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Analytics and Data in Sport Science

“Data, it’s so hot right now. Data. ”    –  Mugatu

From my point of view the biggest thing today in strength and conditioning and sport science is the search for data. I fully support the idea of having good quality information to inform decisions, but it seems like data collection is becoming more than a means to an end, but the end itself. I have been guilty of this behaviour.

Let me explain the big gap that exists (in my opinion). Ever since Moneyball came out, the search for analytics has been full-fledged in sports. With that, most people started thinking about OBP/OPS in baseball, CORSI in hockey, etc. These numbers began to be seen AS analytics. The idea that the Oakland As looked at OBP to select players was one of the biggest takeaways from the book/movie. The truth is, it was about trying to decide the value of one player towards the teams success in such a dynamic environment. The key fact is…Data IS NOT analytics.

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Analytics: the science of logical analysis. That is the definition. Nowhere is data mentioned. So what is data? Just information. The process of logical analysis represents trying to look at something more clearly to get closer to causation. Sometimes data is used, because it can bring light to a certain context. But one can’t be mistaken for another.

Let me give you a real-life sport science example. The first place we are told to start when working a new sport is with a needs-analysis. This brings us to watching the sport and scouring the journals, where most sports have had papers published on this particular concept. Yet most times the needs analysis only explains a result or output of the sport. Not the big question, WHY?! For example, a play or attack lasts 6 seconds, rest for 30 seconds…lactate concentration of X mmol/L after a match. These are merely observations.

So on this note, I witnessed a high-performance environment where the coach was taking great stock in an experiment that showed a particular lactate concentration in competition, and had a desire for training to reflect that. A data-seeking individual would be really happy to collect lactate samples on a regular basis and fill a spreadsheet. Using analytics might get you to ask the question about what the lactate tells us.

Lactate accumulation is about an athlete competing in their sport with a certain intensity, and work-to-rest ratio. It does not explain the key determinant of winning and losing. Let’s take an example with 2 opponents, martial arts, or wrestling, mma, etc. Both fighters will likely have a similar lactate profle after the match. Yet can you guess the likelihood of who won?

In order to do the best job we can in sport science and strength and conditioning we MUST be analytical about the factors that go into predicting success (we can never guarantee a result, but merely increase the probability!). Sometimes that includes collecting data to provide key information, but it doesn’t have to. Being analytical means asking more questions in order to feel more and more confident about your decisions. Don’t get bogged down by data, but choose to be analytical.

It’s About Getting Better!

4 comments on “Analytics and Data in Sport Science”

  1. Michael says:

    Right, but don’t I want to know how hard my athlete needs to train, especially when I can measure blood lactate to know how hard they’re working? The two athletes in your example MIGHT have similar lactates after the fight. But if one is feeling good at 10 and the other is struggling at 7, guess which one is better prepared? Data and sport science exist to answer questions. One contemporary flaw may be that teams are seeking out data without having a question in the first place.

    1. razorsedgeperformance says:

      I think the point Cory was trying to make was not that data is bad, he mentions how much he loves data in the beginning. The point was more that collecting data doesn’t mean analysis; analysis means to analyze. So more data doesn’t necessarily get you more answers, you need to know what you’re looking for, ask the right questions and get answers out of the data. In that sense you’re right,the data can be there to answer questions.

    2. razorsedgeperformance says:

      @Michael, that is a good point and it involves a concept of focus. If you really want to know a person’s capacity to make it through 5 minutes of fighting (or whatever metabolic demands exist in a sport) then MAYBE you seek to look at lactate data or heart rate responses to the effort. I don’t want to say that there is no place for this kind of analysis, because it may be necessary at some point.
      However, just as you mention about the ‘contemporary flaw’ of data collection, the concept of analytics is to be using data to ANSWER the QUESTION we pose. Taking lactate may be a knee-jerk reaction to have some sort of tangible measure in the sport, but at any given time, its value to me may be limited. When my question starts asking why did they win or lose, lactate may not have the resolution for this kind of question (not that any one variable can in an OPEN sport). Hope this makes sense.

  2. Carlee says:

    Well done arclite that. I’ll make sure to use it wisely.

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