The ways in which we measure athletes today provides an abundance of data to pore over, but are we better informed as a result? For example, in some sports — like baseball — not only are there traditional performance statistics like batting average and pitching earned run average, we now have newer measurements to examine, including launch angle, spin rate, and exit velocity. Baseball is not alone, as every major sport has dramatically increased the number of ways to precisely micro-analyze athletic ability — or so it seems. Yes, we have more numbers today, but does the increase in data truly reveal what is intended, or do the numbers sometimes mislead us? Even kids today are regularly caught up in their stats, sometimes at the expense of simply going out and playing their best. In fact, some would-be future athletes prematurely quit because “their numbers” don’t make the grade, for whatever that’s worth. The truth is it takes expertise to understand how to use statistics, what statistics measure, and how applicable stats are to real-life situations. Unfortunately, sport statistics are often misused, and many times lead to poor decisions and/or unwanted outcomes.
Breaking down the numbers…
Years ago, I used to teach graduate courses in research methods where we would examine how to properly set up research questions, find appropriate subjects, develop hypotheses, run statistics, minimize confounds, and responsibly generalize findings. Students quickly learned that good research takes a lot of work, both in the design of the study, as well as how to accurately test for significant differences between control and experimental groups. For example, if you were interested in testing subjects on their level of depression, you could choose from a variety of tests with varying sensitivity for picking up differences between groups. Simply put, a quick 10 question online test for depression will likely provide much less credible findings than a more thorough instrument consisting of more questions and used by mental health experts for many years (i.e. the MMPI).
Going beyond mental health and using a different example, think about if you were shopping for a used car and examined the car on the lot from 50 feet away — there’s a good chance that short of easily visible damage, your first impression of the car might be pretty good. But what if you walked up and got real close to the car, and even used a magnifying glass to look for subtle scratches and other car damage. Do you think you might find more things now? Chances are you would, and this illustrates how data collection can vary based on how precisely you measure things.
Yes, we have more numbers today, but does the increase in data truly reveal what is intended, or do the numbers mislead us?
You might also think about data collection like catching fish. If you use a big net with large holes, small fish will swim through easily while big fish will be caught. If you instead use a very small net, you will catch a lot more fish — but you will also catch everything else that is too big to pass through the net (often unwanted things!). These are the kinds of questions researchers must contend with in order to conduct valid research, and not the kinds of things most sport decision-makers think of when perusing team data.
And then there are questions around external validity, also known as generalizability. If a baseball hitter has a nice launch angle when he makes contact that’s one thing, but what if the hitter consistently misses hitting the ball? In basketball, the +/- statistic looks at how good the team does when the player is on the floor, but we often forget that the +/- number also has to do with the player’s teammates on the floor, the opposing players on the floor, the plays the coach calls, and a host of additional variables that impact this number.
The point here is not to throw statistics out the window, as there are many great uses in collecting data and uncovering important findings. What is concerning, however, is the trend right now for sport decision-makers to try and outsmart one another by procuring as much data as possible, and making haphazard decisions based on what they think are the best ways to predict future success. By taking this approach, many actual things that lead to success are overlooked, including mental toughness, resiliency, focus, leadership, motivation, performing in the clutch, and overall knowledge of the game.
Final thoughts
Collecting data and using data to predict future events and outcomes is a worthwhile pursuit, but findings can be easily abused and misused if not being interpreted by folks who understand research statistics. These days, you can spend hours looking over mountains of data related to athletic performance, but often in doing so we lose sight of the real, human elements that make great players — including motivation, game savvy, and human resilience, to name a few. At the end of the day, it’s not always what you have physically (that contributes to data), but instead what you do with what you have. One athlete with great numbers may under-perform because of poor focus and low motivation, while another athlete with lesser numbers may end up playing better due to a hyper-focus, unwavering motivation, and great resiliency.
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