## 2 Materials and Methods
### Feature Engineering  
In addition to the anthropometric players' characteristics (e.g., age, body mass index, and role) and the 88 daily features extracted directly from the GPS data, 182 individual features and 11 contextual features were constructed. Table 1 describes the features used in our study. The individual features reflect the personal characteristics of the players and their training workloads. Note that these features include both the players' workload in the current training session and a summary of his workloads in the previous sessions. All the individual features were normalized using the z-score to reduce intra-subject variability. First, to take into account a player's effort during the previous weeks, the exponential weighted moving average (EWMA) with a span of 7 days for each of the 88 features extracted from GPS data was \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Individual Features**} \\ \hline Personal & Age, height, weight, body mass index (BMI), and role \\ \hline Daily & 88 GPS features of a training/match extracted from the GPS device (i.e., 21 kinematic, \\  & 37 metabolic, and 30 mechanical) \\ \hline ACWR & Acute-chronic workload ratio—ACWR—(i.e., exponential weighted moving average (EWMA) with span = 6/EWMA with span = 28) of the 88 daily features (i.e., 21 kinematic, \\  & 37 metabolic, and 30 mechanical) \\ \hline Acute & Acute workload (EWMA with span = 6) of the 88 daily features (i.e., 21 kinematic, \\  & 37 metabolic, and 30 mechanical) \\ \hline MonotonyWeek & Reflection of training variation across a week (6 days). It is the ratio between training loads (i.e., product between duration and rate of perceived exertion (RPE)) mean performed in one week and its standard deviation. \\ \hline MonotonyMonth & Reflection of training variation across a month (28 days). It is the ratio between training loads (i.e., product between duration and RPE) mean performed in one month and its standard deviation. \\ \hline StrainWeek & Reflection of overall training stress from the week. It is the product between the training loads (i.e., product between duration and RPE) mean and the MonotonyWeek. \\ \hline StrainMonth & Reflection of overall training stress from the week. It is the product between the training loads (i.e., product between duration and RPE) mean and the MonotonyMonth. \\ \hline RPEPreVlayer & Mean of the RPE provided by a player in the previous week (6 days) \\ \hline \multicolumn{2}{c}{**Contextual Features**} \\ \hline Win-Draw-Loss & Results of previous match \\ \hline RPEPreVTeam & Mean of the RPE provided by the team in the previous day \\ \hline RPEPreVRole & Mean of the RPE provided by the players with the same role in the previous day \\ \hline ID training & Seven Boolean features reflect the day of the week when players perform a training (i.e., md = match day, md + 1 = day after a match, md + 2 = two days after a match, md - 4 = four days before a match, md - 3 = three days before a match, md - 2 = two days before a match, and md - 1 = one day before a match) \\ \hline Month & Month when a training was performed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the individual and contextual features extracted from global position system (GPS) data and the players’ personal features collected during the study. computed. The EWMA allows for weighting more workloads performed close to the current training session than workloads performed in days long before the current training session. Second, for each of the 88 daily features extracted from GPS data, the acute:chronic workload ratio (ACWR) and the acute workload were computed [21]. A player's ACWR is defined in the literature as the ratio between his acute workload and his chronic workload. As proposed by [PERSON] et al. [21], the acute workload of a player was computed as EWMA of his workload in the previous 7 days; whereas, the chronic workload of a player was computed as the EWMA of his workload in the previous 28 days [21].