Development of sport-specific performance parameters during international pre-matches among adolescent basketball players

doi:10.21203/rs.3.rs-4364346/v1

Background

Knowledge of physical performances in the light of match statistics could help to discover what skills make a basketball player successful. The present study aimed to investigate how age and gender affected the Game Related Performance variables (GRP) and find out the connections between GRP) data and game-related statistics (GRS).

Method

Seventeen U19, fifteen U16 boy, and thirteen U16 girl players from Hungarian national teams participated in this study. Players’ GRP data and GRS were collected from fifteen international pre-matches during the preparation training camps in 2023. GRP data were monitored with WIMU PRO™, RealTrack Systems SL, which included a Garmin band that monitors heart rate. The GRS were selected from the official box scores of International Basketball Federation. Depending on homogeneity and normality Mann-Whitney U Test, Welch T-Test, and independent-sample T-Test were used to analyse differences between different age groups and genders. The Pearson correlation was used to examine the relationship between the GRS and GRP.

Result

There was a significant difference in max-speed (p = 0.024), playing load/min (p = 0.008), average HR, (p = 0.009), jump/min (p = 0.001), and explosive distance/min (p = 0.001) between U16 girls and boys. The average speed (p = 0.015), maximal acceleration (p = 0.037), High Intensity acceleration/min (p = 0.001), and high-intensity Deceleration/min (p = 0.002) were significantly different between U19 and U16 boys. A weak and medium relationship between the GRS and the GRP measured were found. We concluded that AVGSpeed, MaxSpeed, ExplDis/min, HIAcc/min, and HIDec/min are discriminative physical parameters and the total distance, the number of jumps, and the pulse values are not important parameters in terms of the outcome of the match. The main difference between sexes among adolescent players is the covered distance of higher intensity running. Among male basketball players, the number of high-intensity accelerations and decelerations were the main differences between different age groups.

Conclusion

According to our findings, experts might verify their opinion on the importance of individual physical skills that have a decisive impact on the players' effectiveness.

basketball

sport-specific performance

match statistics

pre-matches

adolescent

Modern basketball requires complex skills. To be successful, basketball players must be physically, mentally, tactically, and technically at a high level (Fort-Vanmeerhaeghe et al., 2016)(Gál-Pottyondy et al., 2021b) (Petway et al., 2020). This complexity makes it very difficult to predict the outcome of basketball matches. With the development of technology, it became possible to accurately measure the physical performance indicators of players during matches. These activity demands and physiological dates are influenced by several variables, including playing level, age, gender, playing position, and geographical location (Stojanović et al., 2018). One measure of success is how high-level the basketball player plays. Based on the literature, the higher dynamics of the game (Stojanović et al., 2018) (Ferioli et al., 2019) (A. Scanlan et al., 2011) and the better movement efficiency (Petway et al., 2020) (Pino-Ortega et al., 2019) make elite players better than lower-level players.

As biological maturity and basketball experience increase, most physical performance indicators are improved among basketball players (Fort-Vanmeerhaeghe et al., 2016l-Pottyondy et al., 2021b) (Sekine et al., n.d.)(Mancha-Triguero et al., 2021) (Kalén et al., 2021)(García et al., 2021)(Ramos et al., 2019)(Mancha-triguero et al., 2020). In adolescents, lower body strength and speed skills seem to be the biggest differences between early-maturing or older individuals and those of their peers (Fort-Vanmeerhaeghe et al., 2016) (Sekine et al., n.d.) (Mancha-Triguero et al., 2021) Research among NBA players showed that players at the age of 19–25 y.o. run the most and the fastest during the matches, but the peak of their efficiency will be observed in later years (Kalén et al., 2021). Garcia F. et al (García et al., 2021) got similar results. Up to the age of 18 y.o., the total distance, the high speed (> 18 km/h) running distance covered in matches, and the speed of deceleration and acceleration were increased. While running volumes decreased, the value of acceleration and deceleration was the same or higher than in the U18 group (García et al., 2021). However, regarding the physical differences, according to Fort-Vanmeerhaeghe et al (Fort-Vanmeerhaeghe et al., 2016) there were no significant differences between players from the U18 and U16 age groups in any game-related statistical performance variables (GRS - field goals, rebound, assist, steals, turnovers, personal fault, fault drawn, efficiency, points, etc.)

However, several field tests are useful and meaningful during monitoring basketball players (Gál-Pottyondy et al., 2021a), and there are only a few articles about the association between match statistics and the field test results (Gál-Pottyondy et al., 2021b). Ramos et al found that agility positively influenced the match performance among U14 basketball players (Ramos et al., 2020). Another Spanish study found that the result of multi-jump tests, the anaerobic capacity, and the agility test had the greatest influence on the competition (Ibáñez et al., 2023). Against these, Fort-Vanmeerhaeghe et al (Fort-Vanmeerhaeghe et al., 2016) concluded that only the assists and steals per game correlated with any physical field tests. This is confirmed by the fact that the most of correlations between NBA combine tests and game-related statistics were small (García-Rubio et al., 2020), only the correlations of wingspan and blocks, and reactive shuttle run, and draft position were considered medium (< I0,30I). Another remarkable funding of their, that the results of NBA Combine tests were not always reflected in the subsequent performance during their first year of competition (García-Rubio et al., 2020).

An important and little-researched topic in basketball practice is how the effectiveness of the players is related to the performance indicators measured in match. A difference was observed in the physical performance of determinative (those who played for more minutes) and less determinative players during the games. Determinative guards tended to present greater values in covered distance, jumps, player load, acceleration, and deceleration than less determinative players (Reina et al., 2020). Only the value of high-intensity distance per minute was greater for the less determinative players (Reina et al., 2020). Franc Garcia et al (García et al., 2022) examined the relationships between game-related physical parameters (GRP – external load: running distance, sprint distance, maximal acceleration/deceleration, number of jumps, etc., and internal load: maximal heart rate, average heart rate, player load, etc. during the match.) and the GRS. They found only a few small correlations between sport-specific external and internal performance indicators and efficiency indexes such as PIR, PTC, and point.

Although several research deal with the evolution of the physical performance of basketball players, it is still not completely clear which are the crucial physical demands of basketball(Russell et al., 2021) and which GRP indicators determine the outcome of matches in different ages and genders. Based on our literature research, there is little and unorganized data that would provide the GRP profile of top basketball players of different genders and age groups. Most of the research examines GRP alone and does not compare the sport-specific efficiency of basketball players. Overall, the principal aim of the present study was to investigate how age and gender affected the GRP among youth, top-level basketball players Our main hypothesis is that GRP will differ between Hungarian national basketball players of different genders and ages. A secondary objective was to evaluate the relationship between GRP and GRS. Our secondary hypothesis is that there will be a correlation between the GRP and GRS measured in the preparatory matches of the Hungarian national youth teams.

2.1 Participants

Forty-five (seventeen U19 boys (mean [SD]), age: 19.29 [0.32] years, hight:195.25 [6.7] cm, body mass: 84.75 [9.72] kg), fifteen U16 boys, (age:16.43 [0.21] year, hight:192.31 [6.82] cm, body mass: 79.18 [7.46] kg), thirteen U16 girl (age: 16.18 [0.38] year, hight:181.08 [7.5] cm, body mass: 71.4 [8.61] kg)) basketball players from Hungarian national teams participated in the present study. Participants were the best players in their age and gender in Hungary. (Reina et al., n.d.)

Those players who were with the team for 2 weeks of the 5-week preparation were excluded. We monitored fifteen international pre-matches during their preparation training camps. Gameplay was conducted according to official International Basketball Association (FIBA) rules. All players, coaches, and parents were informed both and in written about the research protocol, requisites, benefits, and risks and agreed to participate by providing their written consent prior to the start of the experiment that was executed in accordance with the Declaration of Helsinki. The study was carried out in accordance with the recommendations of The Ethics Committee Hungarian University of Sport Science (TE-KEB/No6/2019) also approved the study’s institutional approval.

2.2 Design

Basketball players' GRP data and individual GRS were collected from fifteen international pre-matches during the preparation training camps of Hungarian national teams in 2023. Four U19 male matches, six U16 male matches and five U16 female matches. Players did not suffer injuries during games, and those who played < 5 minutes of total time were excluded from the analysis of that game, resulting in a total of 167 single records (58 U16 female, 63 U16 male, and 46 U19 male). We divided players into three groups. The first group consisted of U16 girls (n = 13), the second group consisted of U16 boys (n = 15), and the third group consisted of U19 boys (N = 17). We compared the average of the GRP results of the U16 girls and boys and the average of the GRP results of the U16 and U19 boys. We examine the connection between GRP and GRS separately in each group.

2.3 Match analysis

The GRS were selected from the official box scores of FIBA (International Basketball Federation). The following normalized GRS were gathered: field goals/min (FG/min), rebound/min, assist/min, steals/min, turnover/min, personal fault/min (Pfault/min), fault drawn/min (FD/min), efficiency/min (Eff/min), PTS/min (Lorenzo et al., 2010).

2.4. Method

The basketball players' movement, physical, and physiological demands (GRP) were monitored with WIMU PRO™, RealTrack Systems SL, which included a Garmin band that monitors heart rate during home, and international pre-games. With RealTrack System we marked the quarters, and the data used were only included when players were competing on the court. All players wore the same assigned microtechnology instrument (81 × 45 × 15 mm, 70 g) (Bastida-Castillo et al., 2019; Castellano J et al., 2011) which was located in the centre of the player's upper back in an adjustable top. WIMU PRO™ has been used in previous investigations measuring and monitoring basketball players during matches (Ramos et al., 2019; Spiteri et al., 2019). It has good/acceptable accuracy (for the x-position of 5.2 ± 3.1 cm and for the y-position of 5.8 ± 2.3 cm) and inter- and intra-unit reliability for ultra-wideband positioning (for the x-coordinate ICC = 0.65 and for the y-coordinate ICC = 0.85). (Bastida Castillo et al., 2018; Bastida-Castillo et al., 2019). Six antennas, which were placed at fixed points, formed a rectangle outside the basketball court included in the ultrawideband system. The antennas communicate with wireless Bluetooth and were previously calibrated. To determine the exact position of the players (coordinates X and Y) LPS operates using triangulations between the antennas and the units (18 Hz). The inertial measurement units included four 3D accelerometers (100 Hz sample frequency), a 3D magnetometer (100 Hz sample frequency), three 3D gyroscopes (100 Hz sample frequency), and a barometer. The system-specific software SPRO™ (version 977, RealTrack Systems) was used to download and analyse external and internal demands data.

Similar to previous research(García et al., 2022; Reina et al., 2020)(Ramos et al., 2019; Spiteri et al., 2019) the following external loads were fixed: jumps/minute (J/min), distance/min (DIS/min), explosive distance/minute (ExplDis/min), average speed (AVG speed), maximal speed (Max speed), maximal acceleration (MAX Acc), maximal deceleration (MAX Dec), number of high intensity (> 3m · s − 2) Accelerations/minute (HIAcc/min) number of high intensity (> 3m · s − 2) decelerations/minute (HIDec/min), Absolute values of external load were normalized by the played minutes [27]. Heart rate data was recorded using a Garmin belt. For measuring internal load, we fixed player load/minute (PL/min), and average heart rate (AVG HR). Table 1 shows the average GRP of each basketball player.

Table 1

Average GRP of each basketball player
Subject	Dis/min (m)	MaxSpeed (km/h)	AVGSpeed (km/h)	MaxHR (beat/min)	AvgHR (beat/min)	MaxAcc (m/s2)	HiAcc/min (count)	MaxDec (m/s2)	HiDec/min (count)	Jumps/min (count)	PL/min (a.u.)	PT (s)	ExplDis/min (m)
U16M_1	231.04	21.78	3.72	202.00	67.20	5.24	2.03	-6.12	1.78	1.37	4.92	5.61	19.55
U16M_2	162.38	22.81	5.05	197.00	77.21	6.09	3.04	-6.97	2.97	1.11	2.89	13.48	15.05
U16M_3	145.61	22.25	5.09	158.80	71.05	6.48	1.84	-5.53	1.56	1.21	2.87	20.82	13.62
U16M_4	180.32	23.93	5.44	193.33	79.81	6.86	2.68	-5.92	2.63	1.06	3.66	20.17	19.84
U16M_5	154.40	23.93	5.31	210.80	78.20	6.48	2.10	-6.62	2.23	1.03	2.60	24.09	16.82
U16M_6	154.51	23.19	5.13	201.80	76.32	7.59	1.94	-7.98	1.71	1.26	2.59	22.72	15.85
U16M_7	174.39	24.37	5.37	192.33	63.99	7.56	3.40	-7.69	3.22	1.09	3.65	15.35	20.12
U16M_8	173.36	19.94	4.23	202.33	73.18	5.81	1.53	-6.28	1.55	1.24	2.99	10.98	12.15
U16M_9	172.14	21.82	4.94	195.00	73.85	8.98	2.90	-4.92	2.03	0.72	3.55	11.80	15.35
U16M_10	147.00	21.51	5.23	209.00	77.67	6.30	1.91	-6.87	1.44	1.19	2.57	22.89	14.34
U16M_11	136.07	18.28	4.42	192.00	78.69	5.18	2.24	-5.48	2.10	0.98	2.30	14.01	11.74
U16M_12	170.14	24.07	5.56	182.80	76.62	7.74	2.65	-7.14	2.32	1.79	2.84	26.58	16.84
U16M_13	202.77	23.30	5.30	203.00	77.33	5.98	2.24	-5.55	1.83	1.19	3.43	14.52	20.88
U16M_14	186.94	23.22	4.90	197.00	71.32	6.64	3.16	-5.88	2.21	0.52	3.52	11.02	15.49
U16M_15	204.44	23.63	5.47	203.25	78.55	5.74	3.18	-5.97	2.64	1.51	4.07	11.11	18.92
U19M_1	185.93	23.54	4.80	192.50	67.80	6.85	3.83	-6.35	2.85	0.97	3.20	13.34	22.69
U19M_2	171.27	25.07	5.18	187.00	77.25	9.38	4.00	-7.01	4.27	1.36	2.89	22.20	16.43
U19M_3	188.92	24.22	5.08	192.00	71.85	7.45	4.32	-7.59	4.13	1.19	2.99	17.79	16.72
U19M_4	177.77	23.06	4.94	207.00	73.79	7.95	3.08	-6.70	3.04	1.09	3.41	22.07	18.08
U19M_5	149.19	21.80	4.52	203.00	72.50	5.73	1.64	-7.31	1.48	0.76	2.19	24.81	11.61
U19M_6	181.72	24.29	4.92	194.00	75.38	8.29	4.98	-8.13	3.44	0.99	3.01	18.75	17.19
U19M_7	185.19	24.69	4.80	194.00	70.27	7.85	4.08	-6.22	2.97	0.83	3.07	12.74	26.04
U19M_8	208.81	22.42	4.65	192.00	58.90	4.70	2.79	-4.48	2.99	2.26	4.33	7.52	28.18
U19M_9	163.57	19.21	4.37	204.00	65.70	4.67	2.99	-6.47	2.50	0.66	3.58	6.03	14.57
U19M_10	242.71	20.76	4.02	184.00	57.33	6.89	6.94	-6.19	4.63	1.09	6.90	4.51	73.56
U19M_11	181.57	25.39	5.06	195.00	75.83	7.22	4.07	-7.32	3.44	1.46	2.85	18.37	16.06
U19M_12	193.21	23.63	4.71	96.50	72.85	7.43	5.55	-7.40	4.28	1.54	3.14	11.06	17.73
U19M_13	187.21	25.53	5.07	193.50	77.73	7.54	5.55	-6.98	5.86	1.09	3.37	17.50	20.62
U19M_14	172.65	22.53	4.79	204.00	73.95	6.73	3.83	-7.30	3.05	2.20	3.40	16.48	17.97
U19M_15	168.85	23.15	5.26	196.00	76.25	5.96	2.81	-5.37	2.62	0.81	2.58	14.31	14.49
U19M_16	203.70	21.52	4.70	217.00	67.13	7.31	7.14	-6.17	7.04	1.79	3.52	12.12	17.19
U19M_17	175.43	23.07	5.05	205.00	68.40	6.48	3.56	-5.09	2.33	1.04	2.81	15.95	12.83
U16F_1	190.51	22.99	5.72	197.00	75.77	6.14	4.68	-5.85	4.32	1.11	3.12	13.26	19.21
U16F_2	166.74	23.85	5.97	202.20	82.40	7.12	3.29	-8.62	3.28	0.72	2.63	20.42	16.75
U16F_3	166.08	24.38	5.52	199.00	76.34	6.26	3.04	-6.48	2.18	1.02	2.74	19.64	14.76
U16F_4	149.80	20.90	5.02	193.60	78.34	6.22	1.68	-6.67	1.22	0.81	2.33	12.78	12.26
U16F_5	149.79	21.10	5.17	196.00	78.46	7.75	2.52	-7.42	1.92	0.75	2.33	19.93	12.40
U16F_6	156.25	19.86	4.83	200.20	72.44	7.01	2.59	-5.89	2.28	1.21	2.99	13.29	15.26
U16F_7	159.61	21.10	5.33	201.40	82.08	6.71	2.92	-5.64	2.72	0.36	2.48	13.09	14.17
U16F_8	134.08	21.59	5.21	201.20	77.58	6.36	2.04	-7.18	1.99	0.40	2.04	18.51	10.34
U16F_9	160.58	20.66	5.54	195.40	75.63	6.15	2.16	-5.75	1.54	0.86	2.50	16.97	13.48
U16F_10	165.84	22.10	5.59	190.00	79.70	6.12	2.82	-7.30	2.99	0.75	2.98	18.44	16.53
U16F_11	152.36	21.84	5.43	188.00	81.38	7.67	3.43	-6.78	3.45	0.90	2.56	19.13	14.59
U16F_12	151.63	21.77	5.43	197.00	84.36	7.29	2.66	-6.99	2.26	0.59	2.74	19.49	13.91
U16F_13	150.25	22.53	5.35	205.60	77.16	8.15	3.03	-8.24	2.42	0.84	2.66	20.57	14.61
Note: Dis/min = distance/min, Max Speed = maximal speed, AVG Speed = average speed, MaxHR = maximal heart rate, AVGHR = average heart rate, MaxAcc = maximal acceleration, HIAcc/min = high intensity acceleration/min, MaxDec = maximal deceleration, HIDec/min = high

During analysis, we don’t divide the match into quarters, because according to Aron T. Scalan et al (A. T. Scanlan et al., 2012) the player's activity demands were unchanged across the quarters.

Table 1. in here

2.4. Statistics

Before the diversity tests, the Shapiro-Wilk statistics and Levene test for equality of variance were calculated for all data. In the case of non-normal distributions (p < 0.05), we use the Mann-Whitney U Test, in the case of non-homogeneous deviation we use the Welch T-Test, and in the case of normal distributions and standard deviations, we use the independent-sample T-Test to analyze differences between different age groups and genders. The Pearson correlation was used to examine the relationship between the GRS and GRP. Statistical significance was set a priori at p < 0.05. The strength of correlation was in absolute terms r = 0.1–0.3 weak r = 0.4–0.6 medium, and r = 0.7–0.9 strong. All statistical analyses were performed using IBM SPSS Statistics (v 20.0, IBM Corp, Armonk, NY, USA).

Table 2 presents the differences that we have detected between genders and Table 3 presents the detected difference between the two age groups. Table 4–6 shows the result of the correlation analysis separately in the three groups (U16F, U16M, U19M).

Table 2

Data and statistical characteristics of GRP of U16 males and females
	U16 Man		U16 Woman
	Mean	Std. Deviation	Mean	Std. Deviation		U	Z	t	df	p	effect size
Distance_1min	173.29	23.94	154.13	15.47	Mann-Whitney	42	-1.732			0.083	-0.354	r
Sprint_maxspeed	22.85	1.05	21.88	1.58	Mann-Whitney	33	-2.252			0.024*	-0.460	r
Sprint_avgspeed	5.12	0.28	5.37	0.27	Mann-Whitney	39	-1.905			0.057	-0.389	r
MaxHR	198.33	5.75	197.45	3.35	T-Test			0.457	22	0.652	0.180	Hedges's correction
AvgHR	74.92	3.83	78.69	2.44	T-Test			-2.879	22	0.009*	-1.135	Hedges's correction
Max_acc	6.54	0.69	6.90	0.65	T-Test			-1.322	22	0.200	-0.521	Hedges's correction
HI_ACC_ABS_1s	2.40	0.43	2.82	0.61	T-Test			-1.962	22	0.063	-0.773	Hedges's correction
Max_decc	-6.44	0.68	-6.89	0.74	T-Test			1.567	22	0.131	0.618	Hedges's correction
INT_DEC_ABS_1s	2.19	0.43	2.45	0.64	T-Test			-1.151	22	0.262	-0.450	Hedges's correction
Jump	1.21	0.18	0.78	0.22	T-Test			5.184	22	< 0.001*	2.043	Hedges's correction
Player_load	3.17	0.45	2.60	0.24	Mann-Whitney	26	-2.656			0.008*	-0.542	r
Playing_time	17.00	5.56	17.32	2.20	Welch T-Test			-0.184	14.373	0.857	-0.144	Glass's delta
Expl_dist_min	16.73	1.61	14.27	1.68	T-Test			3.669	22	0.001*	1.446	Hedges's correction
Note: Dis/min = distance/min, Max Speed = maximal speed, AVG Speed = average speed, MaxHR = maximal heart rate, AVGHR = average heart rate, MaxAcc = maximal acceleration, HIAcc/min = high intensity acceleration/min, MaxDec = maximal deceleration, HIDec/min = high intensity deceleration/min, PL = player load, ExplDis/min = explosive distance/min, SD = standard deviation
=p < .05 *= p < .01

Table 3

Data and statistical characteristics of GRP of U16 and U19 males U16 and U19 males
	U16 Man		U19 Man
	Mean	Std. Deviation	Mean	Std. Deviation		U	Z	t	df	p	effect size
Distance_1min	173.29	23.94	183.35	15.74	Mann-Whitney	49	-1.328			0.184	-0.271076865	r
Sprint_maxspeed	22.85	1.05	23.44	1.31	T-Test			-1.212	22	0.238	-0.478	Hedges's correction
Sprint_avgspeed	5.12	0.28	4.83	0.25	Mann-Whitney	30	-2.425			0.015*	-0.495001052	r
MaxHR	198.33	5.75	198.19	7.20	T-Test			0.053	22	0.958	0.021	Hedges's correction
AvgHR	74.92	3.83	71.95	3.43	Mann-Whitney	42	-1.732			0.083	-0.35354302	r
Max_acc	6.54	0.69	7.17	0.69	T-Test			-2.222	22	0.037*	-0.876	Hedges's correction
HI_ACC_ABS_1s	2.40	0.43	4.29	1.44	Welch T-Test			-4.363	12.951	< 0.001*	-1.315	Glass's delta
Max_decc	-6.44	0.68	-6.79	0.77	T-Test			1.172	22	0.254	0.462	Hedges's correction
INT_DEC_ABS_1s	2.19	0.43	3.74	1.37	Welch T-Test			-3.745	13.187	0.002*	-1.134	Glass's delta
Jump	1.21	0.18	1.23	0.31	T-Test			-0.24	22	0.813	-0.095	Hedges's correction
Player_load	3.17	0.45	3.25	0.66	Mann-Whitney	71	-0.058			0.954	-0.0118392	r
Playing_time	17.00	5.56	16.45	4.64	T-Test			0.265	22	0.793	0.105	Hedges's correction
Expl_dist_min	16.73	1.61	20.00	8.54	Mann-Whitney	50	-1.27			0.204	-0.259237664	r
Note: Dis/min = distance/min, Max Speed = maximal speed, AVG Speed = average speed, MaxHR = maximal heart rate, AVGHR = average heart rate, MaxAcc = maximal acceleration, HIAcc/min = high intensity acceleration/min, MaxDec = maximal deceleration, HIDec/min = high intensity deceleration/min, PL = player load, ExplDis/min = explosive distance/min, SD = standard deviation =p < .05 *= p < .01

Table 4

Pearson Correlations (r) between GRP and GRF among U16 female basketball players (n = 58)
		playing time	FG/min	steal/min	turn over/min	eff/min
PL/min	r	-0.283^*	-0.063	0.247	-0.030	0.161
	p	0.032	0.636	0.061	0.823	0.369
Max Speed (km/h)	r	0.081	-0.128	0.045	0.146	-0.359^*
	p	0.543	0.337	0.735	0.274	0.040
AVG Speed (km/h)	r	0.349^**	0.276^*	0.149	0.099	0.058
	p	0.007	0.036	0.265	0.459	0.750
Max Dec (m/s²)	r	-0.393^**	-0.063	0.114	0.070	0.423^*
	p	0.002	0.641	0.395	0.603	0.014
Note: PT = Playing time, PL/min = player load/min, AVGSpeed = average speed, Max Dec = Maximal Deceleration, FG/min = field goal/min, Eff/min = effectiveness, =p < .05 *= p < .01, °= weak correlation, °° = medium correlation

Table 6

Pearson correlation values (r) between GRF and GRP among U19 male basketball players (n = 48)
		PT	FG/min	rebound/min	assist/min	steal/min	turn over/min	Pfaut/min	PTS/min
Dis/min	r	-0.569^**	0.214	0.317^*	-0.063	0.053	0.331^*	0.249	0.107
	p	< 0.001	0.153	0.032	0.677	0.725	0.025	0.096	0.479
PL/min	r	-0.581^**	0.333^*	0.181	-0.151	-0.055	0.409^**	0.245	.320^*
	p	0.000	0.024	0.228	0.316	0.716	0.005	0.101	0.030
Max Speed (km/h)	r	0.156	-0.062	0.102	0.389^**	0.029	-0.042	-0.217	-0.123
	p	0.300	0.682	0.501	0.008	0.846	0.782	0.148	0.415
AVG Speed (km/h)	r	0.361^*	-0.268	-0.037	0.421^**	0.058	-0.400^**	-0.379^**	-0.217
	p	0.014	0.072	0.809	0.004	0.699	0.006	0.009	0.147
ExplDis/min	r	-0.403^**	-0.162	0.317^*	-0.106	0.301^*	0.175	0.021	-0.199
	p	0.005	0.282	0.032	0.484	0.042	0.243	0.891	0.185
AVG HR (% of player max HR)	r	0.062	0.037	-0.565^**	0.270	-0.114	0.152	0.038	0.177
	p	0.681	0.809	< 0.001	0.070	0.452	0.314	0.801	0.240
Max Acc (m/s²)	r	0.287	-0.036	0.341^*	0.166	0.215	-0.206	-0.084	-0.043
	p	0.053	0.811	0.020	0.270	0.151	0.170	0.580	0.774
HIAcc/min	r	− .360^*	0.092	0.229	0.104	-0.086	0.085	0.316^*	0.043
	p	0.014	0.541	0.126	0.491	0.570	0.572	0.033	0.776
Note: PT = playing time, FG/min = field goal/min, Pfault/in = personal fault/min, Dis/min = distance/min, PL/min = player load/min, Maxspeed = maximal speed, AVGSpeed = average speed, ExplDis/min = Explosive distance/min, AVG HR = average heart rate, Max DAcc = maximal acceleration, HIAcc/min = high-intensity acceleration/min, =p < .05 = p < .01, °= weak correlations, ° °= medium correlations*

There was a significant difference in max-speed (p = 0.024), PL/min (p = 0.008), AVG HR, (p = 0.009), jump/min (p < 0.001), and ExplDis/min (p = 0.001) between U16 girls and boys based on the Mann-Whitney U test, Welch T-test, and T-test tests. (Table 2.)

Table 2. in here

The AVG speed (p = 0.015), MAX Acc (p = 0.037), HIAcc/min (p = < 0.001), and HIDec/min (p = 0.002) were significantly different between U19 and U16 boys. (Table 3)

Table 3. in here

In the case of U16 female players, there was a significant, weak, negative correlation between PL/min and PT (p = 0.032, r=-.283), MAX speed and Eff/min (p = 0.04 r=-0.359), and between MaxDec and PT (p = 0.002, r=-0.393), a significant weak correlation between AVG Speed and PT (p = 0.007, r = 0.349), and significant, medium correlation between MaxDec and eff/min (p = 0.014, r = 0.423). (Table 4)

Table 4. in here

In the case of U16 male players there was a significant, weak correlation between jumps/min and eff/min (p = 0.015, r = 0.388), MaxSpeed and rebound/min (p = 0.007, r = 0.335), and MaxSpeed and assist/min (p = 0.017, r = 0.3), AVGSpeed and PT (p = 0.017, r = 0.301), HIAcc/min and assist/min (p = 0.014, r = 0.308), HIDec/min and assist/min (p = 0.012, r = 0.316), HIDec/min and steal/min (p = 0.004, r = 0.357). There was a significant medium correlation between AVG Speed and rebound/min (p < 0.001, r = 0.484). (Table 5)

Table 5

Pearson correlation values (r) between GRF and GRP among U16 male basketball players (n = 63)
		PT	rebound/min	assist/min	steal/min	turn over/min	fault draw/min	eff/min	PTS/min
jumps/min	r	-0.099	0.241	0.013	0.217	0.167	0.133	0.388^*	0.181
	p	0.439	0.057	0.919	0.088	0.190	0.299	0.015	0.156
PL/min	r	-0.275^*	0.044	0.091	0.003	0.196	-0.192	-0.056	-0.268^*
	p	0.029	0.733	0.479	0.981	0.125	0.131	0.735	0.034
Max Speed	r	0.221	-0.335^**	0.300^*	0.240	0.174	-0.057	-0.181	-0.149
	p	0.082	0.007	0.017	0.058	0.172	0.660	0.269	0.243
AVG Speed	r	0.301^*	-0.484^**	0.238	0.271^*	-0.168	0.008	0.048	0.042
	p	0.017	0.000	0.061	0.032	0.187	0.952	0.772	0.744
AVG HR	r	0.035	-0.095	-0.061	-0.043	-0.295^*	-0.027	-0.043	-0.060
	p	0.783	0.458	0.637	0.737	0.019	0.833	0.796	0.641
Max Dec	r	-0.141	0.006	-0.114	-0.269^*	0.016	-0.179	0.016	-0.108
	p	0.271	0.966	0.373	0.033	0.902	0.160	0.922	0.401
HIAcc/min	r	-0.083	-0.167	0.308^*	0.140	0.056	-0.034	-0.243	-0.192
	p	0.520	0.192	0.014	0.274	0.660	0.794	0.136	0.131
HIDec/min	r	-0.105	− .261^*	.316^*	.357^**	0.106	-0.127	-0.169	-0.175
	p	0.414	0.039	0.012	0.004	0.409	0.319	0.303	0.171
Note: PT = playing time, Eff/min = efficiency/min, PL/min = player load/min, AVGSpeed = average speed, AVG HR = average heart rate, Max Dec = maximal deceleration, HIAcc/min = high-intensity acceleration/min, HIDec/min = high-intensity deceleration/min, =p < .05 *= p < .01, °= weak correlation, °° = medium correlation

Table 5. in here

In the case of U19 male players, there was a significant, weak correlation between Dis/min and rebound/min (p = 0.032, r = 0.317), Dis/min and turn over/min (p = 0.025, r = 0.331), PL/min and FG/min (p = 0.024, r = 0.333), PL/min and PTS/min (p = 0.03, r = 0.320), Max Speed and assist/min (p = 0.008, r = 0.389), AVG Speed and PT (p = 0.014, r = 0.361), ExplDis/min and rebound/min (p = 0.032, r = 0.317), ExplDis/min and Steal/min (p = 0.042, r = 0.301), MaxAcc and rebound/min (p = 0.02, r = 0.341), HIAcc/min and Pfault/min (P = 0.033, r = 0.316). There was a significant negative weak correlation between AVG Speed and Pfault/min (p = 0.009, r=-0.379), HIAcc/min and PT (p = 0.014, r=-0.360). There were significant medium correlations between PL/min and turn over/min (p = 0.005, r = 0.409), AVG Speed and assist/min (p = 0.004, r = 0.421), AVGSpeed and turn over/min (p = 0.006, r = 0.400). There was a significant, negative, medium correlation between ExplDis/min and PT (p = 0.005, r=-0.403). There was a significant, strong negative correlation between Dis/min and PT (p < 0.001, r=-0.569) PL/min and PT (p < 0.001, r=-0.581), AVG Hr and rebound/min (p < 0.001, r=-0.565). (Table 6)

Table 6. in here

In our current research, we sought the answer to how age and gender influence the GRP. Our first hypothesis was that GRP would differ between Hungarian national basketball players of different genders and ages. The hypothesis is true since there was a difference between the GRP of U16 boys and girls and between U16 boys and U19 boys. Our second aim of the current study is to find out if there is any connection between GRP and GRS. Our secondary hypothesis was that there would be a correlation between the GRP and GRS measured in the preparatory matches of the Hungarian national youth teams. We also accepted our second hypothesis as we found significant weak correlations in all genders and age groups between GRP and GRS. According to our results, we stated that measured GRP data are different in different age and gender groups and there are some significant, weak, and medium correlations between GRP and GRS data in all genders and age groups.

Describing the physical demands of basketball players during a competitive game is a very complex and difficult task (Stojanović et al., 2018) (Puente et al., n.d.). Many external and internal factors affect the performance of players. Not only the actual mental and physical conditions of players are determinative, but the quality of the opponent has also affected their performance on the field (Arede et al., 2021). These complexities and the lack of exact measuring protocols in basketball make it difficult to compare funding (Russell et al., 2021).

The differences that we have detected between genders (Table 2) and different age (Table 3) groups can bring us closer to understanding the specific physical requirements of international, top level basketball games. Based on our results, the female was distinguished from the male by max-speed, PL/min, jump/min, ExplDis/min, and AVG HR (Table 2). During matches in the 2023 summer preparation training camp, U16 females and males covered almost the same distance but at different speeds. This funding is consistent with Portes et al (Portes et al., 2020) funding which compares external loads between elite U18 male and female basketball players. They found that males covered significantly larger (p < 0.05) high-intensity and sprint distances and completed more (p < 0.05) decelerations than females. With advancing age, the equality between the sexes in terms of covered distance shifts more and more. Scanlan et al (A. T. Scanlan et al., 2015) and Stojanovic et al (Stojanović et al., 2018) found that female, adult players cover significantly (p < 0,05) bigger distances than males during competitive games.

The number of jumps during games is very varied. In the case of female adult backcourt players, they measured more jump than their male counterparts (A. T. Scanlan et al., 2015). We observed the opposite, in U16 ages since we found no significant difference in this indicator between males and females. This opposite observation may be due to the different nature of adult and youth basketball matches.

In contrast to Stojanovic et al (Stojanović et al., 2018) who stated in their review that females and males have similar Heart Rate (HR) responses during basketball matches, we found that the AVG HR and PL were significantly higher for females (Table 2).

These differences present that the physical requirements of basketball games are strongly different in different playing levels, age groups, and genders. To reveal the differences more precisely between the sexes, it would be necessary to compare the matches in all age groups and genders among Hungarian national teams with the same protocol. According to our research and literature review on U16 and U18 ages, the main differences between sexes are the distance of higher intensity running during basketball matches (Table 2). This result clearly shows that the tempo of male basketball matches is higher even at a young age.

Among males, we found differences in AVG speed, MAX Acc, HIAcc/min, and HIDec/min between the two age groups (Table 3). Arde et al (Arede et al., 2021) examined post-, pre-, and mid-maturated players, and they came to a similar result as us; the pre-maturated players presented significantly (p < 0,05) more accelerations and decelerations, than post- and mid-maturated players. What is more, they found that there were more accelerations and decelerations when the pre-maturated team played against the pre-maturated team than when they played against post- and mid-maturated teams (Arede et al., 2021). Scanlan et al (A. Scanlan et al., 2011) and Ferioli et al (Ferioli et al., 2019) also find that elite players performed significantly more movement changes (p < 0,001), than sub-elite players. among elite players, they detected more high- and moderate-intensity activity during the games (Ferioli et al., 2019). These results are also supported by the fact that U19 male players change of actions every two seconds during competitive games. (Ben Abdelkrim et al., 2007).

We didn’t find significant differences between Dis/min in different age groups (Table 3). The literature is also very divided on this issue. Some previous research in line with our result, reported that no difference was found in the distances covered during matches among different playing levels, and age groups(A. Scanlan et al., 2011) (Oba & Okuda Tomoyasu, 2008), but the opposite can also be found in the literature(Petway et al., 2020)(Stojanović et al., 2018)(Kalén et al., 2021) (García et al., 2021).

According to the literature and our result, a high level of agility is a determinative skill in the U19 and older age group during basketball matches.

A significant weak and medium relationship between the GRS and the GRP measured during the match was found (Table 4–6), which is similar to the results of Franc Garcia et al (García et al., 2022). This is also consistent in that the correlations between physical field tests and game-related statistical indicators have been not clear yet.(Fort-Vanmeerhaeghe et al., 2016)(Gál-Pottyondy et al., 2021b)(García-Rubio et al., 2020)

Based on our results, we wondered whether there is a GRP or GRS parameter that has a significant effect on the other in all three groups (U16 male, U19 male, and U16 female). Discovering this can bring us closer to the key to successful matches. Although Petway et al (Petway et al., 2020) stated that during basketball matches elite player’s AVG speed was lower than the sub-elite and youth, based on our research the AVG speed is the most determinative indicator of the outcome of basketball matches. In the case of all three groups was a significant relationship between a minimum of two GRS indicators and AVGSpeed. (Table 7)

Table 7

Correlations of AVGSpeed with GRF indicators
	Average speed
	U16 M (N = 63)		U19 M (N = 46)		U16 W (N = 58)
	r	p	r	p	r	p
playing time	*0.301°**	0.017	*0.361°**	0.014	0.349°**	0.007
FG/min	0.005	0.966	-0.268	0.072	*0.276°**	0.036
rebound/min	-0.484°°**	< 0.001	-0,037	0.809	-0.106	0.427
steal/min	*0.271°**	0.032	0.058	0.699	0.149	0.265
assist/min	0.238	0.061	0.421°°**	0.004	0.239	0.07
turn over/min	-0.161	0.181	-0.400°°**	0.006	0.099	0.459
Pfault/min	-0.071	0.58	-0.379°**	0.009	0.072	0.594
Note: PT = playing time, FG/min = field goal/minutum, Pfault/min = personal fault/min, =p < .05 *= p < .01, °= weak correlation, °° = medium correlation

Table 7. in here

However elite players and older players have higher MaxSpeed (Petway et al., 2020), it wasn’t in a relationship with any GRS indicator. Among the GRS indicators, PT was the one that showed a correlation with at least one performance indicator in all three groups, and in most cases, it was a negative correlation (Table 8).

Table 8

Correlation of Playing Time with GRP Indicators
	Playing Time
	U16 M (N = 63)		U19 M (N = 46)		U16 W (N = 58)
	r	p	r	p	r	p
AVG Speed	*0.301°**	0.017	*0.361°**	0.014	0.349°**	0.007
PL/min	*-0.275°**	0.029	-0.581°°**	< 0.001	*-0.283°**	0.032
Max Dec	-0.141	0.271	-0.274	0.066	-0.393°**	0.002
Dist/min	-0.183	0.151	-0.569°°**	< 0.001	-0.146	0.274
ExplDis/min	0.032	0.806	-403°°**	0.005	-0.050	0.712
HIAcc/min	-0.083	0.520	*-0.360°**	0.014	0.035	0.792
Note: AVG Speed = average speed, PL/min = player load/min, Max Dec = Maximal deceleration, Dis/min = covered distance/min, explDis/min = covered explosive distance/min, HIAcc/min = number of high intensity acceleration/min, =p < .05 *= p < .01, °= weak correlation, °° = medium correlation

Table 8. in here

From our results, we observed that among male basketball players, the outcome of a basketball match is more influenced by physical performance than female players and with advancing age, more and more emphasis is placed on physicality, since in the case of U19 males, there is the highest correlation between GRP and GRF. Besides that, in the case of U16 girls, few, significant, weak correlations were found, it is important to emphasize that all these correlations related to the speed of the match (MaxSpeed, AVGSpeed, MaxDec) (Table 4). Among U16 males the MaxSpeed, AVGSpeed, and HIDec/min, while among U19 males AVGSpeed, ExplDis/min, and HIAcc were determinative factors of the match outcome (Table 5, 6). This is also consistent with the fact that agility and aerobic capacity had the greatest influence in the competition (Ramos et al., 2020) (Ibáñez et al., 2023) It can be concluded that the total distance, the number of jumps, and the pulse values are not important parameters in terms of the outcome of the match, since these GRP variables weren’t related to GRS indicators in any of the three groups.

Limitation

The number of measurements occasions is only four to six per team because, during the preparation training camp, several matches were abroad where we couldn’t measure the players. For the representative sample, we excluded players who played < 5 minutes of total time and those players who were with the team for only 2 weeks of the 5-week preparation camp. For this reason, we didn’t get 12 samples from all preparation matches.

Summary

Besides that, it is difficult to predict and specifically state which sport-specific physical parameters can decide the match, it is clear from our results that not the quantitative indicators are significant, but the intensity indicators. It can be concluded that AVGSpeed, MaxSpeed, ExplDis/min, HIAcc/min, and HIDec/min are discriminative physical parameters and the total distance, the number of jumps, and the pulse values are not important parameters in terms of the outcome of the match.

According to our research and literature on U16 and U18 ages, the main differences between sexes are the covered distance of higher intensity running during basketball matches. Among male basketball players, the number of high-intensity accelerations and decelerations in other words the agility were the main differences between different age groups.

Based on our literature research, we can say that GRP in connection with GRS has not yet been investigated in elite youth players. Present study show which GRP can be one of the keys to successful performance during matches among basketball players of different ages and genders. Our research provides a protocol for measuring and analysing the sport-specific physical parameters during basketball matches. Our results bring us closer to a more precise understanding of the physical requirements of basketball matches and according to the real physical requirements of competitive matches, trainers and experts could better plan and specialize the basketball training for ages and genders.

Acknowledgments

The authors would like to thank the basketball players of Hungarian national teams for taking part in this study and thank all the staff members who assisted in player monitoring. The authors are grateful to The Hungarian Basketball Federation for providing the equipment and the court.

Author contributions

AGP, and LT; methodology: AGP, RMK and LT; data acquisition: AGP, ÁM and GF; data analyses: AGP, LM and LT resources: RMK; writing-original draft preparation: AGP; writing-review and editing: AGP, LM, RMK and LT; visualization: AGP; supervision: RMK and LT.

Data availability

The datasets generated during and analyzed during the current study are not publicly available due to the large amount of measured data with associated excel files but are available from the corresponding author on reasonable request.

Funding

The research reported in this paper and carried out at BME has been supported by the Hungarian Scientific Research Fund of the National Research, Development, and Innovation Office (NRDI) (Grant No OTKA K 146308 – From pixel to analysis: marker-less sports performance testing on the field)

Ethics approval and consent to participate

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Hungarian University of Sports Science, Budapest (license number TE-KEB/No6/2019). Before the examination all players, coaches, and parents were informed both and in written about the research protocol, requisites, benefits, and risks and agreed to participate by providing their written consent prior to the start of the experiment Written informed consent was obtained from the participants to publish this paper.

Consent for publication

No individual or indemnifiable data is being published as part of this manuscript.

Consent to Participate

Not applicable

Conflict of interest

The authors declare that there is no conflict of interest regarding this submission.

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No competing interests reported.

Development of sport-specific performance parameters during international pre-matches among adolescent basketball players

Status:

Version 1

Abstract

Background

Method

Result

Conclusion

1. Introduction

2. Material and methods

2.1 Participants

2.2 Design

2.3 Match analysis

2.4. Method

2.4. Statistics

3. Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1