# A New Approach to Quantify Soccer Players' Readiness through Machine Learning Techniques
## 1 Introduction  
With the increasing competitiveness of soccer at the elite level and thus increasing in-game and training intensity [1], load and associated player fatigue monitoring have become mainstream. To quantify training load, wearables sensors such as micro electromechanical systems (MEMSs) containing global positioning system (GPS) capabilities and accelerometers are becoming widely used by practitioners. Despite advancements in knowledge regarding external load monitoring, challenges remain regarding the assessment of players' responses to load and associated fatigue in elite soccer environments [2]. Common fatigue monitoring methods in soccer encompass subjective (e.g., wellness questionnaires) and objective measures (e.g., Countermovement Jump [CMJ]) [3]. Although, several issues regarding such tests, such as players' buy-in, lack of a direct effect on on-pitch performance, and the lack of specificity in the tests, remain [4]. One avenue that is currently being explored to improve fatigue monitoring strategies is the use of the triaxial accelerometers within MEMSs to capture the three-dimensional movements that are commonly quantified using a vector termed PlayerLoad(tm) [5, 6]. PL is generally defined as the magnitude of changes in acceleration and has been used as a global indicator of body load or to detect a fatigue condition in soccer players. For example, using PL collected during standardized runs have been shown to be valid, reliable, and sensitive methods to monitor players' neuromuscular status [4; 7]. Such an approach has the advantage of being able to be conducted in a time-efficient manner as well as being task-specific. Although, the integration of such a procedure during warm-ups limit the decisions practitioners can make with respect to potential training modifications after such a test. Instead, [PERSON] et al. [8] monitored PL during standardized small-sided games to potentially detect neuromuscular fatigue. After showing that PL-derived metrics could be used reliably, they observed an increase in PL per minute during small-sided games after a high previous weekly training load. The author advocated that such a change in players' activity might be related to potential fatigue status. Although, such a procedure requires a high degree of standardization (e.g., rules, pitch dimensions) ascertaining the changes observed is not reliant on contextual variables as previous values are used to make comparisons. This standardization is mostly unseen in elite football as tactical and physical needs usually drive the design of these games. As such, there is still a need for more robust monitoring variables and/or analyses that could be used with confidence, irrespective of daily training contexts [9]. In this regard, [PERSON] et al. [9] proposed the notion of assessing the efficacy of a direct comparison between a predictive a model (i.e., multivariate individual regression predicting heart rate responses from external load variables) and the actual value (i.e., heart rate) measured as a surrogate measure of player fitness. An advantage of such an approach is that it allows one to make an assumption about a player's readiness without testing them, therefore overcoming the above-mentioned limitations of player monitoring strategies. While this approach seems promising, it only assesses player fitness. Consequently, developing similar approaches to estimate players' neuromuscular status could be worthwhile. Considering how PL might be affected by player neuromuscular status, it seems pertinent to examine the potential use of this variable in the context of a direct comparison between actual and predictive values. For this reason, the main objective of this study was to develop a novel method to assess player neuromuscular status using machine learning techniques (ML). First, the ability to predict PL based on the external load data collected during training and matches was assessed.