1 14 CHAPTER 1 sports [34], enabling automatic analysis of physical activity during races or match-play [35]. Wearable sensor devices and monitoring systems provide an immense amount of physical activity and physical performance data [27]–[29], which present opportunities to develop knowledge in scientific areas like behavioural science, human movement, and sports science [23], [36] and to translate this knowledge to daily practice. For example, this data can be utilised in lifestyle interventions, rehabilitation programmes, or training programmes for elite athletes. In addition, training logs kept by athletes and coaches provide a wealth of data on physical activity, physical performance, psychological wellbeing, injury, and recovery. Data analytics, a field that encompasses techniques for working with data, such as machine learning and advanced statistics [37], [38], enables the extraction of insights and predictions from the collected data. However, the use of data analytics in behavioural, human movement and sport sciences is limited so far [23], [35]. Also, the application of artificial intelligence (AI) and machine learning (ML) based on wearable data and monitoring systems data in sports is still in its preliminary stage [38][39]. Although data analytics offers opportunities, there are various problems in extracting actionable and meaningful insights and predictions based on physical activity and physical performance data. Four problems can be identified in data analytics of physical activity and physical performance data, creating a data analytics gap. To address these problems, we propose potential solutions for each of the identified problems. The first problem is the limited use of individualised prediction based on personalised data [39]–[41], limiting the provision of meaningful insights and predictions for individuals, athletes and coaches. In order to provide individualised insights and predictions, a boundary condition is that the data contains sufficient personal information. A potential solution to the first problem to enable performing data analytics at the individual level using personalised data is to use datasets containing personalised data from wearable sensor devices such as Fitbit or Garmin or optical tracking systems, such as SportsVU, monitoring each individual during their daily life, training or match play, or individual test and exercise log data collected by smart apps such as Sports Tracker or Runkeeper. The second problem is the vast amount and complexity of data. Several authors acknowledged the complexity of data analysis and data analytics in the past decade, given the vast amount of data. For example, Silver’s book on prediction, published in 2012, indicated that realising a correct prediction model, among others in basketball, is challenging because the amount of meaningful information relative to the increasing overall amount of data is declining [41]. In 2014, Davenport concluded that the amount
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