Think about Baseball

Danny Young New York Mets pitcher Danny Young’s Baseball Savant movement profile is as follows. He throws a sinker and a sweeper with significant horizontal movement from a low arm angle. He’s a pitcher with an interesting pitch mix. It’s clear that he confuses hitters more with differences in horizontal movement between pitches rather than vertical movement. Horizontal vs. Vertical Movement Differences This gave me an idea. I looked into which pitchers have the greatest differences in horizontal movement and which in vertical movement when pairing their pitches two by two. As previously seen, Danny Young falls into the former category. The image above shows the movement profile of Cubs pitcher Caleb Thielbar in 2020. Unlike Danny Young mentioned earlier, he features a four-seam fastball and curveball combination from a high arm angle. He falls into the latter category, characterized by a large difference in vertical movement. Based on data from 2020 to 2025 for pitchers who faced at l...

 The legendary Major League pitcher Warren Spahn once said, “Hitting is timing. Pitching is upsetting timing.” This statement perfectly captures the essence of both pitching and hitting. A pitch that disrupts a batter’s timing is one that is difficult to react to. So, what kind of pitches are hard to react to? It could be an extremely fast pitch, or a very slow one. But more importantly, it could be a pitch that’s difficult to predict. In this article, I use entropy to simply calculate the randomness of Major League pitchers. Method 1 Here’s how I calculated randomness: I calculated the entropy of each pitcher’s pitch usage rates by pitch type. Then, I normalized each pitcher’s entropy score between 0 and 1 to obtain a “pitch usage entropy score.” The more pitch types a pitcher uses, and the more evenly they’re distributed, the higher this score becomes. I selected every pair of pitch types a pitcher throws (for  n  pitch types, there are nC2 combinations), calculated the...

During the last offseason, I was looking at a scatter plot of Cleveland pitcher Tanner Bibee’s pitch locations, and it reminded me of a regression line with a negative slope. I had a similar thought after watching Aaron Nola’s knuckle curve location. However, the slope of Nola’s regression line is more gradual than Bibee’s. While this may be the result of various factors—such as the trajectory and usage of breaking balls, as well as command—I focused on something a little different: arm slot. Nola’s average arm slot is 20 degrees, whereas Bibee’s is 51 degrees. And arm slot does have some correlation with pitch movement. I looked into the relationship between pitch location scatter plots and arm slots. Here’s how: •   I plotted pitch location scatter plots for pitchers who threw more than 100 pitches of each pitch type. •   From these scatter plots, I drew regression lines and calculated the location correlation coefficients for each pitcher. •   Then, I compared the corr...

Rays are well-known for fixing underrated players, especially pitchers. In 2024, they cast a spell on another player: Edwin Uceta, who became the Rays' new bullpen ace.  Uceta joined Rays before the 2024 season under a minor league contract. Before he came to Rays, he played for six teams; in three of them, he wasn't called up to the major leagues. He had a low arm slot and a long extension. He was evaluated as he has a good change-up from prospect age. It can induce swing in a miss, not picky about the left or right-handed hitter. And his control and command were evaluated as less than average.  From June, he settled into the active roster, and here is his 2024 stat.  How did the Rays change him to the best reliever in the league? 1. Add cutter to his arsenal Before he joined Rays, he had 3 pitch types: Change-up, Four-seam fastball(FF), and Curve. The pitching percentage of the Four-seam fastball and Change-up is above 90%.  In Rays, he started to pitch cutter and ...

 September to October marks the postseason. Numerous articles discuss which team holds an advantage in the playoffs during this period. These articles analyze factors such as roster age (experience), head-to-head records, and win probability against teams with over or under .500 records.   And yes, no clear evidence that these factors decide the postseason-winning team. In the conclusion of  this article,  they analyzed about 60 variables, but none of them effectively predicted postseason results.    There is also one conventional wisdom heard during the postseason: 'Do not think about tomorrow.' That means do everything to win today's game, not prepare for tomorrow's game.   Does it make sense? I tried to figure out the answer using a simple tree model. In this calculation, the win probability per game changes like in a zero-sum game. If one team's win% goes up 5%p, that team's tomorrow win% go down 5%p.   Team A win probability for eac...