Analysis of professional football players’ sprint performance efficiency in matches

Abstract

The purpose of this study is to analyze the efficiency of the high-intensity exercise performances of professional players during matches. Thirty professional players competed in a total of 38 matches throughout the season. 1,558 sprint efforts were recorded and subsequently analyzed. The sprint efficiency assessment tool ensured good interrater reliability (intraclass correlation coefficient, 0.892; 95% confidence interval, 0.883–0.901) and the validity of the analysis was confirmed by four experts with more than 20 years of experience in football. The total distance and low-intensity running were higher during the first half than during the second half of the season (P<0.01). The positions covering the greatest distances were full back and midfielder. High-intensity running (P<0.05) and sprinting (P<0.01) were higher during home matches than during away matches. The sprint efficiency was higher during the second half than during the first half of the season (P<0.05), but the maximum velocity was higher during the first half of the season (P<0.01). Sprint efficiency was higher for the attack than for the defense and defensive transition situations (P<0.05). Higher duration, distance, and maximum velocity were observed for the excellent efficiency group than for the appropriate group (P<0.05). In conclusion, the findings of this study suggest that the development of effective training programs to enhance high-intensity exercise performance, such as sprinting, requires consideration that goes beyond simply physical speed enhancement to encompass situational judgment, timing, path selection, and speed regulation in an integrated way.

INTRODUCTION

Football players perform various forms of exercise to successfully complete a match. The performance of a football player during a match can be largely divided into offensive (to score a goal) and defensive (to repossess the ball from the opponent). The type of movement required by players during a match may vary depending on the type of tactics pursued by the manager of the team. For example, team players led by a manager who pursues a defensive playing style train to develop movement and performance during defensive situations and effectively play matches using defensive tactics. As such, the movement patterns of players in their playing positions may vary with their tactics. However, football players perform walking, jogging, low- and high-intensity running, and sprinting during matches. The total amount of player movement during the match can exceed 11 km (Joo and Jee, 2019).

There are no unimportant football activities during matches, but the performance of high-intensity exercises has become more important than other movements. Compared with the past, modern football has a greater match speed, and more high-intensity exercises are required because of this change. The amount of high-intensity exercise, including sprinting, during a match has increased relative to the past (Allen et al., 2024). Professional footballers can cover total sprinting distances ranging from 153 to 295 m per match, although this may vary with the position (Gualtieri et al., 2023). While midfielders covered the most high-intensity running distance during matches, sprinting distances (>7 m/sec) were the highest for forwards (202 m), followed by midfielders (158 m) and defenders (151 m) (Kavanagh et al., 2024). Differences in sprinting performance with the playing position were observed for both male and female professional football players (Choi and Joo, 2022). Furthermore, the physical demands on players during a football match differ considerably depending on the phase of play, such as in possession, out of possession, attacking, defending, and transitional phases (Jerome et al., 2024).

However, the amount of simple high-intensity exercise is not an important factor in determining the results of a match or the level of the player. Bradley et al. (2013) reported that players in lower-level teams performed more high-intensity distance exercises than those in higher-level teams. Players must perform their exercises successfully for a team to win a match; for this, the quality may be more important than the amount of high-intensity exercise. Recent studies have therefore adopted a more contextualized approach to sprint analysis, analyzing the tactical and situational contexts in which sprints occur (Caldbeck and Dos’Santos, 2022b; Lobo-Triviño et al., 2024). When the timing and form of high-intensity exercise are effective, players can successfully perform exercises in situations that can determine the result of a match such as scoring a goal or successfully performing a defense in dangerous situations. Coaches in the field believe that quality is more important than the quantity of high-intensity exercise performance, and they organize and guide training programs to increase the movement efficiency of players. To maximize the effectiveness of such training programs, an analysis of sprint efficiency in actual competitive situations must first be conducted, as understanding how effectively players perform sprints under real competitive conditions provides the empirical basis for evidence-based training design. Nevertheless, to the best of our knowledge, no previous study has examined in an organized manner sprint efficiency in professional football from this perspective. Sprint efficiency in this study refers to how effectively a player performs sprints within the context of match situations, encompassing the appropriateness of sprint timing, directionality, and situational relevance during various phases of play. This definition moves beyond mere quantification of sprint output and reflects the qualitative effectiveness of sprint performances as evaluated within their specific match context.

Various studies on the high-intensity movements of players during matches have recently focused on Europe, where football systems have developed and players perform well in matches (Caldbeck and Dos’Santos, 2022a, 2022b; Morgans et al., 2025). Currently, high-intensity exercise performance of players during matches is not limited to simple quantitative analysis; high-intensity exercise has been analyzed in terms of tactics (Morgans et al., 2024). Most coaches and players recognize that the efficiency of high-intensity movements of players during matches is crucial in determining the outcomes; however, no research has been conducted on the efficiency of high-intensity movements in professional football. In the past, these studies were not conducted because of a lack of equipment to accurately analyze the movements of players during matches; however, video-based analysis methods and global positioning system (GPS)-based movement monitoring systems have recently been developed to conduct these studies. Therefore, this study aimed to analyze the efficiency of the high-intensity exercise performances of professional players during matches.

MATERIALS AND METHODS

Participants

Thirty players who participated in 38 rounds of matches from March to November for a K-League 1 team were included in this study (age: 26.1±2.2 years; height: 180.5±6.4 cm; weight: 73.0± 6.6 kg). Data on goalkeeper positions and specific types of exercise performance during matches were excluded from the analysis. Players were provided with explanations of the purpose and process of the study, and their consent was obtained. This study was approved by the Ethics Committee of Kangwon National University (KWNUIRB-2025-04-010), which was conducted in accordance with the Declaration of Helsinki.

Match analysis data

The players played 38 matches and three matches against the same opponent in a league involving 12 teams. The season was divided into the first (19 matches) and second (19 matches) halves based on the summer break. Analysis of the data on the exercise performance of athletes during the match was performed using the method used in our laboratory (Choi and Joo, 2022). Although several data quality limitations are reported in GPS technology when analyzing player movement during a football match, particularly for high-intensity actions such as sprinting and change-of-direction movements (Crang et al., 2021), it is still widely adopted to analyze physical performance in football (Hennessy and Jeffreys, 2018). Due to its applicability in field settings, GPS technology is used to analyze the physical performance of players in the present study. A 10-Hz GPS was used to measure the exercise performance according to the movement type of players during the match. The GPS power was turned on 10 min before the start of the match to check the normal operation of the device, and the players wore GPS-equipped vests immediately before entering the match. Immediately after the match, the power of the GPS was turned off, and data such as the movement distance were downloaded using the dedicated software of the device (Vector Pro, Catapult Sports, Australia). The movement types were walking (0–7.1 km/hr), low-intensity running (7.2–14.3 km/hr), moderate-intensity running (14.4–19.7 km/hr), high-intensity running (19.8–25.1 km/hr), and sprinting (>25.1 km/hr), and the total distance was combined with the distance of all movement types (Mallo et al., 2015).

Players who were replaced during the matches consumed less physical energy than those who were playing as starting members. Therefore, the physical exercise performance of the substitutes was higher for the same playing time zone. Therefore, the exercise performance of the players was analyzed using the data of players who performed for more than 80 min during the match (n=1,558). Players were classified into one of five positions (center back [CB], full back [FB], midfielder [MF], wing forward [WF], and forward [FW]) to examine the differences in exercise performance.

Study procedures

All matches were recorded using a single camera and subsequently synchronized with GPS-derived sprint data within the Focus module of Catapult Pro Video software (Catapult Sports, Australia), following the manufacturer’s recommended procedures. Sprint efforts performed during match play were identified and extracted using the software. The extracted video clips included movement context immediately preceding and following each sprint effort. The efficiency of sprinting was analyzed by four experts with more than 20 years of experience as football players, researchers, and coaches. During the efficiency analysis, a panel of more than two experts reached a consensus score for the sprint exercise performances when there was disagreement. The efficiency of sprinting was analyzed in three stages: excellent (3 points), appropriate (2 points), and insufficient (1 point). Sprint efficiency was graded into three levels based on the evaluation of three performance components (timing, path, and speed control), each assessed relative to the context of the match situation. Sprints were classified as: excellent, where all three components were performed appropriately; appropriate, where two of the three components were performed appropriately; and insufficient, where one or none of the components met the appropriate performance criteria. Interrater reliability among the four expert evaluators was assessed using the intraclass correlation coefficient, yielding an average measure of 0.892 (95% confidence interval: 0.883–0.901), which is interpreted as good interrater reliability (Koo and Li, 2016).

Sprint performance was determined based on whether the team had a ball (running with ball, over/underlapping, push up pitch, break into box, running in behind/penetrate, moving to receive/exploit space, and supporting play) or not (closing down, pressing, intercepting, recovery run, covering, and other) (Ju et al., 2023). Match situations were classified as attack when the team possessed the ball, defense when the opposing team possessed the ball, defensive transition from attack to defense, and offensive transition from defense to attack.

Statistical analyses

All statistical analyses were performed using IBM SPSS Statistics ver. 26.0 (IBM Co., USA). All values are expressed as mean± standard deviation. One-way analysis of variance (ANOVA) with Bonferroni post hoc correction was applied to examine differences among groups based on playing positions, match situations, and sprint efficiency. For pairwise comparisons, differences between groups were analyzed using paired and independent-samples t-tests. Effect sizes were calculated using Cohen d for paired and independent-samples t-tests, and eta-squared (η²) for ANOVAs. All effect sizes are reported with 95% confidence intervals. Statistical significance was set at P<0.05.

RESULTS

Season period

Professional football players moved more than 10 km per match, with the highest rate of low-intensity movements such as walking or jogging (Table 1). The total distance and low-intensity running were higher during the first half than during the second half (P< 0.01). No statistically significant differences were observed, but the first half was higher for most movement categories.

Table 1.

Match performance between first and second period of season

Variable	1st half	2nd half	d	95% CI
TD (m)	10,340.2±826.1	10,124.0±915.4**	0.22	0.04–0.39
Walking (m)	4,296.2±361.0	4,254.8±364.6	0.08	−0.10 to 0.26
Low-intensity running (m)	4,038.6±558.9	3,963.0±576.8**	0.12	−0.06 to 0.29
Moderate-intensity running (m)	1,413.6±284.8	1,340.5±295.5	0.23	0.05–0.40
High-intensity running (m)	482.4±141.6	465.2±136.1	0.11	−0.07 to 0.28
Sprinting (m)	109.4±82.7	100.4±64.6	0.10	−0.08 to 0.27
Sprinting (n)	6.4±4.4	5.9±3.3	0.11	−0.07 to 0.28

Values are presented as mean±standard deviation.

d, Cohen d effect size; CI, confidence interval; TD, total distance.

^**P<0.01, significant difference between the first half- and the second half-season.

Positional performance

Table 2 shows the moving distances of the players based on the classification of their playing positions. The positions covering the greatest distances were FB and MF; FB moved more than CB and WF, and the distance covered for MF was higher than those covered for CB, WF, and FW. The MF position was not higher than the other positions during high-intensity running, and sprinting distance was the lowest. In contrast, the total distance covered was not higher for the WF position than for the other positions. However, the high-intensity running and sprinting distances and frequencies were the highest.

Table 2.

Match performance according to playing positions

Variable	CB	FB	MF	WF	FW	F-value	Eta-squared	95% CI	Post hoc
TD (m)	9,672.8±619.2	10,469.3±838.1	10,745.3±770.8	9,925.4±878.0	10,050.7±772.9	21.8***	0.26	0.16–0.33	A<BC, D<B, DE<C
Walking (m)	4,382.1±252.5	4,272.3±356.4	4,216.4±352.5	4,320.2±469.3	3,911.4±466.2	5.7***	0.08	0.02–0.14	A>CE, B>E, D>E
Low-intensity running (m)	3,702.9±480.1	4,079.5±527.5	4,394.2±464.8	3,535.2±417.5	4,152.5±309.3	28.6***	0.31	0.21–0.39	A<BCE, D<B<C, C>D, D<E
Moderate-intensity running (m)	1,152.9±199.6	1,411.5±200.8	1,598.7±298.0	1,297.8±190.6	1,367.8±233.1	35.5***	0.36	0.26–0.43	A<BCDE, B<C, DE<C
High-intensity running (m)	361.3±93.5	553.8±104.5	467.9±129.5	587.3±119.3	521.3±110.4	35.1***	0.36	0.26–0.43	A<BCDE, B>C, C<D
Sprinting (m)	73.6±43.3	152.2±79.6	68.1±48.0	184.9±76.6	97.6±45.5	35.7***	0.36	0.26–0.44	A<BD, B>CE, D>C, D>E
Sprinting (n)	4.4±2.4	8.5±4.0	4.2±2.6	10.4±4.0	5.7±2.6	35.6***	0.36	0.26–0.44	A<BD, B>CE, D>C, D>E

Values are presented as the mean±standard deviation.

CB, center back; FB, full back; MF, midfielder; WF, wing forward; FW, forward; CI, confidence interval; TD, total distance.

A is CB. B is FB. C is MF. D is WF. E is FW.

^***P<0.001, significant difference between the first half- and the second half-season.

Home and away matches

Performance in home and away matches tended to differ with exercise intensity (Table 3). There was no difference between the total distance and low-intensity exercise performance; however, high-intensity running (P<0.05) and sprinting (P<0.01) were higher during home matches than during away matches.

Table 3.

Match performance between home and away matches

Variable	Home	Away	d	95% CI
TD (m)	10,255.0±893.8	10,212.9±860.0	0.05	−0.20 to 0.30
Walking (m)	4,265.8±379.6	4,287.8±343.8	−0.06	−0.31 to 0.19
Low-intensity running (m)	3,979.8±567.4	4,028.2±570.3	−0.09	−0.33 to 0.16
Moderate-intensity running (m)	1,398.7±301.5	1,352.7±279.7	0.16	−0.09 to 0.41
High-intensity running (m)	493.9±148.9	451.9±123.5*	0.31	0.06–0.55
Sprinting (m)	116.8±81.1	92.3±63.6**	0.34	0.09–0.58
Sprinting (n)	6.5±4.1	5.7±3.5	0.20	−0.05 to 0.45

Values are presented as mean±standard deviation.

d, Cohen d effect size; CI, confidence interval; TD, total distance.

^*P<0.05,

^**P<0.01, significant difference between home and away matches.

Sprint performance

The performance of sprint exercises for seasons, match periods, and home and away matches are shown in Table 4. The sprint duration and distance did not differ with the season. However, the sprint efficiency and maximum velocity for the first and second halves were significantly different. The sprint efficiency was higher during the second half than during the first half of the season (P<0.05), but the maximum velocity was higher during the first half of the season (P<0.01). However, there were no differences in efficiency, duration, distance, or maximum velocity between the first and second halves of the match. The sprint performances of the players during home matches during the season were significantly higher than those during away matches (P<0.05).

Table 4.

Sprint performance between first and second period of season and matches and home and away matches

Variable	Season				Matches				Home and away
	1st half	2nd half	d	95% CI	1st half	2nd half	d	95% CI	Home	Away	d	95% CI
Efficiency	2.4±0.6	2.5±0.7*	−0.09	−0.16 to −0.02	2.5±0.6	2.5±0.7	−0.02	−0.09 to 0.05	2.5±0.6	2.4±0.7#	0.12	0.02–0.22
Duration (sec)	2.3±1.3	2.3±1.2	0.01	−0.06 to 0.08	2.3±1.2	2.3±1.3	−0.04	−0.11 to 0.03	2.4±1.3	2.2±1.2###	0.18	0.08–0.28
Distance (m)	17.3±10.5	17.2±10.1	0.01	−0.06 to 0.08	16.9±10.1	17.4±10.3	−0.03	−0.11 to 0.04	18.0±10.6	16.1±9.6###	0.19	0.09–0.29
Maximal velocity (km/hr)	28.3±1.8	28.0±1.6**	0.10	0.03–0.18	28.2±1.7	28.1±1.7	0.04	−0.03 to 0.11	28.2±1.7	28.0±1.6#	0.12	0.02–0.22

Values are presented as mean±standard deviation.

d, Cohen d effect size; CI, confidence interval.

^*P<0.05,

^**P<0.01, significant difference between the first half- and the second half-season.

^#P<0.05,

^###P<0.001, significant difference between home and away matches.

The sprint efficiency and duration did not differ with the playing position of the players (Table 5). However, significant differences were observed between the groups in terms of distance (P< 0.05) and maximum velocity (P<0.01). The MF position had the shortest sprint distance and slowest speed relative to the other positions.

Table 5.

Sprint performance according to playing positions

Variable	CB	FB	MF	WF	FW	F-value	Eta-squared	95% CI	Post hoc
Efficiency	2.5±0.6	2.5±0.7	2.4±0.7	2.5±0.6	2.5±0.6	1.2	0.00	0.00–0.01	-
Duration (sec)	2.2±1.1	2.4±1.4	2.2±1.1	2.4±1.3	2.3±1.3	1.9	0.00	0.00–0.01	-
Distance (m)	16.7±9.3	18.0±11.1	16.0±9.1	17.8±10.6	17.2±10.5	2.5*	0.01	0.00–0.01	C<B
Maximal velocity (km/hr)	28.2±1.7	28.4±1.8	27.5±1.4	28.4±1.7	28.1±1.5	15.0***	0.04	0.02–0.06	C<ABD

Values are presented as mean±standard deviation.

CB, center back; FB, full back; MF, midfielder; WF, wing forward; FW, forward; CI, confidence interval.

A is CB. B is FB. C is MF. D is WF. E is FW.

^*P<0.05,

^***P<0.001, significant difference among playing positions.

Sprint performance differed with ball possession (Table 6). Sprint performance was higher during possession than during out of possession. In addition, the efficiency, duration, and distance were significantly different between the groups (P<0.05) but maximum velocity was not.

Table 6.

Sprint performance between in and out of possession

Variable	Ball possession
	In possession	Out of possession	d	95% CI
Efficiency	2.6±0.6	2.4±0.7***	0.31	0.23–0.39
Duration (sec)	2.4±1.4	2.2±1.2*	0.08	0.01–0.16
Distance (m)	18.0±11.2	16.8±9.7*	0.08	0.01–0.16
Maximal velocity (km/hr)	28.2±1.7	28.2±1.8	−0.02	−0.10 to 0.06

Values are presented as mean±standard deviation.

d, Cohen d effect size; CI, confidence interval.

^*P<0.05,

^***P<0.001, significant difference between in and out of possession.

By analyzing the differences in sprint performance by dividing the match situation into attack, defense, defensive transition, and offensive transition, slightly different results were obtained based on the variables (Table 7). All variables were highest during the offensive transition situation. Sprint efficiency was higher for the attack than for the defense and defensive transition situations (P< 0.05), but sprint duration and distance were higher for the offensive transition situation (P<0.05).

Table 7.

Sprint performance according to match situations

Variable	Attack	Defense	Defensive transition	Offensive transition	F-value	Eta-squared	95% CI	Post hoc
Efficiency	2.6±0.6	2.4±0.7	2.4±0.7	2.7±0.6	20.5***	0.04	0.02–0.06	A>BC, B<D, C<D
Duration (sec)	2.2±1.2	2.1±1.0	2.6±1.4	2.9±1.7	26.7***	0.05	0.03–0.07	A<CD, B<CD, C<D
Distance (m)	16.5±9.6	15.4±8.1	19.4±11.6	22.1±13.8	26.3***	0.05	0.03–0.07	A<CD, B<CD, C<D
Maximal velocity (km/hr)	28.1±1.6	28.0±1.6	28.3±1.9	28.4±1.7	3.1*	0.01	0.00–0.01	B<D

Values are presented as mean±standard deviation.

CI, confidence interval.

A is attack. B is defense. C is defensive transition. D is offensive transition.

^*P<0.05,

^***P<0.001, significant difference among match situations.

The results of analyzing the efficiency of sprint performance after dividing it into excellent, appropriate, and insufficient groups are presented in Table 8. Higher duration, distance, and maximum velocity were observed for the excellent efficiency group (n=884) than for the appropriate group (n=536) (P<0.05) but not for the insufficient efficiency group (n=138).

Table 8.

Differences in sprint efficiency

Variable	Excellent	Appropriate	Insufficient	F-value	Eta-squared	95% CI	Post hoc
Duration (sec)	2.4±1.3	2.1±1.2	2.3±1.1	11.0***	0.01	0.00–0.03	A>B
Distance (m)	18.2±10.7	15.6±9.4	17.0±9.1	11.3***	0.01	0.00–0.03	A>B
Maximal velocity (km/hr)	28.3±1.7	27.9±1.6	28.2±1.7	12.7***	0.02	0.01–0.03	A>B

Values are presented as mean±standard deviation.

CI, confidence interval.

A is excellent. B is appropriate.

^***P<0.001, significant difference among efficiency points.

DISCUSSION

This study analyzed match performances of football players based on exercise intensity and sprinting performance efficiency during a season. The match performance of the players varied across seasons, with sprinting efficiency further differentiated by season period, movement type, and match situation.

The total distance and sprint performance differed during the first and second halves of the season. The total distance traveled was greater during the first half than during the second. The difference in the total distance and maximum sprint speed may be associated with fatigue accumulated during the second half of the season. The results of previous study indicate that players are exposed to chronic fatigue and take more time to recover during the second half of the season than during the first half (Curzi et al., 2024). In addition, players in the K-League must complete matches in high-temperature and humid environments in the summer, unlike in European football leagues. Exercises in this environment can affect the muscle mass of players. Exercise without sufficient water intake in high-temperature environments causes muscle loss (Hackney et al., 2012), and fat-free mass decreases at the end of the season (Petri et al., 2016). Therefore, differences in exercise performance between the first and second halves can be explained by environmental and physiological changes.

Sprint distance did not differ with the season; however, sprint efficiency tended to differ. The sprint distance and efficiency during the second half should be lower than those during the first half due to the accumulation of fatigue; however, the opposite result is related to the composition of the matches during the season. The last six of the 38 rounds of the K-League were divided into upper and lower splits based on the results of the 32 rounds. Players in the lower and upper split teams may have a high motivation to escape relegation to lower tier leagues and win the league championship trophy. Match importance has been positively associated with player motivation, and physical performance has been shown to be significantly higher in matches of greater competitive importance (Link and de Lorenzo, 2016). Due to the specificity of the matches for the last six rounds, there was no difference between the sprint distances during the seasons, and the efficiency may have been higher during the second half than during the first half. These results support the notion that the importance of a match can have a greater impact on player performance than accumulated fatigue during the second half of the season. Professional football players covered more sprint distances during important matches, and highly skilled players showed a higher awareness of the need for pacing (Link and de Lorenzo, 2016).

In general, the performance of players is higher during home than away matches due to fan support, familiar environments, and short travel distances. Studies across all continents confirm that home teams win more frequently, although the exact advantage varies by country (Nevill and Holder, 1999; Pollard and Gómez, 2014). In this study, the home winning rate (58%) was much higher than that of the away winning rate (38%), and the players showed high exercise performance during high-intensity running, total sprint distance, sprint efficiency, and maximum speed during home matches. This phenomenon may be deeply related to psychological, physiological, and environmental factors in the familiar stadiums mentioned above. The collective efficiency of players is high during home matches and can positively affect their exercise performance (Bray and Widmeyer, 2000).

The total distance covered was highest in the MF position and lowest in the CB position. These results were consistent with those of a previous study (Bradley et al., 2009). Players in the MF position showed a higher rate of low- and moderate-intensity movements; however, high-intensity running and sprinting tended to be relatively lower for them than for those in the other positions. The difference in the degree of movement for the MF position is related to its location in the middle of the attack and defense; therefore, the player has to cover marked distance because of the nature of the position that must be involved in both attack and defense situations, but is less exposed to match situations requiring high-intensity exercise such as sprinting. The total number of sprints per match, average distance, and maximum speed were also the lowest for the MF position than for the other positions. In addition, the degrees of high-intensity running and sprinting were higher for the FB and WF positions although the total distance covered was not high. These results may also be related to the tactical characteristics used by the team, as suggested by a previous study (Morgans et al., 2024). For example, the progress of the attack would have been made in the form of using a wide area, which may have led to these results for the team participating in the study.

During football matches, ball possession is closely related to the attack processes. In this study, all sprint-related factors except the maximum speed were higher with ball possession than without possession. A study of La Liga players showed that players in offensive positions cover longer sprint distances when they possess the ball while those in defensive position cover longer distances when not having possession (Dellal et al., 2011). In this study, we did not analyze positional roles during ball possession. However, it is highly likely that this result was achieved because the team playing style was dominant during matches with attacking play. This is based on the consideration that the team in the current study ranked high on the table.

Sprint efficiency was high during attack situations and transitions from defense to attack. These results may be related to differences in decision-making accuracy during offensive and defensive phases of football matches. A video-based decision-making study revealed that players made more accurate decisions during offensive phases than defensive phases (Machado and da Costa, 2020). In defensive situations, players’ activity may respond based on the offensive movements of the opponent rather than initiating actions proactively, which could influence the efficiency of sprint performances. Therefore, it is plausible that sprint efficiency was higher during attacking phases, potentially reflecting more self-initiated movement patterns; however, as we did not assess movement initiative in the present study, caution should be exercised when generalizing this interpretation. Higher sprint distances were covered during the transition phases than during the offensive and defensive phases. This is supported by a previous study showing that football players perform many sprints in transitional situations during a match (Caldbeck and Dos’Santos, 2022a). The maximum velocity is also higher during the transition phase.

In the sprint efficiency classification, sprint duration, distance, and maximum velocity were all higher for the excellent category than for the appropriate category. The ability to make decisions is very important for accurate exercise performance during football matches (Calle-Jaramillo et al., 2023), and players with high decision-making abilities maintain tactical behavioral efficiency, even in fatigue situations (Dambroz and Teoldo, 2023). Considering that the performance of previous exercises related to goal scoring during football matches is closely related to the performance of high-intensity exercises (such as sprints and jumps) (Faude et al., 2012), the decision on sprint timing, speed, and path is an important factor in football matches. The results of this study imply that performing sprint exercises following a clear situational judgment can increase the likelihood of a successful performance. However, there is a tendency to conduct training to improve physical speed in the field rather than sprint training, considering the situation awareness and decision-making components in the training organization to improve high-intensity exercise performance.

There are several limitations in the present study. First, we analyzed the data from only one professional football team in K-League. Due to differences in tactical structures, coaching philosophies, and physical demands, caution should be exercised when extrapolating these results to broader teams and leagues of professional football players. Second, player physical performance across different exercise intensities was assessed using GPS technology. As mentioned earlier in this study, single GPS-based measurements may be prone to reduced accuracy due to a variety of factors, including signal interference, sampling frequency limitations, and environmental conditions. Additional measurement tools, such as video-based tracking systems, alongside GPS technology should be incorporated in future studies, which would likely yield more precise and reliable data, thereby enhancing the overall validity of the findings. Finally, individual player-related factors such as fatigue and injury status, as well as positional roles within specific tactical formations, were not controlled for in the present study, which may have introduced additional limitations in generalizing the findings to various teams and leagues.

In conclusion, performances of professional football players during matches differed with the season, period, and position. Sprints are affected by various factors such as home matches, ball possession, and match situations. The development of training programs to improve football players’ high-intensity performance should prioritize the integration of timing, path, and speed control factors according to the match situation. Future studies should further examine the relationship between situational awareness and sprint performance.

Funding Statement

This study was supported by the research grant of Kangwon National University in 2024.