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Sportlight Conversations: Tim Blackmore Q&A

Updated: Jan 21



Dr Tim Blackmore, who joined Sportlight as the company’s first Head of Data Science in September 2022, is a U.K.-based data scientist with previous professional experience across a spectrum of related fields – including data research, data analysis, biomechanics and software development.


A recent conversation with Tim delved into the intersection of his professional skills, the importance of storytelling in conveying data and new developments he and his team are pioneering at Sportlight.


You are Sportlight’s first Head of Data Science. What is your mission and what sort of skills and experience are you able to bring to this role?

Our mission as a data science team is to maximize the power of our customer’s data to create insights and to develop product features that create added value for our customers.


I hold a PhD in sports biomechanics and have a successful track record of utilizing data to make decisions in fast-paced environments. I have broad experiences in data-related roles and have successfully driven product strategy and improved product performance for companies ranging from small U.K.-based start-ups to the second largest sportswear manufacturer in the world. I’m keen to do the same here at Sportlight.


How do the different competencies in your background – data science, sports science and biomechanics – converge or interact in your day-to-day work?

They are complementary and more closely aligned than most would think, with the sports science and biomechanics competencies feeding into my day-to-day work as head of data science. For example, my sports science competency helps me better understand the needs of our customers, and by liaising closely with our Sportlight sport scientists I can help them realize their vision for new product features, both internally and externally. My experience as a biomechanist really complements our day-to-day data engineering tasks: our LiDAR outputs coordinate data as X, Y pairs, which is a format used across sports biomechanics. Being able to clean, manipulate and extract useful information from this data forms the basis of many tasks.


What is the most common data mistake that you see even savvy sports organizations make most often?

I’m going to flip this question into a positive: What do I see from savvy sports organizations that really stands out? I’ve always been really impressed when I come across a well-constructed data story communicated through great visualizations that really connect with the reader. To achieve this, you need a thorough understanding of the underlying data, so you can distill it down to the most pertinent information and communicate effectively through the visualizations. It also shows an appreciation of storytelling, which, when used effectively, can really elevate the delivery of data.


Your team at Sportlight is currently working on something called a “Fatigue Flag.” Can you explain what that is and how it might be used to benefit sports organizations?

The fatigue flag aims to identify when a player has an increased likelihood of fatigue, which we have shown to be related to decreases in key performance metrics. The fatigue flag is calculated from match data and is specific to each player and their performance history. Unlike similar tools in sports, it isn’t calculated from workload metrics; rather, it assesses the mechanical output of the player and uses this information to make its decision, which is made possible based on the accuracy of Sportlight’s data.


Our customers are already good at monitoring their players. We provide the fatigue flag to empower them in the decision-making process as they manage player workloads across the season.


Sportlight is also currently working on another project: Match Physical Output (MPO) measurement. Please describe MPO and how it will help practitioners, coaches and clubs.

Our MPO metric captures the physical demands of a match for both the team and for a player, and it is normalized to performances for all teams in the Premier League since the beginning of the 2021-22 season. It distills the performance down to a single number, enabling practitioners, coaches and clubs to quickly see the physical demands of a match.


The MPO is made up of an extensive component and an intensive component. The extensive component characterizes the volume of work done across a match, while the intensive characterizes the short bursts of movement and changes in direction that occur throughout the match.


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