# A New Approach to Quantify Soccer Players' Readiness through Machine Learning Techniques
## 6 Discussion
### Relationship between LEI and Period of the Season and Day of the Week  
The LEI was calculated as the difference between the PL value predicted by the ML model and the real PL value (\(\Delta\)PL). Similar to the concept presented in previous studies [9; 22; 29], comparing players locomotor activities (external load) with internal load (e.g., HR) or whole-body load (e.g., PL) can be used to track changes in fitness or locomotor efficiency. In this paper, a positive difference between locomotor activity (predicted PL) compared with actual PL was interpreted as an improvement in locomotor efficiency. In contrast, a negative value was interpreted as a condition in which the player was subjected to a greater body load compared to the cumulated external load. Considering the high individual variability of PL [23], the \(\Delta\)PL was individualized using z-score transformation (LEI). The LEI was analyzed in relation to the period of the season, the day of the week, and in relation to the variation in load in the previous week. We were aware that load management could change in relation to the different styles of the teams and coaches; thus, to remove any confounding factors, we limited these analyses to the first team. Therefore, U19 and U18 data were only used to increase the size of the dataset and the quality of the ML predictions. Regarding the trend of LEI during the season (Figure 4), our results revealed that this index changes according to the period of the season, showing a small to large effect (ES 0.18-0.92). In particular, the lowest values were observed during pre-season (July-August), while the highest values were observed in the period November-December period (95% CI 0.48-0.67; ES = 0.92). The most interesting finding is that the LEI showed a significant reduction at the end of the season (March-April). To the best of our knowledge, few studies have examined the progression of fatigue over an entire season. Similar to our study, [PERSON] et al. [30; 31] found lower values of fatigue and delayed- onset muscle soreness (DOMS) in the mid-season period than in the pre- and end-season periods. In this regard, we can speculate that the low LEI value in the pre-season period is related to the poorphysical conditions that characterize the beginning of the season. During this period, players are exposed to sudden and severe increases in training load, which could lead to acute fatigue [24]. Subsequently, the LEI increases following the players' adaptation to training. Therefore, in the middle of the season, players increase their tolerance to a high workload due to improved physical capacity. Instead, the decrease in LEI at the end of the season might be related to the cumulation of training load, stress, and fatigue over the course of the season and could be used to fine-tune the workload of players in the second part of the season. Changes in the LEI were also investigated with respect to the day of the week (Figure 5). The focus of the first days of the weekly microcycle is generally to determine player overload (Table 2). Particularly, strength-oriented sessions were completed on MD-4, during which a high number of accelerations and decelerations were encouraged, while extensive, metabolic-oriented sessions were carried out on MD-3, with a focus on high intensity running (Table 2). In contrast, during MD-2, the main focus is on active recovery and regeneration, while MD-1 is designed to further favor recovery (using a combination of recovery methods and training load management) and maximize neuromuscular freshness (e.g., explosively in the gym, agility on the pitch, short training session with intensity). It is worth noting that the LEI follows the trend of the weekly training load distribution (ES 0.17-0.81) (Figure 5). The highest LEI values were registered on MD-4, MD-3, and MD-1. On MD-4, the players returned to training after having a recovery session (MD+1) and a day-off (MD+2).