Christmas Special: New Data Audit Tool!

  • On December 18, 2017
  • audit, audit, data, data, html, html, report, report, value, value, variable, variable
Human performance data is often riddled with missing values, data entry error (large outliers), inconsistent recording frequencies and duplicate records. These features can make analysis of your data very difficult. Missing values and duplicate records can introduce bias (leading the practitioner to draw incorrect inferences), data entry errors (or large outliers) can negatively impact statistics […]
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How to Interpret and Apply FVP Sprint Results

  • On November 1, 2017
What we know so far Those who have been following our FVP Sprint blogs would know by now that we made it easy for you to run your own FVP testing session with our SMARTSPEED system. This simple protocol is based on new research (Samozino et al., 2016) that allows us to estimate an athlete’s […]
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Run an Session

  • On October 31, 2017
In our last blog post on FVP Sprint, we outlined what FVP is and how it offers new insight for coaches when compared with traditional sprint monitoring. This post will discuss the implementation of FVP Sprint using a SMARTSPEED timing-gate system and the new FVP dashboard within SMARTSPEED Cloud. If you haven’t had a chance […]
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Three Things We Learnt from the Queensland Academy of Sport’s Seminar on Athlete Resilience

  • On August 17, 2017
  • challenge, challenge, commitment, commitment, confidence, confidence, control, control, growth, growth, mental toughness, mental toughness, resilience, resilience
On August 8th, the Fusion Sport team headed to the Queensland Academy of Sport to hear about all things mental toughness. An afternoon of frank discussion, we heard from elite athletes and sports psychologists alike about what it takes to succeed at the highest level.   1) Discomfort Breeds Growth Dr David Buttifant — Co-Founder […]
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What You Need to Know From the 2017 Annual Congress of the ECSS

  • On July 18, 2017
  • congress, congress, ECSS 2017, ECSS 2017, research, research, sports science, sports science
We were fortunate enough to attend this year’s 22nd annual Congress of the European College of Sport Science (ECSS) in Essen, Germany. Over four days, an abundance of research was presented covering several disciplines. TIME TO EMBRACE THE COMPLEXITY Although we would generally agree that simple is better in the world of sport science, several […]
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Likert Scales vs. Visual Analogue Scales

  • On July 6, 2017
Here at Fusion Sport, we are always trying to help our clients optimise their data collection strategies. You may have read our previous blog post asking what are the optimal number Likert scale points? But are Likert scales even the best type of survey instrument out there? Some of our clients have scrapped the use […]
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SMARTABASE Research From the Field: A Working Example from an AFL Club

  • On June 9, 2017
  • athlete monitoring, athlete monitoring, injury prediction, injury prediction, smartabase, smartabase, training load, training load
See below, how one of our AFL clients have used SMARTABASE to facilitate quality research that translates to the field.   What question were we trying to answer? Which of our measures, collected weekly, has the greatest association with non-contact injury in-season? (workload, subjective wellness, musculoskeletal screening) Does a combination of these measures allow us […]
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Agree to Disagree: How Many Likert Scale Points are Optimal?

  • On June 2, 2017
Of all the research instruments out there, the Likert scale has to be one of the most popular. In the world of elite sport, Likert scales play a prominent role in wellness monitoring. Many if not most of our clients fill in or analyse a wellness survey every day. Yet, despite the Likert scale’s ubiquity […]
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Missing Data: The Serial Pest

  • On May 11, 2017
  • analysis, analysis, data, model, model, R, R, values, values
Data analysts spend most of their time cleaning data. Data with unexplainable outliers, duplicate entries, erratic recording frequencies, inconsistent units, misspellings and/or data entry errors can make it difficult to perform a good analysis. Especially if you are trying to build an automated data pipeline. But a dataset with > 30% missing values will simply […]
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