## 2 Materials and Methods
### Procedures  
The final sample consisted of 840 match data. All variables were recorded with a 15 Hz GPS system, 100 Hz-10G accelerometer and 50 Hz magnetometer (GPSport, Camberra, Australia) on six 15-min split-time time measurement (i.e., 0-15, 15-30 and 30-45 min and 45-60, 60-75 and 75-90 min, respectively). The sampled match results (7 home and 7 away matches) performance consisted of a total of 11 goals scored and 18 conceded by the sample team (i.e., 2 wins, 5 draws and 7 losses). Only 14 out of the total of 38 matches of the season were analysed. For this reason, the matches prior to the 22 nd match and after the 35 th match, where fulfilling the aim to stay in the same division was mathematically impossible were ignored. After performance tagging at each football match, all GPS data were downloaded to a PC and analysed using GPS software package TeamAMS v4.0 (GPSport, Canberra, Australia).  
Data on the total distance covered during the game and the distance covered in each speed range zone were calculated according to previous studies [11; 34]: standing (Z1; 0-2 km/h); walking (Z2; 2-7 km/h); easy running (Z3; 7-13 km/h); fast running (Z4; 13-18 km/h); high-speed running (Z5; 18-21 km/h); and sprinting (Z6; >21 km/h). Actions above (Z5-Z6) 18 km/h were considered for this study as high-intensity running. The GPS system also provided information about the number, time and distance of the sprints. Moreover, GPS devices registered the maximum acceleration peaks and the number of accelerations of the players in different ranges of intensity used in previous research [35] and adapted to the semi-professional football players [11]: Zone 1, 1.5-2 m/s\({}^{2}\); Zone 2, 2-2.5 m/s\({}^{2}\), Zone 3, 2.5-2.75 m/s\({}^{2}\); and Zone 4, >2.75 m/s\({}^{2}\).  
Matches were classified in function to outcome to identify the dimensions of physical performance having a membership value (0 scored= (0) zero point of success; 2 scored= (1/3) of success; and 3 scored = (1) one point of success). Fuzzy set \"Success\" is defined over the domain 0 to |C|*3 of possible points for the set of matches C and a membership function as: [success (C) = point (C)/(|C|)*3]. Generalise the function's success described above with W (number of win matches), D (number of drawn matches) and L (number of lost matches). Therefore, |C| = W + D + L, and success is achieved when |C| = (Aw*W + AD*D + AL*L)/(Aw*|C|), with Aw the value for winning match, AD drawing match and AL losing match, such that Aw < AD < AL [36]. The mathematics member function of fuzzy set Success was determined by:  
\[\text{C}=\left\{p_{0},\;p_{1},\;\ldots,\;p_{r}\right\}\left\|p_{i}\in\;\{\text {win},\text{draw},\text{loss}\}\right.\]  
\[\text{Val}(p)=\left\{\begin{array}{ll}3&p=\text{win}\\ 2&p=\text{draw}\\ 0&p=\text{loss}\end{array}\right.\]  
\[\text{Succes}(\text{C})=\frac{\sum_{p\in\text{C}}\text{val}(p)}{\text{val}( \text{win})*|\text{C}|}\]  
Knowing that the attribution of success and failure is difficult to define and depends on the subject's perspective [37], and in accordance with the approaches used in previous research [38], we propose the segmentation of the level of success, measured with the membership function if fuzzy set of success, with three labels: Success (S), Defeat (D) and a third intermediate one (not success, U)  
\[\text{label}(\text{success}(\text{C}))=\left\{\begin{array}{ll}\text{DEFEAT}& \text{success}(\text{C})\leq 0.44\\ \text{UNSUCCESS}&0.44<\text{success}(\text{C})<0.55\\ \text{SUCCESS}&\text{success}(\text{C})\geq 0.55\end{array}\right.\]In the end, the sample was divided taking into consideration this situational variable (score), classified by the match result (winning, losing or drawing) and divided by the six split-time time measurements mentioned above.