Limitations of Traditional Scouting and Signs of Change
NPB scouting has long relied entirely on the trained eyes of veteran scouts. Scouts observe hundreds of games annually, comprehensively evaluating players' physical abilities, skills, and mental fortitude. This traditional approach had certain effectiveness in identifying hard-to-quantify qualities like future potential and baseball instinct. However, it also carried serious limitations. First, reliance on individual scouts' experience and subjectivity led to significant evaluation variance. Cases frequently arose where one scout rated a player as ready for immediate contribution while another classified the same player as a raw talent. Second, physical constraints on the number of games that could be observed created risks of overlooking unknown players from rural areas. Third, there was a tendency to overvalue the achievement of appearing at Koshien in high school player evaluations, resulting in insufficient discovery of talented players who never made it to Koshien. From the late 2010s, data analysis began to be introduced into scouting as a means to complement these limitations. The Oakland Athletics' approach depicted in the film Moneyball had considerable influence on NPB front offices as well.
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Datafication of Amateur Baseball and the Transformation of Scouting
Supporting the development of data-driven scouting was the establishment of data collection infrastructure in amateur baseball. In high school baseball, some powerhouse schools began introducing measurement devices like TrackMan and Rapsodo from the late 2010s, accumulating data on pitchers' velocity, spin rate, and movement, as well as batters' exit velocity and launch angle. Similar movements spread to university and corporate baseball, dramatically increasing the volume of data accessible to NPB scouts. Particularly noteworthy is the Hiroshima Toyo Carp's scouting reform. Despite being financially disadvantaged compared to metropolitan teams, the club built an efficient draft strategy utilizing data analysis. In addition to traditional scout evaluations, they introduced pitching mechanics data analysis and growth curve prediction models for physique and physical ability, successfully discovering players overlooked by other teams. In drafts from the 2020s, cases of players recommended by data analysis departments performing well at the first-team level have increased, demonstrating the effectiveness of data-driven scouting.
Fusion of Data and Scout's Eye - Practicing the Hybrid Model
The most effective scouting method in current NPB is considered to be the hybrid model that fuses data analysis with scouts' experiential knowledge. In this model, data analysis first screens candidate players, listing those who are numerically promising. Then veteran scouts actually observe the listed players, evaluating elements that data cannot fully capture, such as mental toughness, team adaptability, and growth potential. The SoftBank Hawks are known as pioneers of this hybrid model. Their scouting department has built a structure where former professional player scouts and data analysts coexist, comparing both evaluations in weekly meetings. Interestingly, while players where data and scout evaluations align are prioritized as high-certainty picks, cases where evaluations diverge sometimes lead to new discoveries through deep investigation of the divergence causes. For example, when deeper analysis was conducted on a player who appeared ordinary in data but was highly rated by scouts, it was found that differences in measurement environments and opponent quality levels had affected the numbers, leading to more accurate evaluation.
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Ethical Challenges of Data Scouting and Future Outlook
The development of data-driven scouting also raises several ethical and institutional challenges. First is the privacy issue regarding data collection and use of amateur players. Cases have been noted where physical and performance data of high school and university students are shared with professional teams without sufficient consent, making data governance development urgent. Second is the risk of overlooking talent not reflected in numbers due to data overemphasis. Qualitative attributes such as creativity, leadership, and mental fortitude under adversity cannot be adequately captured by current data analysis. Third is the possibility that disparities in data analysis capabilities between teams may undermine competitive balance. There are concerns that well-funded teams monopolizing advanced analysis systems and talented analysts could widen information gaps in the draft. Future NPB scouting is expected to see further technological evolution, including AI-powered automatic video analysis and utilization of biometric data from wearable devices. However, the final judgment on players remains a human decision, and data is merely a tool to support that judgment. The harmony between technological evolution and human insight will shape the future of NPB scouting.