## 3 Results  
Descriptive statistics of RPE, S-RPE, and the GPS features are provided in Supplementary, Figures S1 and S2. For \(\text{T}_{\text{s}}^{\text{RPE}}\), the feature selection process selected 53 features out of 286, whereas for \(\text{T}_{\text{s}}^{\text{S-RPE}}\), it selected only 11 features. Figures S3 and S4 show the importance of the features in the classifiers computed according to a coefficient measuring how much each variable contributes to explain the RPE and the S-RPE, respectively [12]. Although just a small subset of features is selected, in \(\text{T}_{\text{s}}^{\text{RPE}+\text{RFECV}}\) we observe that the information derived directly from the training (i.e., daily workloads features), from the last six days of training (i.e., acute workloads features) and from the discrepancy between acute and chronic workloads (i.e., ACWR workload features) is related to the perceived exertion. The acute features affect the RPE detection (\(1.22\pm 0.49\) absolute weights mean) more than daily and ACWR features (\(1.19\pm 0.59\) and \(1.10\pm 0.58\) absolute weights mean, respectively). These results show that the workloads performed in the previous week are predictive of the effort during the training or match. Moreover, even if the influence of ACWR features are lowest compared to acute and daily ones, these features contribute to increasing the ability of the ML model to predict the internal load, but, as suggested by previous studies, these features could not be used to predict injuries [10; 29]. Furthermore, metabolic features (e.g., time in power zone between 0-5 W/kg and 5-10 W/kg) affect the RPE detection (\(1.27\pm 0.65\) absolute weights mean) more than kinematic (e.g., duration of the training session) and mechanical ones (e.g., time in acceleration zone 0-1 m/s\({}^{2}\)) (\(1.18\pm 0.43\) and \(1.10\pm 0.52\) absolute weights mean, respectively). See Figure S3 for a detailed list and the importance of all the features used in the ML approach. Regarding the ID training feature, in \(\text{T}_{\text{s}}^{\text{S-RPE}+\text{RFECV}}\) it is possible to observe that the information derived directly from the training (i.e., daily workloads features) is the only one related to S-RPE. These results show that the training load (S-RPE) is only affected by the workload performed in that specific session. Moreover, the mechanical features (e.g., distance in acceleration zone 1-2 m/s\({}^{2}\)) affect the S-RPE detection (0.76 \(\pm\) 0.34 absolute weights mean) more than kinematic (e.g., distance per minute during the training session) and metabolic ones (e.g., time in power zone between 0-5 W/kg) (0.59 \(\pm\) 0.01 and 0.30 \(\pm\) 0.09 absolute weights mean, respectively). Figure S5 shows the performance of the classifiers both for T\({}_{\mathrm{s}}\) RPE+RFECV and T\({}_{\mathrm{s}}\)S-RPE+RFECV. The ordinal regressor is the best classifier in describing both the players' RPE and S-RPE. Indeed, the ordinal regressor has lower values of RMSE and MAD compared to the two baselines. A moderate-high correlation (r = 0.70) between the RPEs observed, and RPEs predicted by the ordinal regressor was found. Figure 1a shows that, 72.5% of times, the ordinal regressor reported high RPE values when players perceived less than four of RPE, and 87% of times it reported low RPE values when players perceived more than seven RPE. Similarly, a high correlation (r = 0.84) was detected between the observed S-RPEs and the predicted ones showing that 50.8% of times the ordinal regressor overestimated (i.e., the algorithm provided an S-RPE higher than the observed one) the S-RPE classes lower than four and 49.5% of times it underestimated (i.e., the algorithm provided an S-RPE lower than the observed one) the S-RPE classes higher than seven (Figure 1b).