Force plates. GPS. Velocity Based Training. Heart Rate Monitors. In the last 5 years, the emergence of these technologies have become less and less rare for high level sports teams. It has become common practice for almost every Division I basketball or football team to have these resources at their disposal. On top of that, all of these technology pieces provide you with hundreds of metrics to analyze your athletes and help inform you on decisions.
Let's be real. It can be overwhelming. “If I don’t use all the metrics, am I utilizing the resources to the best of my ability? Do I just use 2-3 metrics to keep my tracking simple and digestible? Should my athletes test before or after practice? Should they be wearing their Kinexon units in their shorts pockets or vests? Do I measure Peak Velocity or Mean Velocity when using VBT?” These are all questions that many of us have when first implementing sports science into our programs. For me, it was a lot to handle and I needed a way to bring some meaning to the madness.
Here at Eastern Michigan, I’ve been blessed with a plethora of sports science resources at my disposal so this was a very prevalent problem for me. I asked myself one question: what really matters? This question led me down a rabbit hole of “what really matters for MY program?”. What really matters for Eastern Michigan Men’s Basketball? Asking myself this question led me to understanding my team better and what their needs are.
So I started just by simply collecting data consistently. We did countermovement jumps every day that we lifted and we lifted every day we practiced. I also collected Kinexon data every practice and game.
The first step to understanding what metrics matter for your program is understanding what the metrics mean. Coming into the 2023-24 season I had extensive experience with force plates but not with Kinexon. So I spent the first 6 months tracking each player’s jump height, accumulated acceleration load(workload), and practice duration. I realize that these three metrics are not the only key metrics in basketball but I needed somewhere to start.
Load management
The first issue I noticed was how our team periodized practice workloads leading up to games. I had just finished reading The Quadrant System by Daniel Bove and realized that our practice days all fell into Quadrants. The four quadrants of The Quadrant System consist of Repetition, Recovery, Speed, and Strength. I added a twist to my quadrant system. The horizontal axis was workload and the vertical axis was practice duration. This graph of our practice intensity and volume seemed to match up with the four quadrants well.
Every time we practiced for long durations with minimal live drills, practice & lift fell into the repetition quadrant. If we practiced for under two hours but went live the whole day, it fell into the speed/high intensity quadrant. If we practiced low volume and low intensity, practice fell into the recovery quadrant. Finally if we practiced high volume and high intensity we fell into the strength quadrant. This was great information for me to have, but it led to more questions: “WHY does this matter? Is this information useless? HOW can this make us better? Can this make us better? There's no way I can affect what practice looks like”, I thought.
It was then that I realized that all these metrics and data streams that we collect are completely useless unless effectively communicated to the right people. In this case, the right person was our head coach. So I created a system that was simple and easy to understand. I did not teach him The Quadrant System(although it would be great for all sport coaches to know), I did not tell him that he needed to shorten practices. Instead I educated him on how to properly taper practice loads so that our players would be primed and ready for gameday. And I sent him daily workload reports to further educate him on how this looked for us.
I turned every quadrant into a category of High(Red), Medium(Orange), or Low(Yellow). I sent daily and weekly workload reports of practice and game days. 800+ workload is a High(Red), 600-799 workload is Medium(Orange), 599 or lower is a Low(Yellow). On high days we lifted heavy, on medium days we did repetition effort/hypertrophy lifts, and on low days we did recovery. On timeframes leading up to gameday, we did primer lifts, I called them ‘short & fast’. If we are 3+ days out from game day the plan is to do a High(Red) day, if we are 2 days out we have a Medium(Orange) day, and if we are 1 day out the plan is a Low(Yellow) and primer combo. Below is our quadrant system:
This seemed like the perfect plan…until it wasn’t. Coach liked the idea, but as I learned the hard way, you can’t control what your head coach wants to implement at the moment. Through a tough trial & error process I realized that basketball teams need to have a much more personalized training approach. You may get 5-7 players that play 30+ minutes per game, a couple that play 20 minutes, and the rest get 0-10. So a blanket, one size fits all would not work.
At any given moment in season, we may have 3-5 groups of players who are all in different quadrants. This is where this quadrant system allowed for me to personalize workout programs into a Red, Orange, and Yellow group or Quadrants 1-4. The biggest lesson I learned from this ordeal was that simple is better and all information is useless unless communicated effectively.
How can you effect the game?
The second unique metric that mattered to our program came in the form of exertions. We were coming off of a bad loss to a team that was at the top of their conference. But this loss was not due to a lack of talent, but a lack of effort. As we were sitting in the film room watching play after play of players not contesting on defense, jogging in transition instead of sprinting, and not being physical I got to wondering.
“Is there a way to quantify this? And would the quantification correlate with any basketball metrics that matter? Would this type of correlation motivate players?” More rabbit hole questions and more gears started turning in my head.
The first metric that came to mind was Exertions via Kinexon. But I needed context around exertions. We had a particular player on film who looked like he did not care about playing defense at all. We’ll call him Player 1. Player 1 is a player who is extremely coachable, a marksman three point shooter, and a liability on defense. In our previous season, he got away with being a liability by watching film of his matchup and consistently practicing his defensive technique. He is not the most athletic player but is known for giving us all he’s got. So seeing him looking lackadaisical on defense was not his M.O..
This got me curious. “What was Player 1’s Exertion rating from this game? How does it compare to previous games or even his average from last season?” It turned out that Player 1 was down ___ Exertions this game more than his previous season’s average. Which resulted in a poor shooting percentage and a loss. Doing some more digging, I found that the more High Intensity Jumps that Player 1 had per game via Kinexon, the higher his rebound count was per that game. It seems like a straightforward correlation, however simple can be profound.
My next thought was “Is this a common correlation across the roster? But what if various players have different Exertion levels per game? How can we compare players, regardless of how many minutes they play? EXERTIONS PER MINUTE.” A method to the madness was starting to click for me.
“Pick 2-3 players who have consistent Kinexon data to work with. Stat their Exertions/minute for every game they played last year. Stat their rebounds in a parallel row. Graph it. Does it correlate?” I charted these stats for Player 2. Here's what I found:
As you can see, the higher the exertions/minute for Player 2 the higher the rebounds per game. Everytime Player 2 had one exertion per minute or higher he had at least 6 rebounds(with the exception of one game). The next step in this process to validate this data would be to gain context by tracking the exertions/minute for the rest of the roster and track it against their rebounds per game. Then find out if the same correlation happens across the board.
What use is this you may say? Validating this correlation may prove to give the coaching staff another metric to track that may encourage more “effort plays” or correlate with more effort statistics. What are effort stats you ask? These stats may look different for every sport but we will use basketball for the purposes of this article. With the exception of rebounds & blocks, effort stats are usually plays that do not necessarily show up on the stat sheet.
Box Outs
Loose Ball Dives
Deflections
Wall Ups
Effort stats add to the mission of building a team that is more likely to be ‘tougher’, more ‘gritty’, ‘physical’, ‘defensive’, or better able to fight adversity. Whether shots fall or not may be out of a player’s control, but their defense can be primarily dependent on a big controllable
variable: effort(or exertions). A player’s level of defensive intensity in basketball tends to depend on the level of effort given in a game. In turn, if rebounds do correlate with exertions/minute, other effort plays may also correlate with exertions/minute, which may lead to a better defensive team.
As you can see, sports science can be extremely specific and contextual to the team you work for. There may be some teams that use load management and some that don’t. There may be some teams that utilize force plate data and some that don’t. In conclusion, sports science is still a very new area and one that needs a lot of exploration.
At the end of the day, keep it simple. Mitigate the madness for yourself. Nobody said you have to track ten metrics. Utilize two or three to improve or reinforce the performance of your team. The discovery of these two uses for Kinexon data has immensely helped me with preserving player health, impacting team culture, and reinforcing solid training principles.
For more help with sport science check out all SCN's webinars on the topic of sport science. We have presentations from LSU football's Manager of Applied Science Scott Kuehn, Texas A&M's Associate Professor Dr. Bryan Mann, NBA Cleveland Cavaliers's Dr. Jason Avedesian, and more.
About the author
Aaron Abraham is heading into his second year as an assistant sports performance coach at Eastern Michigan University. In his role he oversees all aspects of sports performance for men's basketball, women's tennis, and women's dive.
