Data & Innovation, Performance | Sep 19, 2019
Why the aspiring data scientist would do well to prioritise an empathetic, people-focused approach to performance, of which data analysis is merely one component.

The backdrop for data and analytics in sport is the desire for an increasing number of owners and proprietors to better understand their investments and this is reflected in the growth of analytics departments as teams go searching for the next frontier.

By John Portch

That frontier will not be inefficiencies in the markets for trade and free agency but gains to be made in the fields of athlete education and development.

Of the NBA model, David Martin says: “Owners these days are intelligent successful business men that generally love the rush and excitement of the competition, they’re used to data-driven decisions, statistics and analytics.”

Going are the days when the heirs to railroads or oil rigs, with a glass in hand and a steady supply of cigars were the dominant trope in North American sports ownership. Martin cites the contemporary example of the Golden State Warriors: “They have Silicon Valley investors, they’re extremely bright, analytical, and highly educated. Similarly, the lead owners from the Sixers are educated at Harvard and Wharton School of Business. If you’re going to be working for these types of owner they will be asking hard questions and they don’t just want an opinion – they want hard data and they want thoughtful responses to their questions. They’ll want to see trends, they’ll want to understand risks and, if you’re not measuring the players in a sophisticated, comprehensive and meaningful way then those conversations could be very awkward and unfulfilling.”

A generation of data scientists have long since realised that rushing into the head coach’s office clutching reams of paper will likely spark the kind of scepticism that sees your bar graphs and spreadsheets consigned to the trash can. A more nuanced approach is needed and the aspiring data scientist would do well to prioritise an empathetic, people-focused approach to performance, of which data analysis is merely one component.

Putting an analyst in uniform

The front office imperative is one thing, but those working with data in the clubhouse or locker room must be able to demonstrate its performance value to the athletes, coaches and support staff. To achieve that, Chad Gerhard says that his analytics staff at the Orlando Magic have some learning to do. “First season at Orlando was first and foremost about educating our analytics staff on what sports performance is,” he begins, “and they in turn have been educating me on basketball-specific metrics as well.”

That process is now five years down the line at the Texas Rangers where the analytics staff has grown four-fold since 2014. “In order to do that quickly you have to have a solid plan about how to develop those assets, how to best use the talents of those people in order to further data-driven decision-making throughout the organisation.” Murray will ensure that analysts are deployed across different departments learning what matters to each stakeholder. “One analyst works in the sports science area, we’ve got another in player development, and another in scouting,” he continues. “They do a lot of travelling, they interact directly with people in those fields; they’re with our coordinators. We had one assistant in uniform at Spring Training. We want to embed people throughout the organisation, not sitting in an ivory tower with email sending out spreadsheets and bar graphs – that’s not compelling or useful and we really want to make the biggest impact we can.”

“I say to the guys, ‘let’s not make assumptions, right?’,” says Karl Cooke of his colleagues at the Western Australia Institute of Sport [WAIS]. “Let’s not just assume that because one of us has been looking really closely at something that everyone is interested or aware. We do work around making sure that everyone on the team is aware of the developments happening in each department. We talk about ‘culture’ and it’s culturally important that everyone on the performance staff is contributing, which means listening and questioning, being interested in what’s happening.”

“No dataset will win you a game”

What emerges is a picture where the subjective matters as much as the objective. At the Texas Rangers, as Murray explains: “Subjective information is becoming more and more involved in our modelling and predictive software, using that to our advantage as opposed to just an objective way of thinking. Getting to that point is so critical and it almost entirely comes from understanding that this is what’s going to make us better and take us to the next level.”

The outlook is similar in English Premiership rugby at Harlequins, where Tom Batchelor says: “No dataset in the world of rugby tell you how to win a game. People outside of sport think that the sports scientists and data staff at teams are like Moneyball, forging their way through with a rationale. But you’re not the lone voice, there’s a lot of smart people in the room and it’s trying to use that data intelligently to get a good idea – not necessarily an answer – but a good way forward. That’s where I think data is in sport, especially in rugby because you’ve got a lot of really good people working in it and it’s about the collaborative effort and walking and talking through problems. So much of it can come down to cohesiveness, especially in rugby; your willingness to put your body on the line and that stuff goes far beyond data. It’s about people.”

Avoiding co-dependencies

Before all else, these people are the athletes, who, as the main protagonists, must have agency in the data-influenced conversations around performance. Athlete empowerment has become a stock phrase in recent times but there are, according to Martin, practical reasons. “You don’t want to create co-dependencies, a situation where a player is relying on another individual like a fitness trainer or psychologist to produce the desirable performance,” he explains.

“You love mentors and teachers but if you create co-dependencies with high profile athletes in elite sport then you are producing an athlete that believes they cannot perform without the support staff; and the coach might believe the same thing. Of course, for the support staff it is a great way to build up job security but it’s a terrible way for the coach and the player to develop their skillsets and their capabilities up to the highest level and be confident in who they are.  Teaching a player to improve is very different than intervening in ways that make a player feel emotionally dependent on a member of the support team.”

This exclusive feature has been extracted from our latest Special Report: Navigating the Data Maze. Download the full report by clicking below, and keep an eye out for our next Special Report landing in just a few weeks time.

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