{"id":1442,"date":"2018-01-29T00:00:00","date_gmt":"2018-01-29T00:00:00","guid":{"rendered":"https:\/\/leadersinsport.com\/performance-institute\/reports\/using-data-to-make-decisions\/"},"modified":"2025-12-15T05:12:44","modified_gmt":"2025-12-15T05:12:44","slug":"using-data-make-decisions","status":"publish","type":"article","link":"https:\/\/leadersinsport.com\/performance-institute\/articles\/using-data-make-decisions\/","title":{"rendered":"Using Data to Make Decisions"},"content":{"rendered":"\n\n
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\n Jan 29, 2018 <\/p>\n \t\t\t\t\t\t\n\t\t\t\t\t\t\t<\/span>Articles<\/a>\n\n\t\t\t\t\t\t

Using Data to Make Decisions<\/h1>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\n \n\t\t\t<\/div>\n\t\t<\/div>\n\n\t<\/div>\n<\/div>\n
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Category<\/div>\n Data & Innovation<\/a> <\/div>\n
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Finding the balance between deductive and inductive approaches and the future of in-play decision making.<\/p>\n \n \n \n

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Growing opportunities to collect and leverage performance data have led many leaders within the world of sport to change how they make decisions \u2013 relying less on intuition and more on metrics. But it still continues to be a challenge.<\/h4>\n

Team Sky\u2019s Chief Data Scientist Robby Ketchell says that \u201csport is in a situation where there is rich data, but poor hypotheses. We don\u2019t really understand what our questions are; we are trying to make something apparent that we don\u2019t really know about, like trying to solve a problem we don\u2019t know if it really exists.\u201d<\/p>\n

To help delve into the topic of data-driven decision making we have sought the expertise of Ketchell and Sean Gourley, the Co-Founder of insights agency Quid. Team Sky worked on a new data technique called \u2018crowd sourcing\u2019, whilst Gourley\u2019s organisation focus on the \u2018unmeasurables\u2019 and the relationship between this and better decision making.<\/p>\n

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By Luke Whitworth<\/h6>\n

Finding the balance between deductive and inductive approaches <\/strong><\/p>\n

Team Sky\u2019s implementation of crowd sourcing is a data technique which seeks to gather data based on competitor analysis, via public information. This then allows the team to make their own hypotheses and better evaluate the decision making process.<\/p>\n

The purpose for this is that Ketchell believed that the team had a tendency to try and find answers without really knowing the question. Being hypotheses driven is classed as deduction rather than induction. \u201cThe recommendation is to take this hybrid,\u201d Ketchell says. \u201cSo if you have a bunch of data you can use, how you can analyse it in a way to create these questions, how can you then ask questions that you can perform analytics on and come up with something that may be meaningful.<\/p>\n

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\u201cI bring this up because one of the biggest problems that I encounter, is that if I have data, people expect that I am going to resolve the problem or it is going to replace their job, that they will no longer be the decision maker. If you can show them that induction can actually help supplement their problem solving process, then they will be more inclined to ask questions rather than ask \u2018what does the data say?<\/p>\n

\u201cI think we are really disconnected where we have lots of data and we have a lot of experience, but how are we combining it together? We call this object disorientated. This is where a clash of networked, unbiased data from technology occurs with the opinionated based approach of human experience.<\/p>\n

\u201cI have three thoughts on how we are currently using data in sport. We based our decisions on the data we have available, prior knowledge and experience, and what we speculate will happen. So the idea with the crowd sourcing technique in particular is to gather more data. Combining the experience and the data, we are looking to reduce the speculation, thus leading to better decision making.<\/p>\n

The future of in-play decision making<\/strong><\/p>\n

Complex data is everywhere in sport, whether it is used to\u00a0make better decisions regarding player personnel, injury\u00a0prevention, or to make a tactical change in the middle of\u00a0a game. Data-driven decision making in-play is becoming more commonplace; some leagues have already allowed tracking technology to be used in competition, and this is providing \u2018live\u2019 data for coaches and performance staff to make decisions.<\/p>\n

Quid is a platform that searches, analyses and visualises the world’s collective intelligence to help answer strategic questions. When looking at the world of sport, Gourlay and his team envisage a future of much clearer \u2018predictive performance\u2019.<\/p>\n

\u201cWe\u2019re looking to build models to predict how a game will unfold. What is the likelihood of the team in this position based on its past behaviour of winning inside this game? As the points get scored and the game evolves, you become more and more certain on who is going to win the game.<\/p>\n

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\u201cThis is important, because as coaches you are always thinking when do I remove them from the team and say \u2018game won\u2019? For a coach it also provides information on where you can reduce athletes playing time and help prevent the risk of injury. I can envisage a time where the coaches have technology on the bench and they are able to make these big decisions based on the real-time data.<\/p>\n

\u201cAn also interesting perspective is network-based dynamics. For example where passing networks are about to emerge. As the team comes together we will begin to see the pass patterns the team runs together, thus we can predict the movements and motifs of the player and make much more efficient decisions when either making a substitution or bring a new player into the system. It\u2019s about resolution to strategic decisions.\u201d<\/p>\n

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Growing opportunities to collect and leverage performance data have led many leaders within the world of sport to change how they make decisions \u2013 relying less on intuition and more on metrics. 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