseball industry is experiencing an obnoxious inclination to measure everything under the sun. With mountains of data accumulating, margins for competitive exploitation are diminishing. As margins narrow, the quest for meaningful data persists with no evident hypothesis. Complex quantitative analysis has become an integral part of the game. Leveraging new technology and resources guides organizational decision making while simultaneously aiding player development. Not investing in robust quantitative analysis now signals negligence. The shrinking return on investment for data collection should also not be considered license for aimless exploration.

Unfortunately, we often confound simply having data as a final solution disregarding fine print realities advising some assembly required. Data is obviously not being pursued malevolently although its misconstrued application remains problematic.

Better knowledge, tools, and resources will inevitably emerge. We must accomplish more with what we have now. Remember batting helmets and radar guns once represented new technology. By focusing on specific tasks or behaviors, the processes and tools used, and how the prevailing policies and norms affect the process, improvement science can accelerate our learning (Bryk, Gomez, Grunow, & LeMahieu, 2015, p. 7-8).

The improvement science approach means deploying rapid test of change guiding development, revision, and continual fine-tuning of all relevant inputs reinforcing a learning-by-doing model (Bryk et al., 2015, p. 8).

This approach galvanizes technology and data integration in our development processes thus, yielding systematic improvement. Improvement science can immediately advance baseball development connecting pragmatic objective measurement between the analyst, the coach, and the player. Secondly, improvement science lends perspective on incremental learning encouraging scalable implementation.  Improvement science is not a comprehensive solution, but organizes systematic focus on high leverage opportunities producing faster learning.

The search for pertinent data is a worthy task; however, laboring to integrate data connecting information to practical on-field application has become a wasteful battleground. We must better weaponize data into information bolstering adaptable development processes. This conversion is simply not happening at the pace or depth necessary to meet our potential. Player development will endure as a quixotic adventure shepherding young players toward the big leagues. Employing improvement science will illuminate the path helping us quickly discern the difference between windmills and giants.

Pragmatic integration of objective measurement

Measurement in baseball is widely misunderstood and often misapplied. The analyst and coach view knowledge differently. Akin to finance, analysts are rooted in academic style research broadly testing hypotheses to form opinions. Coaches rely on intuition informed through years of experience in assessing problems and prescribing solutions. Both use quantitative information and apply equally strong conviction in different ways.

Unfortunately, coaching strategies rarely include objective measurement checks and balances leaving their counsel at the mercy of near-term game performance. Peak game performance is of course the ultimate goal, but likely is not the ideal outcome measurement in observing implemented changes. Analysts commonly levy data-driven judgment without acknowledging a player’s potential to improve. Players typically range from age 18 to 24. Since their brains are still growing, we must also acknowledge their capacity to exceed expectations sourced from dated information.

Improvement science demands that good development strategies constantly commit to answering three core improvement questions: What is the specific problem we seek to solve? What change might we introduce and why? And, how will we know whether the change represents actual improvement? (Bryk et al., 2015, p. 9).

By keeping our focus problem-centered and user specific, we can understand what a good development process looks like, the inherent non-negotiables, and where flexibility exists for nuance and creativity. As we implement change behaviors into routines, we can assign specific measurements to assess progress. Finally, we must collectively review whether the changes made actually signify improvement. If we are not adequately testing hypotheses, we can never truly know if we are progressing.

Coaches must be mindful of their proclivity to apply solutions without appropriately considering the problem. Consider a simple arithmetic metaphor comparing the two questions: What is the sum of six plus four? What two numbers add up to ten? The only right answer for the first question is ten. Mediocre coaches are prone to misdiagnosing problems as sixes in order to apply their four solution. The second question’s solution space is infinite. Great coaches consider problems deeply assessing which particular sum best equals ten in that moment. Deborah Ball and David Cohen add, “Teaching occurs in particulars – particular students interacting with particular teachers over particular ideas in particular circumstances” (Lingenfelter, 2016, p. 135). The same can be said for coaches and players.

Improvement science classifies measurement in three categories: improvement, research, and accountability. Improvement measures focus on work processes as the objects of implemented changes. Research measures test for correlations amongst conceptual variables. Accountability measures focus on final outcomes identifying exemplars or problems. Data often resembles accountability by default for baseball practitioners creating unsubstantiated mistrust and devaluing the impact of data informed coaching. Creativity and bias for action would certainly be stifled were job security actually at stake each day. Coaches must understand that improvement measures focused on daily work is the path to progress. By continually testing changes in behavior, we can effectively determine the accuracy of our diagnoses relative to desired outcomes. Maladaptive coaches inevitably fall behind modern technology’s pace, misconstrue the power of objectivity, and reduce their coaching dogma to folklore.

Our tools and resources demand more from our coaching process than the traditional path of diminishing expertise to unexamined heuristics. Our coaching credibility is constantly being tested as illusory information and expertise becomes abundantly available online. As the speed of trust is often slow, coaches must accurately identify problems and relevant measurements before implementing solution strategies. Teaching through parables remains a winning approach assuming we connect them to the people and situations at hand. The winners of the future will blend hard earned tacit knowledge with information procured from modern tools and resources at today’s information pace. Integrating objective measurement with the art of coaching allows faster learning with immense improvement potential.

Incrementally building to scale

Professional baseball player development demands consideration for hundreds of players divided across multiple teams led by distinct coaching cadres. Many factors influence player progress such as cultural and socioeconomic backgrounds, languages, and personal relationships. In serving this diverse population, baseball often succumbs to scaling traps unwittingly deploying more tools and information than sense-makers can manage. This demands decentralized leadership capable of adapting quickly to find solutions. Overarching goals and strategic context must be understood for team members to evaluate risks and approach a complex environment with clarity (McChrystal, Collins, Silverman, & Fussel, 2015, p. 99). Any lack of clarity ultimately hurts the players. Coaches without clarity at best produce unsustainable compliance. Implementing new tools and resources at maximum scale creates similar confusion. While possessing contemporary technology conveys cutting-edge innovation, poor deployment strategies diminish the tools to political theater. Tools running parallel to coaching rather than in conjunction with coaching cripple capacity for improvement.  Baseball organizations frequently experience what Byrk et al. describe as failing to appreciate what is required for promising ideas to work reliably, feeling disappointed when dramatic results do not emerge, and moving on to the next new idea (2015, p. 6).

Although packaging tools as silver bullets is wildly lucrative, no such thing actually exists. Improvement science’s approach is not a comprehensive solution either. Intimately understanding the problem comes first. Understanding and executing the non-negotiables, operating with agency in the space left for creativity and nuance, and objectively assessing change behaviors allows us to answer whether we are making improvements. As improvement and learning accumulates, gradual scaling becomes possible. Full-scale implementation without appropriate consideration discourages critical internal analysis, hides failures privately, and fails to recognize opportunities to learn (Byrk, 2015, p. 177). Beginning the change process unobtrusively cultivates confidence and clarity in the work as risk is marginalized. The pace of changes spreading cannot exceed the current expertise base able to teach and mentor the work itself (Byrk, 2015, p. 119). Accumulating small experiments promotes greater understanding of potential obstacles and means of troubleshooting, discovering unanticipated process innovations, and providing coaches learning reps preparing them as sense-makers. Top performers will gradually emerge as process champions co-opping voices into action as scale increases.

Conclusion

Integrating objective measurement and incrementally building to scale is not an appeal for playing nice and sharing toys. It is a journey to learning faster and smarter. We owe our players this type of quality process. Baseball at its core remains the simple game portrayed in 1988’s film Bull Durham explaining, “You throw the ball. You catch the ball. Sometimes you lose. And sometimes it rains.” However, today’s environment is drastically faster and more interdependent bringing unprecedented complexity. Growing mountains of data and available tools do not automatically yield better information and improvement. To answer the call of player development, leaders must consider themselves designers of meaningful experiences leveraging objective measurement as learning accumulates for scalability. Our players deserve it.


References

Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How America’s schools can get better at getting better. Harvard Education Press.

Epstein, D. J. (2019). Range: Why generalists triumph in a specialized world. Riverhead Books.

Lingenfelter, P. E. (2016). “Proof”, policy, and practice: Understanding the role of evidence in improving education.

McChrystal, S. A., Collins, T., Silverman, D., & Fussell, C. (2015). Team of teams: New rules of engagement for a complex world. Portfolio/Penguin.

Shelton, R. (1988, June 15). Bull Durham.

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