Chapter 1 is the first of what will be an ongoing series called Data-Driven Hitters. I want to discuss the beginnings of implementing technology to a position player development program. Currently, I am using diamond kinetic swingtrackers (actually just one) to build an assess and re-assess hitting model. Blocks are produced in 4-6 week periods depending on the hitter’s schedule; the goal of each series is to identify, communicate, and correct a component of the hitters swing.
The assessment all starts when the athlete walks through the doors. We will sit down, talk a bit about their swing, their habits, approach and overall tendencies. Once we have received a satisfactory background, the hitter will get ready much like they would any other day following their game-day dynamic warm up and comparable preparation whether that be tee, front toss, or batting practice. Next, we attach a swingtracker and throw ‘em in the cage.
The environment we test a swing in is critical to our findings. Simply watching how a player swings in a non-stressful environment has merit but lacks thoroughness. To record our hitter’s data, we put them in a cage with a traditional two-wheel batting machine. The machine is gunned into 80 mph from 60’ 6” elevated on an indoor pitching mound. We replicate velocity, distance and angles.
Our goal is always to increase in-game performance. I love BP bombs as much as anyone, but one thing all coaches agree upon is that they want to see their hitters succeed when it matters most. Recording a hitter’s data in this environment with synched video allows for an accurate depiction of what swing qualities are inhibiting them from catching barrels.
So far, the test samples have been anywhere from 60 – 90 swings. Twice, our sample size would have been larger, but the swingtracker went flying off the bat. Ultimately, I would like to record more swings, but the process is time-consuming already. In the future, I may assess over two days to get double the data. Next, after logging onto the diamond kinetics website, we export the data as a CSV file. We open and make some minor edits to the data before loading it into the statistical program R.
In the program, we have created a function that creates averages, standard deviations and graphs for all ten different metrics. We label their statistics outside their standard deviations and help use these examples to aid us in deciding upon mechanical changes going forward.
So far approach angle has been especially intriguing; players often exhibit negative averages following lots of cueing surrounding the hands. Approach angle measures the angle of the swing just before contact. Pop-ups and groundballs are both correlated with negative AAs, which makes sense because it is a steep vertical hand path. Remember, we want pure square contact. The ball is always coming downhill at the batter from the pitcher who is releasing the ball at their height, plus reach, plus the size of the mound.
Figure 1: Graph plotting approach angle vs swing # with hacks outside of the standard deviation labelled
This process is a learning curve for me. I have already made mistakes and will continue to keep making mistakes going forward. Overall, the results are especially encouraging. Stay tuned as we go more and more down the rabbit hole.
Grit Performance 