UNIVERSITY PARK, Pa. — Baseball teams could soon have a better concept of pitcher strikeout efficiency — particularly when an opposing team’s player is on third base at risk of scoring a run — thanks to a new statistic developed by a Penn State College of Information Sciences and Technology (IST) student.
Using data accessible to him through his summer internship with Prep Baseball Report, Spencer Burns recently introduced a statistic called Finished Strikeout Percentage (FK%). The statistic highlights a pitcher’s strikeout percentage once a batter’s plate appearance logs two strikes. Burns’ statistic could help teams better strategize when to bring in a relief pitcher, or in their recruiting or signing efforts.
“The statistic was created with the idea that a ball in play risks a worse outcome for a pitcher, even though outcomes like fly outs and ground outs can still result in the pitcher doing his job,” said Burns, who will earn his bachelor’s degree in data sciences in December. “If you’re at two strikes, only one more is needed to finish the batter without the risk of the ball going anywhere onto the field and creating a chance that the batter produces.”
At its base, FK% becomes active when a count reaches two strikes. If the pitcher records a strikeout, his FK% will rise; if the play results in anything but a strikeout, the FK% will decrease.
According to Burns, FK% is particularly valuable in situations where a player from the opposing team is on third base and at risk of scoring a run, regardless of the number of outs in the inning. Burns’ hope is that managers and coaches could use the statistic to find or place pitchers that increase the chances of holding that runner to third base and finishing a strikeout rather than potentially putting a ball in play.
“When a runner is on third base with fewer than two outs, if a ball gets into play, a run will score if there’s a hit or sacrifice fly guaranteed. While a ball anywhere in play besides an infield pop out greatly runs the risk of the third base runner scoring,” said Burns. “But if the pitcher finishes the strikeout, the higher the chance that the runner is not even going to have a chance to get home since the batter won’t have a chance to create runs.”
Discovering an interest in data
Burns’ interest in sports analytics stems from his background of thinking deeper while watching sports and his introduction to analytics in high school. While he initially enrolled in Penn State’s energy engineering program, he discovered the applied option of the data sciences program in the College of IST and made the decision to change his major in the summer of 2020. He has put his classroom knowledge to practice with the Penn State men’s baseball team, with whom he works as a data analyst. In that role, he creates new tools and analytical projects to generate reports and data visualizations for players and coaches. He also assists in developing scouting reports that give coaches and players opposing teams’ stats, tendencies and data to help inform their strategies.
“All of these metrics can help identify player tendencies or help you see what opposing teams are doing to give an advantage of what is going to be happening in a game,” said Burns.
Added Jake Stone, director of operations and player development for Penn State men’s baseball, “Spencer has been around with our team longer than nearly anybody on our manager staff, and he has made a great impact throughout that time. Specifically, our data team has evolved a lot in his time here and he has played a big role in that. I’m excited to see how he continues to develop and how his career will blossom after graduation.”
Burns has honed his knack for data analytics through two consecutive summer internships with Prep Baseball Report, one of the country’s largest and most respected independent scouting services. Last year through his internship, Burns worked as a data analyst with the MLB Draft League in its inaugural season to develop reports, scout players and deliver data projects for MLB scouting departments and coaches. This summer, he is working with the company's data operations team, learning more about data engineering.
He and another intern are attending each State College Spikes' home game, where they talk to staff, coaches and players to deliver requested statistics and video. During the game, Burns works from the press box where he operates the Trackman system that tracks all game metrics and Prep Baseball Report cameras throughout the stadium that allow scouts to view specific angles of the game.
Throughout these experiences, Burns has used skills and knowledge gained at IST — including the programming languages of SQL and R, and other tools that explore the back end of databases.
“I’m still learning all these things, little by little,” he said. “But having that baseline from IST definitely helped me learn more quickly and understand what I'm looking at.”
Developing a metric
Burns’ idea for FK% came while he was watching a recent game between the Rays and Yankees. He became very interested in trying to understand how pitchers decide their next pitch. He began carefully watching players to see if he could pick up on specific patterns and had an idea.
“I would watch these professional games and pitchers would get the two strikes, and then the ball would be hit into play,” said Burns. “I thought ‘it would just be so much easier to get a strikeout here without even needing to worry’.”
Burns researched existing statistics to prove his theory but couldn’t find anything. He contacted Jake Stone, director of operations for Penn State baseball, as well as Mojisoluwa Awe, a fellow College of IST student completing a data analytics internship with the San Francisco Giants, to see if they had ever seen such a stat. They hadn’t and encouraged Burns to pursue it.
Using data available to him through Prep Baseball Report from the MLB, MLB Draft League and College baseball, Burns developed his code. He plotted the statistics for starting and relief pitchers, while comparing their weighted on-base averages — an existing statistic that accounts for the value generated by a player getting on base. He found that finished strikeouts show a correlation with a pitcher’s performance.
“Not allowing the ball to get into play or being efficient with the pitch count are two early thoughts that could impact a player’s game with FK%,” Burns said.
As Burns continues to advance his stat, he hopes that other analysts will explore ways it can become more valuable to those who use it. FK% is currently able to identify pitchers that can get batters out more efficiently through strikeouts with a strong correlation. It can also be used for in-game decisions to decide if a particular reliever may be better in certain situations. The statistic is currently available for teams, coaches, scouts and analysts — from the college level all the way to the MLB — to use. Burns is hopeful that it becomes a regularly used statistic in baseball.
“I'm finally able to say, ‘Hey, I created something cool’ from the things I’ve been learning about for years,” said Burns. “I made something, and it could be a contribution. I’m very happy and proud about that.”