With Fall ball in full swing at the collegiate level and the time for players to compete is upon us, it seems like an appropriate time to talk about the benefits of being a data-centric program. In this post I will be covering the how and reasons collecting and utilizing data can give your team a competitive advantage in the world of player development.
Programs across the country are beginning to evaluate the players they have recruited to be on their team and with that being said, it is important there is an evaluation process in place when players are out on the field. Additionally, the system has to scale to a roster of 40+ players in order for coaches to do their jobs effectively.
As coaches and support staff in the player development realm, we are trying to maximize every ounce of potential we see in our players to develop them to achieve their biggest dreams. From increasing velocity to developing a standard of mental toughness, it is our responsibility to be there when our players are searching for answers. What better way to support and train a player than to have and be knowledgeable about the makeup of that player with quantifiable information? This includes performance data coming from systems like Trackman & Yakkertech, as well as assessment data such as cognitive testing, motion-capture analysis, velocity-based training, and much more. With these insights, we are able to better understand our athletes and help them achieve peak performance while tracking our progress along the way.
In Part 1 of this 2-part series, I am going to cover some examples of how a team can leverage different sources of information to enhance their player development system. First, I want to start off by providing my perspective on what it takes from us as coaches and staff to make a data-driven program be successful.
For decades, coaches and trainers have been trying to crack the code to find out what makes an athlete elite. Is it how hard he throws? How fast can he run down the line? Or even the way he’s able to pick up pitches out of the hand? All of these questions are what drive us to find the best answer to provide our athletes with. As the data-driven craze has evolved over time, we have begun to find and integrate systems that help us interpret and understand the granular aspects of what all this information is telling us. In turn, it has presented an opportunity for all of us in the player development realm to learn and further develop our knowledge of the game as a whole. Tom House, a world-renowned expert on the art of throwing, recently commented on the new age of baseball analytics in a tweet, which is shown below.
The ability to learn is a never-ending road, but the next piece of the puzzle is how we apply what we’ve learned and make it actionable for all players in our program. With that, we will be covering the pitching side of things in Part 1 and follow up with the hitters in Part 2, let’s dive in.
Assessing Your Staff
As your team arrives in the Fall returning from Summer ball or training at a private facility, players are able to make significant strides or slight regressions in their overall performance from the last time you were able to evaluate them with your own eyes, which is where our assessment process comes into play.
On the pitching side of things, there are several ways a pitching coach can assess where their staff is currently at. If able and assuming your team has a ball-flight tracking device, it is a good first step to get a baseline of your staff’s arsenal. If not, Jake does a great job in this youtube video talking about cost-effective options to get the job done. Being cognizant of the pitcher’s intent level and their recent workload, this initial assessment will provide a baseline for what type of pitchers you have on your staff by analyzing several metrics that could include: velocity, spin rate, vertical & horizontal break, spin efficiency and any other metrics you value. In my time at Iowa, we were fortunate enough to have an analytics department capable of forming post-action reports but fortunately for you reading this post there are becoming more cost-effective options for processing data such as Driveline’s TRAQ software, Baseball Cloud, as well as a newcomer in StatStak, which is 100% free and encourage you to check out if you haven’t already.
In addition to a pitcher’s arsenal, it is also important to know how each of your pitcher’s move down the slope and to standardize your way of evaluating their movement. Whether your process is qualitative with video or quantitative with a motion capture lab, being able to retrace the steps of a pitcher’s delivery can aid in the overall development process. Every pitcher is unique in their delivery but what if you were able to find commonalities amongst your staff and could program based on what you find? Could more than one pitcher have the same mechanical issue? This is information that can better help connect the dots down the road. We will not go into the specifics of what to evaluate in the delivery, but if you’re interested in learning more, I encourage you to check out one of Demetre’s blogs discussing the delivery more in-depth.
Unfortunately, not every program has the ability to set up a motion capture lab or have a third-party company do an analysis with the budget they are given, but nonetheless being able to refer to a standardized assessment can help expedite improvements over time.
Collecting information early and consistently in the Fall presents an opportunity for the pitching coach to have a large sample of data to evaluate and improve the staff as a whole. My current philosophy on collecting data is that I’d rather collect it now, than to wish I did as time moves on. In our industry, wishing can be the difference between winning and losing or the chance of a player being drafted. In my opinion, eliminating guesswork and being able to support a coach’s decision with data only enhances a player’s career.
Creating a Feedback Loop
After the assessment process is complete, a feedback loop is needed to measure progress over time. Creating a feedback loop takes time to develop and implement if you are in the beginning stages of becoming a data-driven program, but don’t let that deter you from tackling what can make you great. Feedback loops are not uncommon in today's world, from quarterly sales reports for a business, to customer feedback surveys, this feedback helps guide the path of development in multiple industries - so why should baseball be any different?
In my experience, Fall practice for pitchers consists of team scrimmages & bullpens. With this in mind, we can begin to think about what our feedback loop can look like. Let’s lay out what a typical week might look like for a pitcher who plans to pitch in a scrimmage every Friday and how we can integrate feedback at the right time to maximize production.
It goes without saying that a ton of work is put in behind the scenes to form these reports. Whether it is a group of student managers or a delegated staff member processing the data, from the time a pitcher steps off the mound to the time the report is in his hands, it is crucial this work is performed in a timely manner. As a reference point, my fellow blogger Sam Bornstein was adamant about sending reports off the day of the outing. Having analysts and people in your program like Sam can go a long way and was always a treat as I pulled in the driveway and parked my car to have an email with reports from all the action that occurred that day.
Tying It All Together
So far, we have covered our assessment process and ways to monitor athlete development. To round out this blog we will talk about how our data-driven program comes full circle. After the Fall season is over, it comes time to reassess your players and develop new road maps for their development based on the information you collected. Reassessing your athletes can mirror the assessment process above to see the changes from start to finish. Utilizing this information can support future decisions on how to go about an athlete's training.
To provide an example of how the information you collected can be utilized for future development, let’s talk about the Pitch Design phase and the factors involved. After a pitcher has completed his fall season, we can go back to the data we collected and analyze how his pitches performed and where the areas for improvement are. After a road map is laid out for the improvements, the pitcher has a clear plan of what his arsenal should look like when it comes to to compete in the Spring. As the first game of the season approaches, we can test his new arsenal to see how the changes effected his overall performance.
Although I've strictly talked about Fall ball and the ways to integrate data and the systems mentioned above, this can be applied to any stage in the program’s calendar year. Being a data-driven program does not mean a change in philosophy but rather guide and improve the philosophy in a more efficient manner by eliminating as much guesswork as possible. Stay tuned for Part 2 of this series for the values of being a data-driven program for a hitter.