Major League Baseball recently released a report about pitcher injuries. The report included interviews with 200 subject-matter experts discussing the increase in arm troubles in the sport, with the term “stuff” mentioned 47 times. Specific sections of the report focused on stuff metrics like Stuff+ and their potential impact on pitcher health.
Advancements in technology and machine learning have significantly improved the study of the physical characteristics of pitches and their relation to outcomes in recent years. Metrics like Stuff+, which assess a pitch based on its velocity, spin, and movement, have become instrumental in evaluating and developing pitchers. These metrics have also led to the increased use of pitch types like sweepers, hard sliders, and cutters across the league.
Teams and analysts have embraced Stuff+ as a crucial tool in understanding pitch quality and evaluating players. The development of stuff metrics was inspired by a player’s own question about improving his pitches, leading to the creation of metrics like Stuff+.
Stuff metrics have become an integral part of player evaluation and development processes within teams. They have also made their way into scouting reports, offseason plans, and regular discussions among analysts, coaches, and players.
To better understand the concept of Stuff+, it is essential to know that it evaluates pitchers based on their movement, velocity, spin, and release points. The analysis seeks to remove on-field performance context and focus on how certain combinations of pitch characteristics typically perform across baseball.
Stuff+ was pioneered by research analysts like Jeremy Greenhouse and has been further developed by experts like Harry Pavlidis, Alex Chamberlain, and Tom Tango. The metric has become a standard tool in the evaluation of pitcher performance and pitch quality.
Stuff+ emphasizes the importance of velocity, movement, and release point in determining the effectiveness of a pitch. It uses machine learning to assess various combinations of these factors and assign a single value to measure a pitch’s quality.
One of the key insights from these models is the significance of a pitcher’s release point in influencing the movement of their pitches. Pitchers who can manipulate expectations based on their release point tend to perform well in stuff models.
These metrics have had a profound impact on player acquisition, development, and in-game strategy. Notably, they have highlighted the underutilization of certain pitch types like sliders and have led to the resurgence of pitches like sweepers, cutters, and sinkers.
Researchers have also found a strong correlation between pitch shapes and batted-ball outcomes, providing a more reliable predictor of performance metrics like ground-ball rates and home-run rates.
The rise of stuff metrics and their impact on player health has raised concerns within the baseball community. While these metrics offer valuable insights into pitch quality, they also pose challenges in managing pitcher workload and injury prevention.