## 4 Discussion
was 205.3 \(\pm\) 100.5 m. Moreover, the mean heart rate (HR) during the match was 176.8 \(\pm\) 7.9 bpm (96.3% of HRmax). Average RPE was 15 (in 26 point scale) in both of the first half and the second half [5]. [PERSON] et al., defines load in AF as high--the distance covered over the 50 min of the match was 2984.2 \(\pm\) 56.1 m [5]. In our study it was 2483.14 \(\pm\) 583.64 m to 2911.08 \(\pm\) 828.90 m. These are noticeably higher values comparing to the results of [PERSON] (2020), who made a study on Polish Kestraleague teams and reports values of 661.81 \(\pm\) 389.40 m for the first half and 625.29 \(\pm\) 305.31 m for the second, which equals 1287.1 m on average during a match [4]. The differences between the results of our study and [PERSON]'s study can be caused by different match times (50 vs. 40 min respectively) and including GK position into the analysis, which was made by [PERSON], and which significantly decreases the average results in the study of [PERSON] (2020) (in our study GKs' were excluded from the GNSS analysis). It should be also noted that the requirements of international tournaments may place higher demands on players in relation to league games, which were analyzed by [PERSON]. Unfortunately, the lack of standardization of individual speed zones (speed bands) for the AF analysis makes it impossible to compare them between studies. In the work of [PERSON] et al. (2018) and in the study of [PERSON] (2020), as well as in this study, other speed zones have been defined. According to the authors, it is related to the use of other tools from other manufacturers of GPS or GNSS systems with predefined zones. A similar situation occurs in the interpretation of the distance covered in HIR in the work of [PERSON] et al., presented as (HIR: \(\geq\)13 km/h) [5] in [PERSON]'s (2020) study; HIR was determined to be above 15.00 km/h [4], and in this study sprint is an effort of locomotion with speed \(\geq\)17.1 km/h for at least 1 s.  
[PERSON]'s study analyzed HR response and showed that GKs have significantly lower HRmax and average HR compared to field players and cover significantly less distance than field players [4]. The study of [PERSON] et al. showed that experienced players have higher speed than inexperienced [26], and the study of [PERSON] et al. showed that there is no relationship between hand grip strength and sprint effectiveness in AF players [27]. The study of [PERSON] (2021) showed that there is no statistically significant relationship between upper limb power and max speed [28]. All of the above studies assessed performance parameters, and unlike this present study, aimed to relate them to pain assessment which is considered important in long-term team management, injury risk and many other aspects, such as the psychological. [PERSON] and [PERSON], in their systematic review of scientific articles about AF published in 2016, point to the need for research about the types and causes of injuries [1], which could lead to decreasing the injury incidence, severity and injury burden. To the authors' best knowledge, the present study is the first to analyze pain in AF. Pain is often a signal of overload and can in some cases foretell injury. Injury prevention is important in sport, but it seems to be even more essential in sport for the disabled such as AF. [PERSON] (2014) and [PERSON] (2017) claim that AF can be a way to improve the quality of life of people with amputations [29; 30], but study of [PERSON] et al. (2018) shows overload of the upper limbs and single lower limb in AF [10], and our study shows that this is connected with pain in location of upper and lower limbs. The present study shows that pain monitoring in AF can provide a lot of valuable information for coaches, physiotherapists, doctors, athletes and scientists, letting them improve the players' performance and minimize the negative consequences in everyday living. Precise GNSS analysis of KPIs can bring crucial data needed in programing the training process in AF.