No Matter What Data Says, You Can’t Ignore the Human Element in Sports


Dave Hancock, 49, has more than two decades of experience working as a physiotherapist, strength coach and performance director across professional sports in the U.K. and the U.S. He recently helped rehabilitate NBA star Kevin Durant from an achilles injury, and he has also worked with Chelsea FC, the New York Knicks, the English Men’s National soccer team and the 2019 World Series champion Washington Nationals.

Dave Hancock

Dave Hancock

Hancock is also the CEO and co-founder of Apollo V2, a customizable sports software system that helps teams and individuals manage, collect and communicate data, as well as teach them how to enhance performance and prevent injury. Hancock and his team launched the system to create a landing spot for a team’s new and existing day-to-day tech platforms, and to present it in an easily accessible way on mobile devices.

Looking back over the past decade, what has surprised you the most about the sports tech space?

There’s been a massive data explosion in sport. There’s products coming out left, right and center to do X, Y and Z. And I think some of them are very good. However, I think people are trying to run before they can walk—people are trying to think that that’s the answer. 

Like GPS, for instance, well let’s look at his load, let’s look at the intensity. It’s very helpful information, but it’s not the whole answer. There’s been a big thing about acute chronic work ratio and using some science and research from different sports, but you can’t look at a paper that studied AFL and compare it to Premiership Soccer, or NFL, or different sports that have completely different workouts. Especially at the pro level, people are missing the point, I think they’ve drifted towards this huge data collection but no one really understands what it actually really means. People are trying to hypothesize what they should be doing, or following what someone said. But actually, if you’re collecting data, is it influencing your change? Because if it isn’t, why are you bothering to collect it? If you’re doing something like, for instance, a strategy on injury prevention, is it reducing your injuries? If not then you need to go back and reassess what you’re doing. That’s what the data should allow you to do. 

I think that there has been a fascinating amount of objectivity coming into the industry. We’ve gotten less about the coach’s view and the subjectivity of the coach. What I tried to do in the Apollo is to meet halfway, to take the coaches’ subjective, take the objective from the collections of the data piece you’re using—accelerometry, gyroscope, heart rate, whatever you’re doing—and look at them collectively together. And I don’t think people are doing that. They are being very clouded by what they’re collecting. And they’re thinking that something is occurring, but they’re not necessarily tracking what is occurring. 

So I think that we need to go back and probably go back to a bit more basic analysis and try to keep things more simple rather than trying to be complicated in what we’ve done. And I’ve seen a ton of that over the last 10 years: I’ve seen sports scientists produce reports that even I, someone who’s got two master’s degrees and been in pro sports for 25 years, just could not even understand. And if I can’t understand it, how on earth can a coach understand it? 

So therefore, the coach is just going to look at it, and literally tear it up and put it in the bin. And the guys probably spent two or three weeks putting that report together with all the really fancy algorithms and calculations. Therefore, you’ve got this guy doing all this stuff, but he’s not really influencing anything. But he thinks in his world that he’s doing some really fancy stuff, but it’s not really making any change. So what you have is all these silos working, and the silos think that they’re doing some really interesting stuff, but the problem is being on an island or being in a silo is not the way to operate. The idea is that you should be all together and be influencing the people that make the decisions, and those people are the GM, the owner and the coach.

Are there any trends brewing that will change the way athletes are trained and rehabilitated?

Holistic longitudinal analysis—and what I mean by that is it has to be everything. It’s no good just looking at medical and weight training data. You have to be looking at what the coach thinks, you have to be looking at the subjective view of the coaches, you have to be looking at the objective data from training or from the game. You have to be looking at the statistics of the game. The Billy Beane Moneyball scenario was fascinating, but he only looks at games, where he’s not looking at all the other variables around what makes that guy tick, what makes that guy perform on a regular basis. And if he’s going through a divorce with his wife, where does that come up on the statistics? If he’s got some pain in his Achilles, where does that go up on the statistics? If he’s going through some psychological depression, where does that go on the statistics? 

If you can find a way to collect this data easily through API’s or through your phone or through voice—so like we’ve got voice, so a coach or an athlete can just press a button on his phone and talk, and that’s completely dictated and recorded for the coach or the athlete to go back retrospective and look at that game or that practice. So you have to understand, coaches aren’t administrators. If you can find a way to collect information very easily from the people working with the athletes—the more information you can get consistently, the better your algorithms or your AI’s or your modeling is. The problem is that they’re only as good as the people putting the data in, and what I think they miss the point that if you have data sets coming in as limited, then your modeling is going to be limited. 

And we’re not dealing with big data in sport, but everyone thinks we are. It’s so competitive, everything else is small data. So what we try to do is say, ‘How can we collect more information? How can we make the coach’s life easier?’ Another example is we have a medical app where I literally could be driving home earlier from the training ground every day, maybe three quarters an hour every day earlier to be with my wife and kids. I feel better. It’s hard enough work in this sport. Right? It’s your life. The people that work in sport—and I’ve experienced it for 25 years—are literally committed to that team 12, 14, 16 hours a day. 

So if I can save an hour by putting my notes when driving home and just talking into my speaker in my car, how much better will I be? Because the last thing I want to do at the end of the day is write up the notes, but medically, legally I have to. And you would never know that if you never got off a plane at 3 a.m. and you’ve got to go and do a report for the coach for the next morning for shootaround. So all of a sudden that report is already done and the data flows, you don’t have to worry about it. Show it to him on your phone at breakfast the next morning and brilliant, I can go to bed early.

People are taking these data sets out of these different products and putting them into other systems because those boxes don’t allow them to do or manipulate what they wanted to do. And we’ve tried to look at it from different ways. How are you manipulating the data? How can we help you make your life easier? How can we bring these data sets to flow? And how can we produce visual reports to deliver, make a change, or at least make an awareness? 

What is your best advice for someone breaking into this industry?

Don’t just assume that because you’ve got an idea that seems really good to you, it’s necessarily going to work for someone else. Go and see what other people are doing. Go and see what other companies are doing, go and see more. The engineers and the people that are developing these products, that’s the part that they’re missing. And I think it’s great to produce a product that measures X, but measuring X without bringing in Y and Z might not necessarily be the answer. 

What companies have to think about—and that’s really where we’ve come at it—is let’s just take everything we’re doing from A to Z. And can we bring it all together and allow you to put it in a simplistic format to make sense of it? It’s almost like: how do you influence the change? Just because you’re collecting something and you’re showing something improving, how is it going to improve that? How are you going to monitor it? 

There are a ton of instruments coming out now about being in a weight room and working out and collecting what you’re lifting and the speed you’re lifting and the load that you’re producing from squatting, bench pressing, power cleaning. And you can show progression of whether you’re getting stronger, whether you’re jumping higher. And all that is relevant within the gym space. But if a guy doesn’t do it on the court, or do it on the pitch, what relevance is how high he jumps or how much he presses in the gym, because what matters is what he does on the field, not what he does in the gym. 

What we’ve tried to do is to allow those two divisions to start looking at that together. Because if I show that this guy is fantastic in his combine, but he can’t catch a ball, it doesn’t matter about his combine testing, it’s a waste of time. Can he do it week in, week out in the game? So therefore, my testing I’m doing in the combine is an example, maybe I need to adjust what I’m doing or testing. Maybe I need to look at what I’m doing differently for that individual compared to another individual. And that’s what Apollo allows the teams to do.

Who do you admire in your field the most and why?

So I have a saying about sport—and I feel that people who get at the top level of sport sometimes just do the necessities to survive within that team within that organization. And I call them survivors. And I think there’s a lot of survivors in sport. Basically, people that get to the top and they’ve done very well to get there but never really thrive. The people that I admire more are what I call the innovators. So the people that get the sport, challenge the norms. Right? So always every single year, even like at Chelsea we won championships back to back, it’s like what are we going to do different next year? You could argue, well, we shouldn’t do anything different because we just won—in that mindset you’re a survivor, you’re not an innovator. So it’s the innovators, the people who change, the people who are always looking at how they can improve, even if they are at the very top of their field. They are the people for me, in any industry: CEOs, coaches, they are people for me that I most admire.

What doesn’t exist yet but should?

Live AI for sport. But when I’m talking about it, I’m talking about it from the whole holistic view, not retrospective modeling, live so that you literally can see by putting that information in today where’s the out. And that’s something again with one of the reasons that interests us with Brooklyn Dynamics was that they do live for the Tour de France. So they do live analysis with the Tour de France. So that was one of the big attractions for us partnering with Brooklyn Dynamics, to look at how we can influence and do that.

And I also think simplicity of getting new information to us. So using products out there to drive information when we need it. And I don’t think that that is as good as it could be in sport. We’re currently working on something which I won’t reveal to you, but something that just would make people’s lives really easy when they’re trying to get information. Because in sport, like any business, that’s the most important thing for people. Tell me that, tell me this, tell me this and then allow that analysis to make a decision—and some of that is always going to be the experience of the coach.

But some of that objectivity now can help influence that subjectivity. It’s like you’re a financial analyst buying a share and you can look at all of the data traits and all of the models and things that people use to make money within the world of stocks and shares. There’s still an element of the experience of the person pressing the button to buy or sell, this feeler, you know, and intuitive experience, as well as the model to help make that effect. And that’s the thing, the problem is we are dealing with so many variables. You are also dealing with human beings, who are complicated people—individuals that make up a team are all very different. And the idea is that if you can bring all that information together and make it in a really simplistic, easy way to bring it together, it can allow them more exploration about what you do on a day to day, week to week, month to month, year to year basis, whether that’s on injury prevention, or whether that’s on performance.

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