KinaTrax

First look at our new Ball Tracking system that we are happy to roll out and share. Full ball flight trajectory tracking and tracking spin @500 frames per second. #biomechanics #collegebaseball #baseball #ncaabaseball #MLBProspect #Scouting #MLBScouting #Prospect #sportsscience #sportsanalytics #playerdevelopment #pitchingdevelopment #baseballbiomechanics #mlb #kbo #npb #ingameanalysis #pitching #pitchinglab #sportsbiomechanics #balltracking Major League Baseball (MLB) NCAA

If you missed today's Biomechanics webinar highlighting KinaTrax, reach out for the recording! Discover how Databricks Solution Accelerators streamline biomechanics data processing in real-time with an event-driven ETL pipeline. Explore the user-friendly interface for insightful data querying, empowering biomechanists to enhance player performance optimization. #Biomechanics #DataAnalysis #KinaTrax #Databricks

Another publication utilizing an in-game KinaTrax system from this group. Great work and looking forward to seeing and reading more! #biomechanics #collegebaseball #ncaabaseball #mlb #nba

***Tidbit Tuesday - Velocity in the Jump is Velocity on the Mound*** This week’s Tidbit Tuesday is going to focus on the velocity of the pitcher, both on the field and in the countermovement jump (CMJ). Let’s first talk about what the velocity of the pitcher means in these contexts. When talking about the velocity of the pitcher on the mound, I am talking about the anterior/posterior velocity of the center of mass of the pitcher, or how fast the pitcher is moving their entire body towards home plate in the linear move. Specifically, this post will be dealing with the maximum velocity that the pitcher is moving down the mound. From the CMJ standpoint, we are looking at the same center of mass velocity of the pitcher but measured at the time when the peak power of the jump occurs. For this post, both of these metrics will be measured in meters per second. First, let’s examine what influence the center of mass velocity has on fastball velocity. Using linear mixed modeling, grouping by pitcher, and utilizing random slopes and intercepts, we find that, among 77 pitchers who threw at least 10 fastballs, there is a significant positive relationship between the two variables. Specifically, every 1m/s increase in center of mass velocity results in a 0.3mph increase in fastball velocity. This can be seen in Figure 1, where the majority of the lines are trending upward, suggesting the faster a pitcher can move down the mound, the faster their fastball will be. So, fastball velocity is related to the center of mass velocity in the pitch, but can we relate the center of mass velocity to the velocity of the pitcher during the CMJ? Interestingly, yes, there is a relationship between the two player velocities! Utilizing jump metrics on a week-by-week basis, and obtaining the median center of mass velocity for each week, we can see that the velocity of the countermovement jump performed during training is positively related to the pitcher’s center of mass velocity on their fastballs (Figure 2). Similarly, there is a significant relationship between weekly median fastball velocity and the countermovement jump velocity, with weeks where pitchers move faster on their jumps resulting in higher median fastball velocity for the week (Figure 3). However, the data is very limited in sample size, and replication would be needed. These results give pivotal information to both training and monitoring athletes using different countermovement jump metrics. Identifying pitchers who may have low COM velocity at peak power on CMJ could mean they are leaving pitch velocity on the table. Similarly, if you see an athlete who starts to trend toward a slower COM velocity on their CMJ, this could mean the athlete is experiencing fatigue, and this may directly impact their ability to perform on the field. Share your thoughts below! Have you noticed similar connections with your athletes?

Tidbit Tuesday and the Two-Strike Approach We are back with another weekly research question, and this week we will be looking at hitting biomechanics. Specifically, we will be quantifying the two-strike approach in college hitters. When looking at the two-strike approach, it is logical to think that a hitter would prioritize making contact rather than trying to optimize the exit velocity of the ball, and thus we would expect a decrease in bat speed. Well, with in-game biomechanics data we can quantify this! And that is in fact exactly what we see. There is a significant within-hitter difference (p < 0.001) in bat speed between counts below 2 strikes and 2 strike counts. On average a hitter slows their bat down by about 2.12 mph in two-strike counts when compared to counts with less than two strikes (see Figure 1)! Taking a more in depth look at this, we can see that sometimes hitters still swing hard with two strikes (Figure 2) but the cluster for bat speed with two strikes is skewed towards swinging slower to optimize contact over power. But where exactly in the loss in bat speed come from biomechanically? Likely, the resulting bat velocity decrease is linked to the velocities of the more proximal segments, making the trunk and pelvis max rotational velocities primary candidates for differences (Figure 3). Both the trunk (p = 0.001) and the pelvis (p = 0.045) were significantly slower in the two strike counts! On average, the max trunk rotation decreases by about 18 degrees per second, while the pelvis was around 14 degrees per second slower. This is a case where data may be statistically significant but not clinically relevant as these rotational velocities typically peak around 710 degrees per second and 650 degrees per second respectively. Interestingly, a significant decrease in max hip shoulder separation (p < 0.001) in the two-strike counts was also observed, with the average decrease being around 4 degrees. This 4 degree difference seems clinically relevant when the typical max hip shoulder separation is 30 degrees for a swing. This change could also alter additional swing mechanics down the line. So, when quantifying the two-strike approach, we know that the bat speed is likely to be lower, and this decrease in bat speed is likely due to a change in intent during the at-bat. But that same change in intent also has ripple effects that alter the mechanics, which in turn slow the bat down! What are your thoughts on the two-strike approach? Have you noticed similar trends in your data? Feel free to share your thoughts or experiences in the comments below. #Biomechanics #Baseball #DataAnalysis

Advancing Player Performance in MLB with Data - Thursday, August 15, 2024, 10:00 AM - 11:00 AM #biomechanics Major League Baseball (MLB) National Basketball Association (NBA) National Hockey League (NHL)

Great to see #Biomechanics more and more in pro and amatuer sports: "What's crazy is my [#biomechanist] Ralph Mann, before I left for Paris, he's like, 'This is how close first and second is going to be away from each other,'" Lyles said, holding his hand up with a narrow space between his index finger and thumb. "I can't believe how right he was." Full story: https://lnkd.in/g3iMVr4Z Coley Harvey Ralph Mann Noah Lyles #sportsscience USA Track & Field

Eight-camera COM Player Tracking for everyone on the field including baserunners and the ball. Ready for the 2025 season. Rolling out samples of the 19-segment biomechanical results shortly. Major League Baseball (MLB) Baseball United #collegebaseball #ncaabaseball #biomechanics #baseballbiomechanics USA Baseball #baseball Korea Baseball Organization #NPB #KBO

Another sneak peek at our final modular piece of the #baseball #tracking puzzle along w/our Pitching, Batting, & Player Tracking Systems - the new Ball Tracking system including tracking full ball flight & tracking spin at 500 fps. #biomechanics #collegebaseball #ncaabaseball #MLBProspect #sportsscience #sportsanalytics #playerdevelopment #pitchingdevelopment #baseballbiomechanics #mlb #kbo #npb #ingameanalysis #pitching #pitchinglab #sportsbiomechanics #balltracking NCAA Major League Baseball (MLB)

⚾ **Exciting Updates!** ⚾ I am thrilled to share that I have successfully completed my first year of graduate school at Wake Forest University. This past year has been an incredible journey of intense learning and personal growth, as well as learning more about baseball and pitching biomechanics' influence on the sport while working in the Wake Forest Pitching Lab. Over the course of the summer, I have started my thesis work, exploring UCL injury and recovery. I am utilizing predictive modeling techniques to forecast UCL injuries through biomechanics, and exploring forearm strength training's impact on UCL injury prevention through the use of FlexPro Grip. I am excited about the discoveries and insights this research will bring. In addition to my academic progress, I am delighted to announce that I have accepted a position as a Data Processing Engineer at KinaTrax. In this role, I will be responsible for processing KinaTrax markerless motion capture data and ensuring the accuracy and reliability of our research-grade biomechanics data. Thank you to everyone who has supported me along the way—I am excited to see where this journey takes me next!