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So I have a research paper due that can be about anything I want. I've been wanting to do one on the NBA, so do you guys have any academic topics I can write about the NBA?
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Sports injuries in basketball players: a systematic review.
2. materials and methods, 2.1. search strategy, 2.2. inclusion and exclusion criteria, characteristics of the studies, 4. discussion, 4.1. injuries relative to gender, 4.2. injuries relative to location, 4.3. basketball injuries relative to the position on the court, 4.4. basketball injuries relative to other sports, 5. conclusions, author contributions, conflicts of interest.
Click here to enlarge figure
First Author and Year | Participants | Localization | Key Findings |
---|---|---|---|
Yde and Nielsen (1990) [ ] | Male and female adolescent athletes participating in three ball sports (soccer, handball, and basketball) n = 302 | Knee Ankle Fingers | The injury rates (number of injuries per 1000 playing hours) were 5.6 for soccer, 4.1 for handball, and 3.0 for basketball. Ankle sprains represented 25% of the injuries, finger sprains 32%, thigh and leg strains 10%, and tendinitis/apophysitis 12%. The most severe injuries included four fractures, one ACL rupture, and two meniscus lesions. Soccer had the most severe injuries, requiring the longest rehabilitation periods. Tackling and contact with opponents were common causes of injury in soccer, while ball contact and running were frequent causes in handball and basketball. |
Prebble et al. (1999) [ ] | Male and female patients with sports-related injuries n > 6000 | Ankle Fingers | A total of 19% were injured playing basketball. A total of 66.4% of the injured individuals were males, and the majority of injuries (53%) happened during school-related activities. A significant percentage (78%) of injuries occurred between the ages of 10 and 19. The most frequently injured body site was the ankle (33.1%), followed by finger injuries (19.3%), with sprains and strains accounting for the majority (55%) of injuries. The most common mechanism of injury (37.4%) involved no contact with other players. The vast majority of injuries (99%) were treated as outpatients. Around 72% of cases were expected to recover within a 2-week period. |
Messina et al. (1999) [ ] | Male and female students; high schools in Texas n = 100 | Knee Ankle | Injury rates were similar between boys (0.56) and girls (0.49), with NS difference in the risk per hour of exposure. Sprains were the most common injuries for both, with the ankle and knee being the most commonly affected areas. Female athletes had a significantly higher rate of knee injuries, including a 3.79-times greater risk of ACL injuries. The risk of injury during games was significantly higher than during practice for both sexes. |
McGuine et al. (2000) [ ] | Male and female high school basketball players n = 210 | Ankle | Subjects with ankle sprains scored 2.01 ± 0.32, while those without scored 1.74 ± 0.31. Higher postural sway indicated more ankle sprains. Poor balance correlated with nearly seven-times more ankle sprains than good balance. |
McKay et al. (2001) [ ] | Male and female basketball players n = 10.393 | Knee Ankle | The overall injury rate was 18.3 per 1000 participations; 24.7 per 1.000 h. Serious injuries, missing a week or more, occurred at 2.89 per 1000, with the ankle being the most common (1.25), followed by the calf/leg (0.48) and knee (0.29). More severe injuries were linked to the lower limb, regardless of competition level, gender, age, height, games played, training, injury type, or injury mechanism. |
Sallis et al. (2001) [ ] | College athletes of both genders in seven similar sports (basketball, cross-country, soccer, swimming, tennis, track, and water polo) n = 3767 | Back/Neck Shoulder Hip Thigh Knee Lower-leg Foot | Injuries were sustained by 45.7% of female athletes and 54.3% of male athletes. NS gender difference was found for injuries per 100 participant-years (52.5 for females vs. 47.5 for males). Significant differences were noted in swimming and water polo: female swimmers had more back/neck, shoulder, hip, knee, and foot injuries, and female water polo players had more shoulder injuries. Overall, female athletes reported higher rates of hip, lower-leg, and shoulder injuries, while male athletes had more thigh injuries. |
Walters (2003) [ ] | Female basketball players in WNBA n = 813 | Knee Ankle | The knee (15.2%), ankle (14.3%), and patella (6.8%) were the most frequently injured body parts. Sprains (28.4%) were the most common injuries, with 49.4% affecting the ankle. Other injuries included tendonitis (19.6%), strains (18.6%), contusions (13.3%), and fractures (4.8%). NS difference in game-related injuries was found among guards, forwards, and centers. The highest injury incidence occurred during defensive rebounding (9.1%), offensive rebounding (6.0%), and driving (5.5%). Overuse/chronic injuries accounted for 20.2% of injuries. Injuries ending the season for the player made up 4.6% of all injuries, and 3.9% required surgery. |
Cumps et al. (2007) [ ] | Male and female senior players of all levels of play n = 164 | Knee Ankle | The incidence of acute injuries was 6.0 per 1.000 h. Ankle sprains accounted for 20.7%, Overuse injury incidence was 3.8/1000. The knee incidence was 1.5/1000. The forward position experiences less knee overuse injuries compared to other positions. Overuse knee injuries and ankle sprains sprains accounted for >14.8%. |
Randazzo et al. (2010) [ ] | Male and female adolescent basketball players with injuries in the period 1997–2007 n = 4,128,852 | Head Upper extremity Trunk Lower Extremity | Injuries occurred in the lower extremity (42.0%), upper extremity (37.2%), the head (16.4%), ankle (23.8%), and finger (20.2%). TBI injuries increased by 70%. Fractures or dislocations are higher in male athletes. TBIs and injury of the knee are higher in female athletes. |
Drakos et al. (2010) [ ] | Male basketball players in NBA n = 1094 | Back Knee Ankle | Lateral ankle sprains accounted for 13.2% of injuries, patellofemoral inflammation accounted for 11.9%, lumbar strains accounted for 7.9%, and hamstring strains accounted for 3.3%. |
Yeh et al. (2012) [ ] | Male basketball players in NBA n = 129 | Knee | Lateral meniscus accounted for 59.7% of injuries and the medial meniscus accounted for 40.3%. Injuries occured in the left and right knee equally. Medial meniscus (>30 years) Lateral meniscs (<30 years). BMI > 25 kg/m increased risk of meniscal tear. BMI < 25 kg/m decreased risk of meniscal tear. 19.4% players did not RTP. |
Owoeye et al. (2012) [ ] | Male and female adolescent basketball players n = 141 | Upper extremity Trunk Lower Extremity | Incidence rate for male atlhetes was 1.1 injuries per match. Incidence rate for female athletes was 0.9 injuries per match. Jumping/landing accounted for 28.1% of injuries, lower extremities 75%, and knee 40.6%. Wrist and fingers, hip, and leg accounted for 3.1% and offensive half of the court accounted for 41%. |
McCarthy et al. (2013) [ ] | Female basketball players with injuries in the period 2000–2008 in WNBA n = 506 | Head Shoulder Hand Knee Ankle | Ankle sprain accounted for 47.8% of injuries, hand injury 20.8%, patellar tendinitis 17.0%, ACL injury 15.0%, meniscus injury 10.5%, stress fracture 7.3%, and concussion 7.1%. |
lei et al. (2013) [ ] | Male and female adolescent basketball players n = 204 | Upper extremity Lower Extremity | Injury incidence in shooting guards was 47.8%, injury incidence in centers was 34.8%, and injury incidence in point guards was 17.4%. |
Ito et al. (2014) [ ] | Male and female basketball players n = 1219 | Upper extremity Lower back Knee Ankle Foot | The knee was the most often injured joint, with the foot and ankle, upper extremities, and lower back following closely behind. Female knee injury accounted for 50.4% of injuries, male knee injury accounted for 41.7% of injuries, female upper extremity injury was 5.1% of injuries, and male upper extremity injury was 9.7%. Most common was ACL injury. Least common was Osgood–Schlatter disease. |
Leppanen et al. (2015) [ ] | Male and female team sports athletes (basketball and floorball players) n = 401 | Head/Neck Upper body Trunk Lower back Hip Thigh Knee | A total of 190 overuse injuries (47.4%); basketball injury incidence was 51%, lower extremities accounted for 66% of injuries, knee 45%, trunk 33%, lower back/pelvis 28%, shin/calf 11.4%, and groin 4%. |
Minhas et al. (2016) [ ] | Male basketball players in NBA n = 129 | Hand/Wrist Knee Achilles tendon | The RTP rates for hand/wrist fractures was 98.1% and for achilles tears was 70.8%. Age ≥30 years and BMI ≥ 27 kg/m were predictors of not RTP. Achilles tendon rupture had a negative effect on career length and performance after recovery. Knee surgeries negatively affects performance after recovery. |
Riva et al. (2016) [ ] | Professional male basketball players n = 55 | Low back Knee Ankle | ↓ in the occurrence of ankle sprains (81%), low back pain ↓ (77.8%), and reduction in knee sprains (64.5%). Enhancements in single-stance proprioceptive control could be crucial for a successful decrease in low back pain, knee sprains, and ankle sprains. |
Pasanen et al. (2017) [ ] | Male and female adolescent basketball players n = 201 | Knee Ankle | Injury incidence was 2.64 per 1000 h, and injury rate was 34.47 in basketball games and 1.51 in team practices. IRR between game and practice was 22.87. Lower limbs accounted for 78%, ankle 48%, knee 15%, and joint or ligaments 67%. NS differences were observed in injury rates between females and males during games and practices. |
Anderson et al. (2019) [ ] | Male and female sports athletes (basketball, lacrosse, and soccer) n = 529 | Knee | Preseason IRR was 1.86, middle regular season IRR was 1.48, late regular season IRR was 1.56, and postseason IRR was 2.20. IRR of 2.18 indicates that female athletes had a greater injury rate than male athletes. Among all ACL injuries, 50% were in basketball players, 24% were in lacrosse athletes, and 26% were in soccer players. Early regular season before halftime IRR was 0.38 and after halftime in the late regular season the IRR was 2.40. |
Rodas et al. (2019) [ ] | Professional male basketball players n = 59 | Muscle and ankle. | |
Patel et al. (2020) [ ] | Male basketball players in NBA n = 65 | Adductor | Guards accounted for 49% of injuries, forwards 25%, and centers 25%, and the adductor re-injury rate was 18.5%. Adductor injuries did not change any statistical parameter; an average of 16–17 days on the court are missed by NBA players after adductor injury. |
Abdollahi and Sheikhhoseini (2022) [ ] | Male basketball players (professional super league and first-divison league) n = 204 | Ankle, Lower Back/Pelvis, Knee, Wrist/Fingers, Shin/Calf | Total of 628 injuries (6.07 injuries/1000 h). Acute ankle injuries accounted for 26.9% of injuries, lower back/pelvis injuries 15.5%, knee injuries 15.7%, wrist/fingers injuries 13.4%, and shin/calf injuries 14.2%. Mean time loss in first division league was 7.84/1000 h exposure, and mean time loss in professional super league was 4.30/1000 h exposure. Injuries during practice were more frequent than during competition. |
Tosarelli et al. (2024) [ ] | Male basketball players (professional European basketball leagues) n = 38 | Knee (ACL) | Injuries while attacking accounted for 69% of injuries and injuries while defending 31%. Direct contact injuries accounted for 3%, indirect contact injuries 58%, and noncontact injuries 39%. Most injuries occurred during offensive cut, landing from a jump, and defensive cut. Most knee injuries occurred during sagittal plane flexion and valgus loading. More injuries were observed during the first ten minutes of a player’s effective playing time, notably in the scoring zone and among guards. |
First Author and Year | Participants | Localization | Key Findings |
---|---|---|---|
Ford et al. (2003) [ ] | Male and female high school basketball players n = 81 | Knee | KMA (3D) examined the valgus knee during DVJ performance; female athletes exhibited more total valgus knee motion and a larger maximum valgus knee angle than males. They also showed significant side-to-side differences in maximum valgus knee angle. Lack of dynamic knee stability, often not assessed before participation, may contribute to higher knee injury rates in females. |
Ford et al. (2005) [ ] | Male and female adolescent middle and high school basketball players n = 126 | Knee Ankle | KMA (3D) examined knee valgus; females showed greater knee valgus angles compared to males. Gender differences also appeared in maximum ankle eversion and inversion during stance. NS differences were found in knee flexion angles. These variations in knee and ankle movements may explain higher ACL injury rates in females. |
Sell et al. (2006) [ ] | Male and female healthy high school basketball players n = 35 | Knee | Jump direction had a major effect on ground reaction forces, joint angles, proximal anterior tibial shear forces and knee joint moments. Female participants demonstrated different KMA, KA, and EMG parameters during jump direction tasks. The direction of the jump greatly affected knee biomechanics. |
Golden et al. (2009) [ ] | Female collegiate basketball athletes n = 13 | Knee | Internal rotation angle in knee was correlated with step width. Peak flexion, knee flexion, and internal rotation are associated with lateral false step. Lateral false step can increase injury risk of ACL. |
Hewet et al. (2009) [ ] | Male basketball players in NBA and female basketball players in WNBA n = 23 | Knee | Injured female athletes demonstrated higher knee abduction and lateral trunk angles compared to male athletes and non injured athletes. |
Wilderman et al. (2009) [ ] | Female intramural basketball players n = 30 | Knee | A 6-week agility program increased hamstring activation during ground contact. Agility training sessions can decrease injury incidence of ACL among female basketball players. |
Koga et al. (2010) [ ] | Female basketball and female handball players n = 10 | Knee | Valgus loading in the knee indicates higher risk for ACL injury. Valgus motion occures 40 miliseconds after ground contact. Vertical ground-reaction force was 3.2 × body weight. |
Munro et al. (2012) [ ] | Female football players and female basketball players n = 93 | Knee | Football and basketball female athletes had higher values for FPAA in SLL than in DJ. Basketball female players demonstrated higher FPPA values during SLL than football female players (ACL injury risk). |
Paz et al. (2016) [ ] | Young male basketball players n = 27 | Knee | Knee valgus angle difference during the DVJ exercise was not found. During FSUP, a difference was observed between the non-dominant and dominant limbs. |
Padua et al. (2019) [ ] | Young female basketball players n = 28 | Ankle | Right ankle dorsiflexion ↑ in EXP. NS improvement was reported in CON group. There was ↑ in left ankle in EXP group. EXP group ↑ ROM in right and left ankle and the COP. Single-leg stance barefoot with eyes closed, triceps sural stretching, and plank forearm position can decrease injuries in ankle area. |
Kamandulis et al. (2020) [ ] | College male basketball players n = 18 | Rectus femoris Semitendinosus Biceps femoris | High-velocity elastic band training improved hamstring strength in male basketball players. High-velocity elastic band training can be used as a tool for injury prevention in hamstrings. |
Morikawa et al. (2023) [ ] | Male basketball players in NBA n = 126 | Shoulder and elbow | Returning from shoulder and elbow problems did not influence shooting accuracy. Significant decline in player efficiency rating after dominant shoulder injury. Elbow or non-dominant shoulder injuries did not affect player efficiency rating. There is a correlation between younger age players and faster return to baseline player efficiency rating after shoulder injury. |
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Aksović, N.; Bubanj, S.; Bjelica, B.; Kocić, M.; Lilić, L.; Zelenović, M.; Stanković, D.; Milanović, F.; Pajović, L.; Čaprić, I.; et al. Sports Injuries in Basketball Players: A Systematic Review. Life 2024 , 14 , 898. https://doi.org/10.3390/life14070898
Aksović N, Bubanj S, Bjelica B, Kocić M, Lilić L, Zelenović M, Stanković D, Milanović F, Pajović L, Čaprić I, et al. Sports Injuries in Basketball Players: A Systematic Review. Life . 2024; 14(7):898. https://doi.org/10.3390/life14070898
Aksović, Nikola, Saša Bubanj, Bojan Bjelica, Miodrag Kocić, Ljubiša Lilić, Milan Zelenović, Dušan Stanković, Filip Milanović, Lazar Pajović, Ilma Čaprić, and et al. 2024. "Sports Injuries in Basketball Players: A Systematic Review" Life 14, no. 7: 898. https://doi.org/10.3390/life14070898
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Zachary d. griffin.
a University of Texas Health Long School of Medicine, San Antonio, Texas, U.S.A.
b Mayo Clinic Alix School of Medicine, Mayo Clinic, Scottsdale, Arizona, U.S.A.
Kade s. mcquivey.
c Department of Orthopedic Surgery, Mayo Clinic, Phoenix, Arizona, U.S.A.
Anikar chhabra, associated data.
To identify the 50 most highly cited research publications in the sport of basketball.
Using the Clarivate Analytics Web of Knowledge database and the search term “basketball”, we identified 2,704 articles. These articles were filtered by the total number of citations and the top 50 most cited articles with a central focus on basketball were selected for this analysis. For each article, we further identified and analyzed author name, publication year, country of origin, journal name, article type, main research topic area, competitive level, gender of study population, and the level of evidence.
Medicine-related topics, particularly those involving knee injuries, are more common than nonmedical topics (coaching, sports psychology etc.) among the highest cited articles. Articles originated from 13 different countries, with 48% originating in the United States. Only four authors had more than one article included in the top 50 most cited articles.
A majority of the top 50 research articles were from English-speaking countries, published after 2000, primarily focused on medicine-related topics, and were Level III evidence. Publications examining knee injuries were the most highly cited and appear to be of high interest to current investigators. The prestige of an author’s name appeared to be less influential to the number of citations.
The top 50 most cited articles list will provide researchers, medical students, residents, and fellows with a foundational list of the most important and influential academic contributions to the basketball literature.
Basketball was invented in 1891 by Dr. James Naismith. 1 Since then, it has slowly spread throughout the world. The International Basketball Federation estimates that there are currently 450 million players and fans of basketball worldwide. 2 In the United States, nearly 40% of children between the ages of 6 and 14 play basketball. 11 million youth between the ages of 12 and 17 participate in the sport, including the 1 million high school students who play for school-sponsored teams. 3 , 4 While basketball provides many benefits to those who participate in it, injuries are common. Yearly, basketball-related injuries result in roughly 340,000 emergency department visits for youth under the age of 24. 5 These injuries often occur in game competition rather than in practice and most commonly effect the lower extremeties. 6
With such a wide influence, basketball has long been a focus of research in the scientific community. Topics of basketball research have included injury prevalence and prevention, player physiology, 7 , 8 biomechanics, 9 , 10 training methods to optimize development, 11 , 12 , 13 and the characteristics of winning teams. 14 , 15 With such a wide scope, the literature on basketball is growing rapidly, and it is nearly impossible to stay up to date on recent publications. Bibliometric analyses provide a helpful way of condensing the ever-growing available research.
Bibliometric citation analyses provide a quantitative method of investigating the impact of scientific articles by analyzing the number of citations. Articles with a high number of citations are often considered the most influential articles in a particular field. 16 Researchers use citation analyses to identify which subtopics are of most interest to fellow investigators, to learn from high-yield articles in an unfamiliar field, and to help students and novice researchers gain foundational knowledge about a topic. These analyses have been widely used in medicine. 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 Similar analyses have been performed in the fields of ecology, 31 biotechnology, 32 , 33 medical education, 34 soccer, 35 and various others. 36 , 37 , 38 , 39 The purpose of this study was to identify the 50 most highly cited research publications in the sport of basketball. The hypothesis of this study was that articles with medicine-related topics would be highly cited.
Given the public nature of these data, institutional review board approval was deemed exempt by our institutional review board. As described in similar studies conducting bibliometric analyses of orthopedic literature, the Clarivate Analytics Web of Knowledge database was used to gather data and metrics. 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 The database was queried on December 14, 2021. All articles containing the word “basketball” in the title were isolated. No date, language, journal, or country of origin restrictions were placed on this search.
This search yielded 2,704 total articles, which were sorted in descending order based on the number of citations. The title and abstract of each article were then reviewed in order to determine its relevance to basketball. Only articles that had basketball as the focus of the study were included. For experimental studies, this was defined as articles that presented independent data for basketball players. If a study was unclear or if there were a question as to whether it should be excluded, the full article was obtained and reviewed by two independent authors to ultimately decide upon inclusion or exclusion. Duplicate studies were removed.
A total of 56 articles were reviewed in order to reach the 50 most cited studies that met the inclusion criteria. These 50 studies were reviewed by two authors to obtain the following information: author name, publication year, country of origin (determined by first author affiliation), journal name, category of research (original research, review article, case study, short communication/technical report, letter to the editors, point-counterpoint, editorial, or thesis), main research topic area (physiology, biomechanics, nutrition, training and testing, sports medicine, performance analysis, sport psychology, coaching, or social sciences), and the level of evidence for clinical articles based on the guidelines published by The Centre for Evidence-Based Medicine (CEBM) . 48 The level of evidence was determined by a consensus opinion between the second and third authors, both of which are medical students with research experience who were instructed on how to classify studies by the physicians on this study. If there was still a question of classification, the senior author, an experienced orthopedic surgeon specialized in sports medicine, was consulted. For articles classified as original research, the type of study (observational or experimental), gender of study participants (male, female, or both), competitive level of participants (elite, non-elite, or both), and the age group of participants (youth, adult, or all) were determined. An elite competitive level included participants who were classified as either semi-professional or professional athletes. Youth, high school, college, and recreational basketball players were classified as non-elite. If an article incorporated a systematic approach to reviewing the literature or if a meta-analysis were performed, the article was classified in the “review article” category. The citation density, which represents the number of citations per year since publication for each of the 50 studies, was also calculated and recorded.
Table 1 lists the 50 most cited articles in basketball research. The number of citations per article ranged from 1,017 to 116. The average number of citations was 242.9, while the median was 164.5. The 50 most cited articles rank, article title, total citations, and citation density are listed in Table 1 . Characteristics of these 50 articles are analyzed in Fig 1 , Fig 2 , Fig 3 , Fig 4 . The number of a articles published by year ( Fig 1 ), the number of citations per year ( Fig 2 ), country of origin ( Fig 3 ), and level of evidence ( Fig 4 ) are highlighted.
Top 50 Most Cited Articles in Basketball
Rank | Article Title | Total Citations | Citation Density |
---|---|---|---|
1 | Arendt E, Dick R. Knee injury patterns among men and women in collegiate basketball and soccer: NCAA data and review of literature. 1995;23:694-701. | 1,017 | 39.12 |
2 | Aglioti SM, Cesari P, Romani M, Urgesi C. Action anticipation and motor resonance in elite basketball players. 2008;11:1109-1116. | 636 | 48.92 |
3 | Plisky PJ, Rauh MJ, Kaminski TW, Underwood FB. Star excursion balance test as a predictor of lower extremity injury in high school basketball players. 2006;36:911-919. | 629 | 41.93 |
4 | Krosshaug T, Nakamae A, Boden BP, et al. Mechanisms of anterior cruciate ligament injury in basketball: Video analysis of 39 cases. 2007;35:359-367. | 626 | 44.71 |
5 | Ford KR, Myer GD, Hewett TE. Valgus knee motion during landing in high school female and male basketball players. 2003;35:1745-1750. | 569 | 31.61 |
6 | Agel J, Arendt EA, Bershadsky B. Anterior cruciate ligament injury in National Collegiate Athletic Association basketball and soccer: A 13-year review. 2005;33:524-530. | 505 | 31.56 |
7 | Koga H, Nakamae A, Shima Y, et al. Mechanisms for noncontact anterior cruciate ligament injuries: Knee joint kinematics in 10 injury situations from female team handball and basketball. . 2010;38:2218-2225. | 416 | 37.82 |
8 | Abdelkrim NB, Fazaa SE, Ati JE. Time–motion analysis and physiological data of elite under-19-year-old basketball players during competition. 2007;41:69-75. | 411 | 29.36 |
9 | McKay GD, Goldie PA, Payne WR, Oakes BW. Ankle injuries in basketball: Injury rate and risk factors. 2001;35:103-108. | 352 | 17.60 |
10 | McGuine T, Greene J, Best T, Leverson G. Balance as a predictor of ankle injuries in high school basketball players. 2000;10:239-244. | 347 | 16.52 |
11 | McInnes SE, Carlson JS, Jones CJ, McKenna MJ. The physiological load imposed on basketball players during competition. 1995;13:387-397. | 316 | 12.15 |
12 | Rozzi SL, Lephart SM, Gear WS, Fu FH. Knee joint laxity and neuromuscular characteristics of male and female soccer and basketball players. 1999;27:312-319. | 292 | 13.27 |
13 | Dirks KT. Trust in leadership and team performance: Evidence from NCAA basketball. J Appl Psychol 2000;85:1004-1012. | 285 | 13.57 |
14 | Mah CD, Mah KE, Kezirian EJ, Dement WC. The effects of sleep extension on the athletic performance of collegiate basketball players. 2011;34:943-950. | 248 | 24.80 |
15 | Bressel E, Yonker JC, Kras J, Heath EM. Comparison of static and dynamic balance in female collegiate soccer, basketball, and gymnastics athletes. 2007;42:42-46. | 236 | 16.86 |
16 | Montgomery PG, Pyne DB, Minahan CL. The physical and physiological demands of basketball training and competition. 2010;5:75-86. | 226 | 20.55 |
17 | Messina DF, Farney WC, DeLee JC. The incidence of injury in Texas high school basketball. 1999;27:294-299. | 223 | 10.14 |
18 | Kujala UM, Taimela S, Antti-Poika I, Orava S, Tuominen R, Myllynen P. Acute injuries in soccer, ice hockey, volleyball, basketball, judo, and karate: analysis of national registry data. 1995;311:1465-1468. | 197 | 7.58 |
19 | Zelisko JA, Noble HB, Porter M. A comparison of men’s and women’s professional basketball injuries. 1982;10:297-299. | 196 | 5.03 |
20 | Ziv G, Lidor R. Physical attributes, physiological characteristics, on-court performances and nutritional strategies of female and male basketball players. 2009;39:547-568. | 194 | 16.17 |
21 | Gray J, Taunton JE, McKenzie DC, Clement DB, McConkey JP, Davidson RG. A survey of injuries to the anterior cruciate ligament of the knee in female basketball players. 1985;6:314-316. | 192 | 5.33 |
22 | Abdelkrim NB, Castagna C, Jabri I, Battikh T, El Fazaa S, El Ati J. Activity profile and physiological requirements of junior elite basketball players in relation to aerobic-anaerobic fitness. 2010;24:2330-2342. | 174 | 15.82 |
23 | Ostojic SM, Mazic S, Dikic N. Profiling in basketball: Physical and physiological characteristics of elite players. 2006;20:740-744. | 168 | 11.20 |
24 | LaBella CR, Huxford MR, Grissom J, Kim K-Y, Peng J, Christoffel KK. Effect of neuromuscular warm-up on injuries in female soccer and basketball athletes in urban public high schools: Cluster randomized controlled trial. 2011;165:1033-1040. | 167 | 16.70 |
25 | Emery CA, Rose MS, McAllister JR, Meeuwisse WH. A prevention strategy to reduce the incidence of injury in high school basketball: A cluster randomized controlled trial. 2007;17:17-24. | 167 | 16.70 |
26 | Sitler M, Ryan J, Wheeler B, et al. The efficacy of a semirigid ankle stabilizer to reduce acute ankle injuries in basketball: A randomized clinical study at West Point. 1994;22:454-461. | 162 | 6.00 |
27 | Manzi V, D’Ottavio S, Impellizzeri F, Chaouachi A, Chamari K, Castagna C. Profile of weekly training load in elite male professional basketball players. 2010;24:1399-1406. | 160 | 14.55 |
28 | Wilson MR, Vine SJ, Wood G. The influence of anxiety on visual attentional control in basketball free throw shooting. 2009;31:152-168. | 156 | 13.00 |
29 | Borowski LA, Yard EE, Fields SK, Comstock RD. The epidemiology of US high school basketball injuries, 2005-2007. 2008;36:2328-2335. | 149 | 11.46 |
30 | Narazaki K, Berg K, Stergiou N, Chen B. Physiological demands of competitive basketball 2009;19:425-432. | 143 | 11.92 |
31 | Matavulj D, Kukolj M, Ugarkovic D, Tihanyi J, Jaric S. Effects of plyometric training on jumping performance in junior basketball players. 2001;41:159-164. | 142 | 7.10 |
32 | Button C, MacLeod M, Sanders R, Coleman S. Examining movement variability in the basketball free-throw action at different skill levels. 2003;74:257-269. | 141 | 7.83 |
33 | Leppanen, M; Pasanen, K; Kujala, UM, et al. Stiff landings are associated with increased ACL injury risk in young female basketball and floorball players. 2017;45:386-393. | 138 | 34.50 |
34 | Simenz CJ, Dugan CA, Ebben WP. Strength and conditioning practices of National Basketball Association strength and conditioning coaches. 2005;19:495. | 137 | 8.56 |
35 | Bourbousson J, Sève C, McGarry T. Space–time coordination dynamics in basketball: Part 2. The interaction between the two teams. 2010;28:349-358. | 136 | 12.36 |
36 | Nemhauser GL, Trick MA. Scheduling a major college basketball conference. 1998;46:1-8. | 134 | 5.83 |
37 | Mihata LCS, Beutler AI, Boden BP. Comparing the incidence of anterior cruciate ligament injury in collegiate lacrosse, soccer, and basketball players: Implications for anterior cruciate ligament mechanism and prevention. 2006;34:899-904. | 131 | 8.73 |
38 | Montgomery PG, Pyne DB, Hopkins WG, Dorman JC, Cook K, Minahan CL. The effect of recovery strategies on physical performance and cumulative fatigue in competitive basketball. 2008;26:1135-1145. | 130 | 10.00 |
39 | Labella CR, Smith BW, Sigurdsson A. Effect of mouthguards on dental injuries and concussions in college basketball. 2002;34:41-44. | 129 | 6.79 |
40 | Hoare DG. Predicting success in junior elite basketball players—the contribution of anthropometic and physiological attributes. 2000;3:391-405. | 129 | 9.92 |
41 | Stojanovic, E; Stojiljkovic, N; Scanlan, AT; Dalbo, VJ; Berkelmans, DM; The activity demands and physiological responses encountered during basketball match-play: A systematic review. 2018;48:111-135. | 129 | 43.00 |
42 | Agel J, Olson DE, Dick R, Arendt EA, Marshall SW, Sikka RS. Descriptive epidemiology of collegiate women’s basketball injuries: National Collegiate Athletic Association Injury Surveillance System, 1988-1989 through 2003-2004. 2007;42:202-210. | 128 | 9.14 |
43 | Dick R, Hertel J, Agel J, Grossman J, Marshall SW. Descriptive epidemiology of collegiate men’s basketball injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2003–2004. 2007;42:194-201. | 128 | 9.14 |
44 | Matthew D, Delextrat A. Heart rate, blood lactate concentration, and time–motion analysis of female basketball players during competition. 2009;27:813-821. | 126 | 10.50 |
45 | Spiteri, T, Nimphius, S, Hart, NH, Specos, C, Sheppard, JM, Newton, RU. Contribution of strength characteristics to change of direction and agility performance in female basketball athletes. 2014;28:2415-2423. | 124 | 17.71 |
46 | Cook JL, Khan KM, Kiss ZS, Griffiths L. Patellar tendinopathy in junior basketball players: A controlled clinical and ultrasonographic study of 268 patellar tendons in players aged 14–18 years. 2000;10:216-220. | 123 | 5.86 |
47 | Longo UG, Loppini M, Berton A, Marinozzi A, Maffulli N, Denaro V. The FIFA 11+ Program is effective in preventing injuries in elite male basketball players: A cluster randomized controlled trial. 2012;40:996-1005. | 123 | 13.67 |
48 | Maffiuletti NA, Gometti C, Amiridis IG, Martin A, Pousson M, Chatard J-C. The effects of electromyostimulation training and basketball practice on muscle strength and jumping ability. 2000;21:437-443. | 122 | 5.81 |
49 | Meeuwisse WH, Sellmer R, Hagel BE. Rates and risks of injury during intercollegiate basketball. 2003;31:379-385. | 118 | 6.56 |
50 | Delorme N, Raspaud M. The relative age effect in young French basketball players: A study on the whole population. 2009;19:235-242. | 116 | 9.67 |
Number of articles published by year. Top 50 most cited basketball articles published by year.
Number of citations by year. Total number of citations generated by the top 50 most cited basketball articles by year.
Number of articles by country of origin. Number of top 50 cited basked articles by the country of origin.
Number of articles versus level of evidence. The number of top 50 basketball articles by level of evidence.
Of the 50 articles shown in Table 1 , 37 articles were original research articles, 12 were review articles, and 1 was a short communication/technical document. Four authors published more than one article included in this list. Three of these authors published two articles (Agel, LaBella, and Abdelkrim), while one author published three (Montgomery). The number of articles published in each journal is listed in Table 2 , while the top 20 most cited articles since 2013 are listed in Table 3 .
The Top 50 Cited Basketball Journals of Origin
Journal of Origin | Number of Articles |
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13 | |
5 | |
4 | |
3 | |
3 | |
2 | |
2 | |
2 | |
2 | |
2 | |
1 | |
1 | |
1 | |
1 | |
1 | |
1 | |
1 | |
1 | |
1 | |
1 | |
1 | |
1 |
The Top 20 Most Cited Basketball Articles Since 2013
Rank | Article Title | Total Citations | Citation Density |
---|---|---|---|
1 | Leppänen M, Pasanen K, Kujala UM, et al. Stiff landings are associated with increased ACL injury risk in young female basketball and floorball players. 2017;45:386-393. . | 138 | 34.50 |
2 | Stojanović E, Stojiljković N, Scanlan AT, Dalbo VJ, Berkelmans DM, Milanović Z. The activity demands and physiological responses encountered during basketball match-play: A systematic review. 2018;48:111-135. . | 129 | 43.00 |
3 | Spiteri T, Nimphius S, Hart NH, Specos C, Sheppard JM, Newton RU. Contribution of strength characteristics to change of direction and agility performance in female basketball athletes. 2014;28:2415-2423. . | 124 | 17.71 |
4 | Amin NH, Old AB, Tabb LP, Garg R, Toossi N, Cerynik DL. Performance outcomes after repair of complete Achilles tendon ruptures in National Basketball Association players. 2013;41:1864-1868. . | 97 | 12.13 |
5 | Siebenrock KA, Behning A, Mamisch TC, Schwab JM. Growth plate alteration precedes cam-type deformity in elite basketball players. 2013;471:1084-1091. . | 98 | 12.25 |
6 | Torres-Unda J, Zarrazquin I, Gil J, et al. Anthropometric, physiological and maturational characteristics in selected elite and non-elite male adolescent basketball players. 2013;31:196-203. . | 108 | 13.50 |
7 | Spiteri T, Newton RU, Binetti M, Hart NH, Sheppard JM, Nimphius S. Mechanical determinants of faster change of direction and agility performance in female basketball athletes. 2015;29:2205-2214. . | 89 | 14.83 |
8 | Scanlan AT, Wen N, Tucker PS, Dalbo VJ. The relationships between internal and external training load models during basketball training. 2014;28:2397-2405. . | 86 | 12.29 |
9 | Sampaio J, McGarry T, Calleja-González J, Sáiz Schelling I Del Alcázar X, Balciunas M. Exploring game performance in the National Basketball Association using player tracking data. 2015;10:e0132894. . | 82 | 13.67 |
10 | Gómez MA, Lorenzo A, Ibañez SJ, Sampaio J. Ball possession effectiveness in men’s and women’s elite basketball according to situational variables in different game periods. 2013;31:1578-1587. . | 88 | 11.00 |
11 | Fox JL, Scanlan AT, Stanton R. A review of player monitoring approaches in basketball: Current trends and future directions. 2017;31:2021-2029. . | 78 | 19.50 |
12 | Conte D, Favero TG, Lupo C, Francioni FM, Capranica L, Tessitore A. Time-motion analysis of Italian elite women’s basketball games: Individual and team analyses. 2015;29:144-150. . | 69 | 11.50 |
13 | Torres-Ronda L, Ric A, Llabres-Torres I, de Las Heras B, Schelling I Del Alcazar X. Position-dependent cardiovascular response and time-motion analysis during training drills and friendly matches in elite male basketball players. 2016;30:60-70. . | 68 | 13.60 |
14 | Schelling X, Torres L. Accelerometer load profiles for basketball-specific drills in elite players. 2016;15:585-591. | 67 | 13.40 |
15 | García J, Ibáñez SJ, De Santos RM, Leite N, Sampaio J. Identifying basketball performance indicators in regular season and playoff games. 2013;36:161-168. . | 74 | 9.25 |
16 | Puente C, Abián-Vicén J, Areces F, López R, Del Coso J. Physical and physiological demands of experienced male basketball players during a competitive game. 2017;31:956-962. . | 65 | 16.25 |
17 | Rodríguez-Rosell D, Mora-Custodio R, Franco-Márquez F, Yáñez-García JM, González-Badillo JJ. Traditional vs. sport-specific vertical jump tests: Reliability, validity, and relationship with the legs strength and sprint performance in adult and teen soccer and basketball players 2017;31:196-206. . | 61 | 15.25 |
18 | Mangine GT, Hoffman JR, Wells AJ, et al. Visual tracking speed is related to basketball-specific measures of performance in NBA players. 2014;28:2406-2414. . | 65 | 9.29 |
19 | Fox JL, Stanton R, Scanlan AT. A comparison of training and competition demands in semiprofessional male basketball players 2018;89:103-111. . | 61 | 20.33 |
20 | Klusemann MJ, Pyne DB, Hopkins WG, Drinkwater EJ. Activity profiles and demands of seasonal and tournament basketball competition. 2013;8:623-629. . | 58 | 7.25 |
Of the 37 original research articles in our study, 16 were experimental, while 21 were observational. In the original research articles, the investigators conducted their research with male participants (17), female participants (6), or a combination of both male and female participants (14). This research investigated both elite and non-elite competition levels, with non-elite settings being more common (22 to 15). The investigators also conducted these studies with youth and adult groups, with 24 of the studies involving adults and 13 involving youth.
The top 50 most cited articles in basketball covered a variety of different topics. The most common topic of research was sports medicine with 20 articles. The second most common area of research was physiology with 11 articles. Other topics included performance analysis (7), biomechanics (4), coaching (2), training and testing (2), sports psychology (2), and social sciences (2). Ten of the top 50 articles focused on knee-related topics. These articles had an average citation density of 22.1. Of the articles with the 9 highest citation densities, 6 analyzed the knee. Anterior cruciate ligament injuries made up 6 of the 10 knee-related articles. Three articles examined ankle injuries and had an average citation density of 11.9. One article focused on oral injuries and had a citation density of 6.32. No publications exclusively investigated injuries of the upper extremities or trunk.
The majority of the research articles included in the top 50 were focused on medicine-related topics. With such a heavy focus on medicine, sports medicine doctors and physicians who treat basketball players could benefit from a study of the articles in our analysis. When the articles related to medicine were analyzed further, it became apparent that interest in knee-related injuries is high. Articles that examined injuries of other parts of the body, including ankle, mouth and shoulder injuries, were less prominent in our analysis. Knee injuries, particularly anterior cruciate ligament injuries, appear to be an area of high interest to current investigators.
Articles in our analysis originated from 13 different countries. Roughly half of all articles were published in the United States, and 35 were published in primarily English-speaking countries. The majority of other countries were European. In comparison, a soccer analysis from Brito et al. found that only 18% of the top 50 articles were published in the United States, while all other articles were published in Europe. 35 These findings may indicate a deficiency in the number of research articles available or simply a lack of interest in research from areas with little to no highly cited publications, such as in Asia. These results may also be explained by basketball’s popularity in a given country.
Finally, the publications included were chosen on the basis of citation numbers alone. This metric, although useful in identifying articles with high levels of influence and utility, is influenced by a variety of factors and should not be used as the sole determinate of article impact. Citation density, among other factors, may be useful to researchers as they seek to obtain the most current view of influential research. Individuals using our analysis should also understand that articles not included in our analysis can be very impactful in their clinical practice and future research, and these articles should be explored further.
A limitation to our study, and to bibliometric analysis in general, is the exclusion of newer publications. Newer publications did not have the time to accrue citations and, thus, were excluded from the top 50 articles. However, our analysis did include citation density statistics, which control for the effect of publication year. Further, we have included a table listing the top 20 most-cited articles since 2012 to include these newer articles. Another potential limitation of our study is that we only used one database. The use of other databases may result in slightly different results than our findings; however the Web of Knowledge database is commonly used in bibliometric analyses. 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47
The authors report the following potential conflicts of interest or sources of funding: A.C. reports receiving grants from Stryker and personal fees from Arthrex, Zimmer-Biomet, and Thrice Medical. Full ICMJE author disclosure forms are available for this article online, as supplementary material .
Dismissing the idea that basketball is a “contactless” sport: quantifying contacts during professional gameplay.
Introduction: Basketball, introduced by Naismith as a contactless and indoor alternative to sports such as American football, now frequently involves physical contact among players, challenging the traditional notion. Up to date, a thorough understanding of these contacts and their implications remains limited. This study aims to analyze player contacts, embedding it within overall load monitoring to optimize performance and reduce injury risk.
Methods: Using a mixed-method design, video-based observations and quantitative analysis were employed to study contact characteristics during ten professional male basketball matches. Fisher exact tests and chi-squared tests ( p < .05) were conducted to examine positional variations across different contact variables.
Results: A total of 2,069 player contacts were examined, showing centers had the most contacts at 40.5%, followed by power forwards (19.6%), point guards (17.7%), shooting guards (12.9%), and small forwards (9.3%). Notably, half-court defense (46.1%) and set offense (48.9%) emerged as the primary game phases associated with the majority of contacts across all playing positions. Key play actions leading to physical contact included screening/picking (25.7%), box outs (22.9%), and fights for position (FFP) (18%). Post hoc analyses identified significant associations between centers (32.6%, 5.93) and point guards (21.5%, −1.98) during screening/picking maneuvers. Moreover, the torso/upper body (48.1%) and upper extremities (38.2%) were identified as the most affected contact points, while lower extremities and the head/neck exhibited minimal impact. Additionally, 81.4% ( n = 1,684) of contacts resulted in kinematic displacement, whereas 18.6% ( n = 385) exhibited no change. Post hoc analyses indicated significant associations of physical contacts against opposing counterparts for each playing position.
Discussion: Basketball entails frequent physical contacts across all playing positions, with distinct patterns observed for each playing position. Integrating contact monitoring alongside traditional load metrics offers a more comprehensive understanding of physical demands in professional basketball. Practical implications include the developing of tailored training strategies based on playing position-specific contact profiles and recognizing the physiological and biomechanical impacts of contacts. Future research should consider whether the number of contacts between players has increased over the years, and it should acknowledge the impact of player contacts on performance in basketball in order to refine training strategies and enhance player well-being.
Basketball was introduced in 1891 as an alternative physical activity to traditional sports such as American football or baseball ( 1 ). It is an intermittent sport characterized by the physical demands of requiring players to execute repeated high-intensity actions ( 2 , 3 ). These actions include rapid changes of direction, jumping, cutting, and high-speed movements over short distances ( 2 , 4 , 5 ). Coaches and staff implement training strategies aiming to enhancing players' performance on the court. Furthermore, training periodization is utilized to monitor and manage players' fatigue levels ( 4 , 6 ). Accordingly, load monitoring plays a pivotal role in furnishing valuable information for the development of training programs and maximizing physical performance while preventing overreaching and reducing injury risk ( 7 – 9 ). Monitoring external and internal loads, both during training and competition, is acknowledged as being crucial for athlete management across different training and competition phases ( 10 – 15 ). Moreover, it is also important to understand the extent to which players are exposed to game-like demands during practice sessions ( 16 – 20 ).
In basketball, the most widely used external load tracking metrics are currently total distance, relative distance (distance/duration), time in speed zones (e.g., total, relative and percentages), high-intensity actions (e.g., time and counts of accelerations, decelerations, jumps, player load metrics), and peak velocity (, 2 , 5 , 13 , 20 ). These are usually measured with high validity using local positioning systems (LPS) ( 21 ), global positioning systems (GPS) ( 22 ), video-based time-motion analysis (TMA) ( 10 , 23 ), or inertial movement analysis (IMA) including tri-axial accelerometry, gyroscope, and magnetometer data ( 13 , 20 , 24 , 25 ). External load variables are useful when considering the role of contextual factors such as gender ( 26 ), team quality ( 27 ), playing position ( 8 , 28 , 29 ), ball possession status ( 30 ), game period ( 3 , 31 , 32 ), game outcome ( 33 ), final score differences (per period) ( 31 ), and accumulated point differences (per period) ( 31 ). Ultimately, the external load (e.g., accelerations) influences the degree of internal load (e.g., cardiovascular or metabolic) that represent the psychobiological response to the stimuli imposed by physical practice and game demands ( 34 ).
While monitoring the external load on basketball players is now common practice, the contribution of physical contacts to the external–internal load relationship is limited. Rice ( 35 ) emphasizes that athletes routinely make contact with each other in basketball, but usually with less force than in typical collision sports such as rugby. Besides the running demands, players frequently engage in quick and forceful physical interactions during key phases of offensive and defensive possession. Thus, repeated physical contacts are fundamental in basketball ( 3 , 8 , 36 , 37 ). For example, when a player posts up, they use their body to establish position close to the basket, often against a defender who is trying to push them away. Similarly, when setting or fighting through screens, players must withstand and apply considerable force to create or prevent scoring opportunities ( 8 ). Also, the importance of boxing out has been a key performance indicator during rebounding ( 38 ). These common play actions during rebounding scenarios involve intense physical contact, as players use their bodies to gain advantageous positioning over opponents.
To date, analyses of contact events in basketball have been largely restricted, with limited information on the frequency and context of impact events. García et al. ( 32 ) noted that guards are typically less involved in scenarios involving high-impact body contact with opponents compared to forwards and centers. In addition, Johnston et al. ( 39 ), focusing on physical contacts during small-sided rugby games reported that contact in game-based activities induces more upper-body neuromuscular fatigue, a greater and longer lasting increase in plasma creatine kinase activity, and an increased perception of effort than game-based activities involving no contact. Importantly, for a comprehensive understanding of overall training load, valid measurements of both the volume and intensity of contacts are essential, because these actions provide a greater subjective, physical, and physiological load than noncontact training or high-intensity intermittent running alone ( 39 ).
The quantification of contacts and their significance for the external–internal load relationship provide valuable information about the physical demands of the playing positions ( 40 ). Guards, for instance, are primarily involved in accelerative and decelerative scenarios, such as perimeter play and one-on-one attacks, which generally involve less physical contact but require higher peak velocities compared to other positions ( 32 ). Forwards, on the other hand, engage less in high-intensity actions ( 41 ) and are frequently involved in physical battles for rebounds and screens, leading to more instances of body contact ( 3 ). Centers often experience the most physical contact as they are typically involved in posting up, boxing out, and protecting the rim ( 42 ). Due to tactical principles, centers usually occupy smaller court dimensions around the basket ( 13 ), resulting in the lowest total distance covered during matches ( 32 ). Conversely, Ferioli et al. ( 8 ) and Svilar et al. ( 43 ) found that centers exhibited the highest number of high-intensity accelerations, jumps, and high-intensity specific movements during training sessions and seasonal games, emphasizing a variance in positional requirements across training and competition modes ( 32 ). The quantification of contact loads (e.g., screens, box outs, post ups) alongside more traditional running metrics (e.g., distances covered) and high-intensity efforts (e.g., accelerations) would offer a more comprehensive picture of the external and internal demands of basketball by considering different playing positions ( 8 , 31 – 33 ). For the best of the authors' knowledge, no previous research has been published related specifically to the quantification of physical contacts and contextual variables in professional basketball.
Therefore, the primary objective of this study is to observe and quantify contacts during professional male basketball matches and to analyze how contact situations are distributed across playing positions. Positional contacts are expected to demonstrate dependencies on situational patterns such as games phase, play action, and the opponent's playing position. By examining the occurrence of contacts, the goal of this study is to enhance the understanding of individual activity profiles and playing positions in the context of athlete monitoring in basketball. Additionally, the study aims to highlight the distinct demands associated with each playing position and provide practical applications for training purposes.
2.1 experimental design.
Following Creswell and Plano Clark's ( 44 ) methodology, this study employed a predetermined fixed mixed-method design. This systematic approach integrates qualitative and quantitative data, with both components predetermined at the beginning of the research process. At first, qualitative data collection enables visual inspection and initial description. Subsequently, a quantitative summary, guided by standardized observational elements, complements prior qualitative insights, aiming to support the overall qualitative estimation. Synthesizing and controlling the data are followed by a conclusive qualitative stage. In this phase, results are presented and interpreted in the context of the previously identified research problem. Merging qualitative and quantitative components establishes methodological symmetry, thereby fostering a comprehensive approach that is deemed advantageous for drawing final conclusions ( 45 ). This interconnection ensures a holistic perspective, promoting a more nuanced and well-rounded understanding of the research phenomena.
A total of 19 players from one team were included in the analyzed cohort over the study period. Players were categorized into five positional groups (defined by the head coach): point guard (PG) ( n = 8, mean age = 23.1 ± 4.6 years, mean height = 190 ± 0.1 cm, mean weight = 82.6 ± 8.1 kg), shooting guard (SG) ( n = 3, mean age, 28.5 ± 8 years, mean height = 1.93 ± 0.3 cm, mean weight = 90.7 ± 5.9 kg), small forward (SF) ( n = 2, mean age, 21.4 ± 2.8 years, mean height = 2.01 ± 0.4 cm, mean weight = 88 ± 8.5 kg), power forward (PF) ( n = 4, mean age, 23.8 ± 4.1 years, mean height = 2.00 ± 0.1 cm, mean weight = 97.3 ± 7.9 kg), and center (C) ( n = 2, mean age, 31.9 ± 1.5 years, mean height = 2.02 ± 0.2 cm, mean weight = 107 ± 5 kg). It should be noted that some players filled more than one playing position. For instance, certain players transitioned from the SF to the PF within specific plays due to tactical decisions (e.g., foul trouble) by the head coach. In such scenarios, these players were categorized differently, reflecting their positional change during the analyzed moves, as opposed to their initial designated playing position. For the analysis, all situational position changes were considered. Also, not all 19 players could be incorporated into the analysis, because some did not receive playing time and/or were unable to play due to injuries. This led to a refined cohort of 9 players who each participated for an average duration of 10 min across all games. Players were routinely filmed during all games in the course of the competitive season. All players confirmed the usage of video material for analytical purposes by contract and all information was publicly available on a streaming service (i.e., MagentaTV). Each participant provided written consent for participation in the study, which was fully conducted according to the principles of the declaration of Helsinki ( 46 ) and approved by the local ethics committee (Grant Number: 2021-30).
Throughout the 2020–2021 German first league season, a longitudinal video-based analysis was conducted within one professional basketball team participating in this national league by two independent raters, possessing a minimum of 20 years of basketball-specific experience in national and international competition formats (DW) and a minimum of ten years in video analysis and game tagging (JJ). Data collection involved systematically quantifying contact actions of each player across ten home games during the season. All contacts were analyzed in real time and, if necessary, by slow motion or frame-by-frame sequencing. Contacts were included in the analysis only if they had a recognizable impact on the game, as defined by Meehan et al. ( 47 ). This included game situations with frequent physical contact resulting from specific basketball movements, such as setting a screen or boxing out during a rebound, or using physical contact to disrupt an opponent's dribble drive, which contain a clear impact among involved players. These scenarios represent frequent game sequences in professional basketball that involve contact but do not constitute targeted collisions. In this context, a distinction was made between recognizable contacts and those in collision sports (e.g., tackling in rugby), where collisions are an integral and expected part of the sport ( 47 ). Furthermore, incidental touches, body stripes, and other non-substantive forms of contact (e.g., cheering or substitution), were not considered.
Preceding the video inspection, an analytical catalogue was formulated by drawing upon insights from prior observational studies in basketball ( 48 ). This catalogue comprised seven overarching items encompassing 40 specific factors [see Table 1 ; for wording, see ( 48 )]. Items I and III classify the positions of players and opponents engaged in contact situations. In cases where there was a change in the opposing player, leading to inconsistent classification, the contact source was categorized as “Other”. Opponents and teammates were identified as contact sources (Item II), whereas “Other” encompassed such contacts as impacts on the floor, court, or basket. Game phases (Item IV) were categorized into four situations covering the majority of basketball scenarios, whereas play actions (Item V) represented various techniques and tactical elements. “Other” play actions included scenarios not clearly assignable to a specific factor (e.g., passes accidentally going into the basket) (see Figure 1 ). Four body areas (Item VI) were defined as points of contact with lower and upper extremities incorporating specific segments. This broader categorization was chosen because isolated labeling of individual segments was not feasible in some scenarios. This is attributed to rapid multi-contact situations (e.g., knee and lower leg) and low contact counts, especially in distal anatomic segments. Item VII segmented contacts into two factors to analyze kinematic displacements occurring during the contact.
Table 1 Items, categories, and factors included for the observation [Modified from ( 48 )].
Figure 1 Percentage of play actions containing contacts per playing position. All observed contacts are included. FFTB, fight for the ball; FFP, fight for position.
To assess the test-retest reliability of the analytical catalogue, a pilot study involving one entire game was conducted. The inter-rater agreement for all items was evaluated over a one-week interval. Using Cohen's kappa ( κ ), the analysis of test-retest-reliability for Rater 1 resulted in “very good” for Item I ( κ = .98), III ( κ = .96), IV ( κ = .97), V ( κ = .93) VI ( κ = .88), VII ( κ = .84) and “good” for Item II ( κ = .79). Rater 2 showed “very good” (Item I, κ = .92; Item III, κ = .89; Item IV, κ = .89; Item V, κ = .92; VI, κ = .84) and “good” (Item II κ = .78; Item VII κ = .78) test-retest-reliability. Inter-rater-agreement resulted in “very good” (Item I, κ = .91; Item III, κ = .94; Item IV, κ = .89; Item V, κ = .91; Item VI, κ = .82) and “good” (Item II, κ = .79; Item VII, κ = .74) concordance. For the final analysis, contact actions were identified and labeled using Focus for teams by SBG Sports Software © and subsequently exported to a separate worksheet using Microsoft Excel (Version 2311). Sequences that were unanalyzable due to inadequate visibility (e.g., concealed by teammates or opponents) on the video were further assessed by both raters. In instances in which contacts were not included, the agreement between the raters was examined. For the final determination, all scenarios were discussed by both raters until an agreement was reached ( 49 ).
To assess observational consistency, the agreement between the two raters for each contact sequence was quantified using κ . Interrater agreement was assessed for each factor listed in Table 1 . These individual values were aggregated to calculate the mean values of the individual items. Threshold values for κ were classified as follows: <.2 (poor), .2–.4 (fair), .4–.6 (moderate), .6–.8 (good), and .8–.0 (very good) ( 50 ). The exploratory data analysis regarding the positional contact count is presented in means and standard deviations. Leven's test for homogeneity of variances ( p = .1), and Shapiro-Wilk test ( p = .06) showed non-significant results, confirming equal variances and normal distribution of the data. Subsequently, a one-way analysis of variance (ANOVA) was conducted to determine significant differences in contact count among different positions. Eta squared ( η ²) was used as a measure of effect size, with thresholds defined by Cohen ( 51 ): small (.01), medium (.06), and large (.14). Post hoc comparisons were conducted using the Tukey Honest Significant Difference (HSD) test to examine specific pairwise differences. Playing-position-specific variations concerning Items II–VII ( Table 1 ) were examined utilizing the chi-square test of association and the Fisher exact test with a post hoc analysis incorporating standardized residuals ( 52 ). The significance level for all statistical tests were set at p < .05. All graphics and statistical analysis were performed using RStudio software ® (Version 4.3.3).
For the ten games the agreement level between both raters could be defined as “very good” for Items I ( κ = 0.98), II ( κ = 0.96), IV ( κ = 0.89), and V ( κ = 0.91). A level of agreement ranging from “good” to “moderate” was observed for Items III ( κ = 0.77), VI ( κ = 0.79), and VII ( κ = 0.58). Out of 2,079 contacts, a total of 10 contacts with a “good” agreement level ( κ = 0.78) could not be identified adequately due to limited visual inspection, resulting in a final 2,069 contacts being included across the ten games. The C ( n = 837, 40.5% of total) received a higher number of contacts than PF ( n = 406, 19.6% of total), PG ( n = 367, 17.7% of total), SG ( n = 267, 12.9% of total) and SF ( n = 192, 9.3% of total). The mean contact count per position across all games is displayed in Figure 2 . ANOVA revealed a significant effect of player position on contact frequency F (4, 45) = 24.95, p < .001, η ² = .69. Post-hoc comparisons showed significant differences in contact among between PF-C, PG-C, SF-C, SG-C (all p < .001) and SF-PF ( p < .005). The C ( n = 837, 40.5% of total) received a higher number of contacts than PF ( n = 406, 19.6% of total), PG ( n = 367, 17.7% of total), SG ( n = 267, 12.9% of total) and SF ( n = 192, 9.3% of total). The Fisher exact test and the chi-square test revealed significant associations between the player's position and the source of contact [ χ ² (8, 2,069) = 15.6, p = .048], the opponent's playing position [ χ ² (20, 2,069) = 301, p < .001], the game phase [ χ ² (12, 2,069) = 175, p < .001], the play action [ χ ² (60, 2,069) = 421, p < .001], and the point of contact [ χ ² (12, 2,069) = 67.2, p < .001]. No significant association was found regarding the form of contact [ χ ² (4, 2,069) = 6.94, p = .139].
Figure 2 Positional average contact count per game over the course of analyzed games. Data are presented as mean values and standard deviation.
Overall, the predominant source of contacts was made by opponent players (97.8%). Falls or contacts with objects on the court (“Other”) ranked as the second most frequent (1.5%), except for SF. Contacts initiated by teammates were the least frequent across all playing positions (0.7%). Figure 3 presents an overview of the opponent's playing position during contact. Interestingly, the highest residual associations for each playing position were identified for the corresponding opposing counterpart (C = 5.89, PF = 10.29, PG = 8.81, SF = 6.8, SG = 5.38, all p < .05). With regard to the observed game phases, the majority of contacts were observed during half-court defense (46.1%) and set offense (48.9%). Fast breaks (2.9%) and transitional defensive phases (2.2%) of the game exhibited significantly fewer contacts between players. Positional post hoc analyses utilizing standardized residuals indicated the most contacts for the C during half-court defense (−9.26), set offense (10.84), and transition defense (−2.51), whereas the SG had significantly more contacts on fast breaks (6.01).
Figure 3 Contact frequency relative to the oppositional position. Data are presented on all contacts for each position.
The observation of different play actions showed that it was predominantly screening/picking ( n = 531, 25.7%), box outs ( n = 474, 22.9%), and fights for position (FFP) ( n = 373, 18%) that led to physical contact, whereas close outs, shot blocking, while “Other” actions (all n = 6, 0.3%) led to contact less frequently. Figure 1 presents a visualization of the relative positional distribution in the aforementioned defined play actions. The visualization indicates that contacts were most frequent during screening/picking, box outs, and FFP. In contrast, the fewest contacts were observed during cutting movements, shot blocking, and close outs. Post hoc analyses and the percentile distribution indicate significant associations between C (32,6%, 5.93) and PG (21.5%, −1.98) contact involvement during screening/picking. Conversely, even though SF and SG had the highest percentage involvement in screening/picking, there was a statistical divergence. Standardized residuals indicated that SF were more likely to experience contact during post ups (−1.62), whereas SG had significantly more contacts during penetration to the basket (5.36). PF also exhibited a deviation between observed and expected values. Whereas they received a higher percentage of contacts during box outs, the standardized residuals, similar to SF, were highest in post ups (5.61).
Regarding contact points, the torso was the most frequently affected area ( n = 996, 48.1%), followed by the upper ( n = 795, 38.4%) and lower extremities ( n = 271, 13.1%), whereas the head/neck ( n = 7, 0.3%) were the least impacted. Figure 4 displays playing position-specific distributions of the contact points. Positional post hoc analysis showed that C and PG had significantly more contacts at the arms (−4.95; 5.56), torso (2.4; −2.95), and legs (4.07; −3.97). Sustained contacts at PF (−2.24) and SG (2.55) showed a significant association with the upper extremities. SF showed no significant differences in terms of contact points. Furthermore, a total of 81.4% ( n = 1,684) of all contacts resulted in a kinematic displacement, whereas the remaining 18.6% ( n = 385) exhibited no change in playing position during the physical contacts between players.
Figure 4 Positional distribution of contacts across body areas. A deeper shade of red indicates a higher frequency of contacts.
The primary objective of this investigation was to conduct a video-based analysis of physical contacts during professional male basketball games. As highlighted by previous research emphasizing the importance of considering contacts during gameplay ( 28 ), our results support the assumption that contacts among players are prevalent across all playing positions in basketball ( Figure 2 ). Notably, the center (C) position emerged as the recipient of the highest frequency of contacts throughout all ten games with over 40% of the total number of contacts. This outcome aligns with findings reported by Ibáñez et al. ( 53 ) and Ribeiro et al. ( 54 ). It should be mentioned that a basketball player's position is influenced predominantly by individual factors such as basketball-specific skills, body height, and body mass, as highlighted by Puente et al. ( 28 ) and Svilar et al. ( 43 ). In conjunction with these considerations, a possible explanation for the increased contact experienced by C may be attributed to their tactical role that often requires them to occupy smaller spaces around the basket. These positional demands for C are confirmed by our results, showing high numbers of box outs (26.6%) and FFP (18.8%), which typically occur close to the basket. Studies by Schelling and Torres ( 13 ) and Vanderlei et al. ( 55 ) posit that C assume responsibility for shots within the key area by engaging in disputes for both defensive and offensive rebounds and executing forceful maneuvers when competing for spatial dominance ( 55 ). A similar explanation is given by Ferioli et al. ( 8 ) positing that C gain possession of the ball by executing rapid and intense movements such as offensive maneuvers to score or secure rebounds. Current studies indicate that C experience a lower physical and physiological demand in terms of overall running movements, accelerations, and decelerations ( 8 , 13 , 32 ). However, there is still a lack of evidence regarding the impact of such contact on external and internal loads in the context of basketball.
In addition, within this cohort, results indicate that each playing position engages predominantly in contacts with its corresponding opposing counterparts. However, there are instances of contact in different playing positions. One plausible explanation for this could be mismatches or tactical maneuvers such as intentional physical interactions during offensive plays. Notably, an exception to this trend is observed in the case of the point guard (PG), who exhibits comparable physical contact frequencies with the C. This pattern may be explained by the strategic deployment of ball screens, a significant facet of gameplay, utilized in most offensive plays in professional basketball ( 56 – 58 ). Our study has shown similar results, indicating a high number contacts during ball screens for all positions ( Figure 1 ). The setting of ball screens involves a collaboration between two players, typically an inside player as the screener and an outside player as the beneficiary ( 56 ). This strategic maneuver is defined as a fundamental technical–tactical element wherein the screener executes a screen to create a favorable situation and advantage for the player with possession of the ball. This advantageous position is sought for purposes of passing, shooting, or penetrating to the basket ( 42 , 57 , 59 ). The unique dynamic introduced by ball screens may contribute to the atypical equalization of contact between PG and C in contrast to the prevailing pattern observed among other players.
The majority of contacts in this study were sustained during set offense and set defense, aligning with the findings of Achenbach et al. ( 48 ). This pattern suggests that contacts are predominantly employed in organized situations such as set plays. Concurrently, this trend is reflected in in-game situations in which contact is utilized such as screening/picking, box out, or FFP ( Figure 1 ). Furthermore, results reveal that, within this cohort, these three game actions (screening/picking, box out, and FFP) constitute the most prevalent contact actions across all five playing positions. Ribeiro et al. ( 54 ) indicated that the PG experiences the highest frequency of contacts. Regarding contact with other players, Puente et al. ( 28 ) and Ibáñez et al. ( 60 ) reported body impacts (including physical contacts) exceeding 5 g per minute (Sum of impacts measured in g-forces in the three planes per minute). Whereas it is acknowledged that contact in basketball can lead to injuries necessitating players break, as discussed by Achenbach et al. ( 48 ), Brumitt et al. ( 61 ), and Minghelli et al. ( 62 ), it is crucial to recognize that physical contact is inherent to basketball, constituting an integral aspect of the game that does not invariably result in severe injuries.
However, existing research on the impact of physical contact on internal load responses has primarily focused on collision sports such as rugby ( 63 – 65 ). Consequently, the direct applicability of these studies to basketball in particular is limited. Doeven et al. ( 66 ), reports a recovery time on the neuromuscular level up to 48 h after a game which can be explained by the high number of intensive activities (e.g., jumping, shuffling, running) performed during basketball matches ( 3 ). Furthermore, for a comprehensive understanding of recovery processes ( 67 ), mentioned that various contextual factors need to be considered (e.g., travel duration, individual chronotype, playing style). In this context, the quantification of load induced by physical contacts might be helpful in implementing primary (e.g., nutrition, sleep and rest) and secondary (e.g., supplementation, physical recovery, therapeutic interventions) recovery strategies ( 68 ). Also, the implementation of subjective load measures (e.g., differential rated perceived exertion, dRPE) might enhance the understanding of different dimensions of physical efforts such as contacts received ( 11 ). A detailed investigation of physical contacts during basketball gameplay could extend the knowledge of internal load responses, building on existing research on inflammatory processes ( 69 ), salivary markers ( 70 ), and neuromuscular performance ( 71 ). Therefore, following a multimodal approach in players’ recovery, the monitoring of load produced by physical contacts in games and practices needs to be considered besides commonly used external load markers in elite basketball.
Our study showed a high number of contacts to the torso and upper limbs, suggesting that these areas experience significant internal load, which may lead to structural reactions such as contusions, tears, impacts, or laceration injuries ( 72 – 75 ). Visual inspection indicated that contacts in the chest-shoulder area (e.g., post ups, screens) are associated with high impacts. This allows us to speculate that especially C and PF, who are frequently involved in these situations, experience higher internal load responses due to physical contacts. Recognizing that controlled studies of muscular responses during game observations are challenging, isolated studies with high internal validity (for an experimental design, see ( 76 ) on muscles at different contractile characteristics could provide valuable insights into the impact of physical contact at the muscular level in basketball. Although our analysis revealed a low incidence of head impacts, it is crucial to recognize that contact in sports can result in both musculoskeletal injuries and brain effects. Sports-related concussions, caused by direct blows to the head, neck, or body, expose the brain to impulsive forces during sporting activities ( 77 ). Repeated concussions pose a risk to long-term brain health ( 77 , 78 ), and there is concern that even frequent low-level impacts in contact sports can harm healthy individuals. Studies suggest that repeated subconcussive impacts can lead to neurophysiological changes ( 79 – 81 ), emphasizing the importance of monitoring physical contact received in the head/neck area in basketball.
On the other hand, diverse contact situations are evident, with an elevated incidence of contact for the PG during dribble situations and for the PG, SG, and SF when penetrating to the basket. Additionally, the augmented contact observed for the PF during post ups warrants attention. Indeed, distinct playing positions yield disparate outcomes in terms of contact scenarios, indicating the imperative to consider the specificity associated with each playing position. Even among players occupying the same playing position, differences in on-court functions may manifest. Another compelling indication supporting the need for distinct consideration of playing positions is given by the distribution of contact points on the body. In this context, C consistently exhibit the highest frequency of contacts across all anatomical areas. Delextrat et al. ( 82 ) offer insights into these phenomena, characterizing inside players as engaging predominantly in static efforts such as blocking and positioning for rebounds. This underscores the need to recognize and analyze playing positions individually, acknowledging the diverse roles and demands inherent to each playing position on the basketball court.
The monitoring of the contacts players experience could potentially mitigate the risk of injury. Similar to the monitoring of in-game workload, gaining insights into in-game contact loads can empower coaches to formulate more targeted training and recovery strategies, enhancing the overall preparation of their players ( 83 ). This information underscores that determining the physical load required for competitive basketball cannot rely solely on measuring the quantity and intensity of dynamic actions. Acknowledging the limitations of relying solely on dynamic metrics, the inclusion of physical contact monitoring provides a more comprehensive understanding of the physical demands placed on players during a basketball game. By integrating information on both dynamic actions and contact loads, coaches can tailor training regimens and recovery strategies more effectively and contribute to optimizing player performance and injury prevention.
This study advances the understanding of physical contacts in professional basketball and highlights the importance of considering physical contacts when assessing internal load after gameplay. Utilizing video-based observation in a professional basketball setting, the study provides initial insights into the contextual characteristics of physical contacts across various playing positions. These analyses need to be deepened and extended in future research. For practitioners, the findings offer valuable information for conceptualizing load in training, conditioning, and recovery strategies for basketball players. Notably, the examination of players’ physical contact profiles during gameplay reveals significant discrepancies across individual playing positions. The increased number of contacts observed across playing positions underscores the need for tailored resistance training regimes to address the distinct demands encountered by players in different roles. From a training perspective, it is imperative to expose players to manageable levels of contact, isometric exercises, and eccentric loads on a regular basis. This approach aims to minimize muscle damage and facilitate adaptive responses to the specific demands of basketball. Particularly noteworthy is the emphasis on the fatiguing effect of contusions, as demonstrated by Barnes et al. ( 76 ). Their study, utilizing an unspecific experimental contusion model, suggests that the impact forces experienced are comparable to, or slightly lower than, those observed in contact sports such as rugby union tackles (∼1,600–2,000 N) or martial art kicks (∼1,500–2,000 N). Consequently, the authors speculate that the physiological responses observed are indicative of those typically associated with sport-related contusion injuries. Although direct cross-sport comparability presents challenges, these findings offer a preliminary framework for initiating contusion monitoring in basketball, with the aim of broadening load monitoring practices within the sport. Moreover, Barnes et al. ( 76 ) highlight that contusions share similarities with eccentric muscle injuries in certain aspects, underscoring the relevance of these findings to the broader context of sports medicine.
Furthermore, basketball coaches can leverage the insights provided by our research on players' contact demands during different game phases to tailor individualized and team-based training sessions. Specifically, exercises for C, given the heightened contact demands inherent to this playing position, should emphasize the development of specific movements, body contacts, and collision scenarios. Guards, who frequently navigate ball possession amidst diverse contact situations, would benefit from dedicating substantial time to a variety of ball-related exercises aimed at enhancing their skills in such contexts. Another consideration arises when players operate across multiple playing positions, whether for tactical or strategic reasons or because modern basketball teams often deviate from strict adherence to traditional playing position classification systems. In these instances, the load profile for these players becomes inherently more complex, potentially affecting the physical demands across various playing positions and individuals ( 23 , 84 ). Consequently, it is essential to exercise caution when applying the results pertaining to physical demands classified by playing positions in a practical setting, particularly in cases where the categorization and clarification of playing position roles within the team are ambiguous.
Match video analysis is an indispensable tool in sports, particularly for training and coaching purposes. However, its efficacy is contingent upon various factors and relies primarily on image quality, resolution, and available camera angles. Whereas video analysis offers invaluable insights, its utility is not without limitations. Accessibility to all contacts via video analysis is not assured. Challenges arise when attempting to identify precise contact events due to occurrences being outside the camera frame or obscured by players or referees. Blind spots, created by players or equipment (e.g., basket), further impede the accurate identification of critical events. Limited camera positions exacerbate these challenges by restricting visibility of certain pitch areas, potentially resulting in information gaps—particularly during pivotal event moments beyond the field of vision.
It should also be acknowledged that our study identifies only the initial contact, which often encompasses several ranges. Consequently, our findings do not account for more detailed analyses of body areas. Future research efforts could delve deeper into these nuances to elucidate the intricacies of physical contacts in basketball more comprehensively. Lastly, the authors acknowledge that a G*Power analysis is typically conducted to determine the appropriate sample size for detecting significant effects. However, in this case, a power analysis was not performed due to the exploratory and descriptive nature of the study, which aimed to provide initial insights and preliminary observations into this research phenomenon.
It is important to note that our study focuses solely on elite male basketball players from a single country, potentially limiting the generalizability of our findings across genders, cultures, playing levels, and nations. Thus, there is a critical need for further research to bridge these gaps and provide a more comprehensive understanding of contact dynamics in basketball across diverse contexts. Furthermore, as a multifaceted team sport, basketball encompasses dynamically interconnected game events and situations ( 85 ). Studies have analyzed situational variables such as game location, match status, and opponent quality in order to explore their impact on performance metrics ( 30 ). Disparities in fundamental player characteristics, including physical and physiological traits, between top and bottom teams contribute to variations in attacking and defensive contacts. Although not within the scope of our study, future research should comprehensively investigate this aspect.
Whereas our study utilized video analysis to identify player contacts, it is important to acknowledge that quantifying the associated load resulting from these contacts cannot be accomplished solely through video analysis. Video analysis provides valuable visual data on the occurrence and nature of contacts during gameplay. However, it lacks the capacity to measure the physiological or biomechanical impacts of these contacts on players directly. Micro-technical devices such as LPS or GPS offer a potential supplementary method for quantifying the load resulting from player contacts during basketball gameplay in future research. Previous studies have demonstrated the utility of accelerometers or specific load metrics in describing body contacts and assessing physical loads encountered by athletes across different sport types ( 86 – 88 ). In addition to video analysis and micro-technical devices, utilizing psychological measures such as ratings of perceived exertion or physiological measures such as heart rate variability offer other valuable methods for quantifying the internal load response resulting from player contacts ( 89 ). Furthermore, future examinations of intra- and interindividual variabilities in fatigue markers (e.g., creatine kinase or urea) could enhance the understanding of physical contacts on the dynamics of internal load responses. Implementing such methods provides a valuable complement to the video-based quantification of physical load, enhancing the understanding of the holistic impact of player contacts on athletes' performance and well-being in professional basketball.
The results of our study underscore the assumption that basketball cannot be considered as a noncontact sport as initially developed by Naismith. Given their frequent occurrence across various play actions and game phases during competitive matches, incorporating physical contacts into analyses seems appropriate in order to assess external and internal load in professional basketball. Additionally, our analysis highlights that contacts affect different anatomical regions of basketball players. Thus, our findings emphasize the complexity of physical contacts in shaping the overall load profile of professional basketball players. In summary, the results suggest that future research should consider incorporating physical contact in the assessment of physical load in basketball in order to gain a more comprehensive picture of external load and internal load responses. By acknowledging the significance of these contacts, researchers and sports practitioners can better understand the holistic impact of player interactions on physiological and biomechanical demands, and this will lead ultimately to more effective training strategies and injury prevention measures in basketball.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethical approval was not required for the studies involving humans because players were routinely filmed during all games in the course of the competitive season. All players confirmed the usage of video material for analytical purposes by contract. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
DW: Conceptualization, Methodology, Project administration, Software, Writing – original draft. JJ: Data curation, Methodology, Software, Visualization, Writing – original draft. KZ: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing.
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
We would like to sincerely thank the Skyliners GmbH for providing the video material used in this study. Additionally, we express our gratitude to Catapult Sports for their assistance in obtaining the license for Focus for teams SBG Sports Software. We also cordially thank Jonathan Harrow for his outstanding native speaker advice.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: monitoring, physical contact, basketball, load management, game analysis
Citation: Wellm D, Jäger J and Zentgraf K (2024) Dismissing the idea that basketball is a “contactless” sport: quantifying contacts during professional gameplay. Front. Sports Act. Living 6 : 1419088. doi: 10.3389/fspor.2024.1419088
Received: 26 April 2024; Accepted: 10 July 2024; Published: 23 July 2024.
Reviewed by:
© 2024 Wellm, Jäger and Zentgraf. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Dennis Wellm, [email protected]
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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D eciding to CAlL it quits is a relatively simple judgment early on in a career. If you find the prospect of going to work on Monday morning more depressing than a Lars von Trier film, it is time to leave. If you have nothing left to learn in your current organisation, you should probably grab more stimulating opportunities elsewhere. But knowing when to quit is less easy when you are in a role that already confers lots of status, novelty and purpose. And moving on is particularly difficult when it might be the last big job you have.
What is true of American presidents is also true of chief executives. Bob Iger has made not leaving Disney into an art form. The surest way to know you will not succeed Jamie Dimon at JPMorgan Chase is to be anointed his successor. Both bosses are stars, and their firms have reasons to hang on to them. The same cannot be said of Dave Calhoun, Boeing’s CEO , who will lead the company until the end of the year despite the enormous reputational damage it has sustained on his watch. (Mr Calhoun was supposed to have departed years ago; instead the firm raised the mandatory retirement age to allow him to stay.)
The incentives for CEO s and other leaders to stick around are material: assistants, chauffeurs, private jets and all. They are also psychological. People who reach the top of organisations do not often lack ego; the idea that someone else can do the job well may be hard to stomach. Michael Watkins, a professor at IMD Business School in Switzerland, calls this “the aura of indispensability”. The prospect of retirement can be particularly gruesome—this week, a farewell trip to Davos; next week, a strategic review of the spice rack.
There is some research that can help bosses think about how long to stay in a role. A study by Francois Brochet of Boston University and his co-authors looked at the relationship between CEO tenure and firm value to see if they could identify an optimal period in charge. They found that firm value started to decline, on average, after a CEO had been in the job for 14 years. That is not particularly helpful. There are too many differences between executives, firms and industries for one number to be a useful guide. Many chief executives get booted out an awful lot quicker than that; some bosses will warrant more time in the job, not less.
More usefully, however, the researchers did confirm a hump shape in firm performance. Things improve over time as CEO s master the complexities of their role but fall away later as they become more fixed in their ways and accrue more power. Similar humps have been observed from college basketball to Hollywood.
Changes in circumstances can shorten the duration of the hump. Separate research, by Bradley Hendricks of the University of North Carolina at Chapel Hill and Travis Howell, then at the University of California, Irvine, suggests that firms led by founder- CEO s are associated with a valuation premium when they first list on public markets but that this premium disappears within three years as the demands of the top job evolve.
If bosses are prone to misjudge when to quit, what can be done? Blunt instruments do exist, from mandatory retirement ages to explicit term limits. But strict rules have drawbacks, too. CEO s may be approaching their peak, not past it, at the time they are required to throw in the towel. Bosses approaching the end of their terms risk being seen as lame ducks, says Mr Watkins. And the knowledge that the end is nigh can change a CEO ’s own behaviour in potentially unhelpful ways.
Research by Sam Yul Cho of Oregon State University and Kim Sang Kyun of Sungkyunkwan University in South Korea suggests that firms run by bosses with short “career horizons” (ie, less time to go until they retire) generate fewer big innovations. Another paper, by Dirk Jenter of the London School of Economics and Katharina Lewellen of the Tuck School at Dartmouth, found that the likelihood a firm would be taken over jumped when a CEO was at retirement age. The bosses of target firms often lose their jobs; that is less of a concern when a career is winding down.
Rigid rules are not the best defence against people overstaying their welcome. More important are institutional constraints on CEO power—most obviously, a board that has a mind of its own—and bosses with the self-awareness to recognise that everyone has a natural shelf life. One of the first questions to ask a would-be senior hire is how long they think they should last. ■
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This article appeared in the Business section of the print edition under the headline “Knowing when to quit”
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Two suspects were arrested and face murder charges in connection with the shooting, officials said..
By Lilly Kersh
12:21 PM on Jul 22, 2024 CDT
A woman was found shot in east Oak Cliff early Monday morning, police say.
Two suspects were arrested and face murder charges in connection to the shooting, Keiona Hargis, 26, and Deiontay Horn, 21.
Around 1:05 a.m., Dallas police responded to a call in the 2000 block of Lanark Avenue and found the woman who was shot. Dallas Fire Rescue responded, and she died at the scene, police say.
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This investigation is ongoing. Anyone with information is asked to contact Detective Patty Belew at 214-422-9275 or [email protected] .
Lilly Kersh , Staff Writer . Lilly Kersh is a business reporting intern at The Dallas Morning News. She graduated in 2024 from the University of Georgia with a degree in journalism. Originally from Atlanta, Georgia, Lilly covers business and the economy in the Dallas-Fort Worth area.
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To help you get started, we have compiled a list of 114 basketball essay topic ideas and examples. From historical milestones to the impact of basketball on society, these topics cover a wide range of aspects related to the sport. Let's explore some of the exciting possibilities! The Evolution of Basketball: From its inception to modern-day ...
We have a list of professional basketball research paper topics to help you write a top-tier paper. What To Consider When Writing Basketball Topics. The first thing you will need to understand is that basketball is a fun sport. Therefore, the topic should also be in line with the genre. It is not like a technical science field where the topics ...
The tremendous popularity of the game has made it highly favored in the academic niche, leading students to develop basketball research paper topics at both colleges and universities. Given the vast scope of the research available for this game has, choosing the subject of basketball for an academic paper is an obvious option for young scholars.
⛹️♀️ Excellent Topics for a Basketball Research Paper. Now, if it is more serious than an essay - here are some good topics to write a basketball research paper on. The history of basketball, psychology research, and some controversial topics for a good basketball research paper - will make a great paper. Women's Basketball Topics
142 Basketball Topics & Essay Examples. Updated: Mar 2nd, 2024. 8 min. If you need to write a research paper about basketball, it's useful to read through some essay examples while looking for content ideas. Our team has compiled this selection of the best basketball research topics. Table of Contents.
Best Basketball Research Paper topics. The development of female basketball and the challenges faced by female players. The history of high school basketball and its impact on young athletes. The evolution of college basketball and the role of NCAA rules and regulations. People who have contributed to the growth and success of basketball ...
Each of the topics will make students discover many facts that are useful for both their research papers and personal life. By using the topics below for a research paper, students will make the most out of their research papers. Get Writing Help. Rated 4.8 out of 5. The development of female basketball.
Research Paper Topics about Basketball. Basketball research paper topics cover many subjects related to the sport. Its history, rules, psychology, sociology, and physiology are among them. Writing an essay on such a subject is an excellent way to explore the game and its impact on society. And don't think these topics won't help you excel.
1. Introduction. Players' performance prediction by using current and past data has gained attention, particularly in basketball [1], [2].Sports analytics and forecasting through these data is a rapid growing field with many methods that can be implemented from a different perspective for each situation [3].In a team, and specifically for the technical staff and coaches, the knowledge of ...
To get a wider glimpse of basketball essay topics and write an essay about it, try to explore samples of other papers and basketball argumentative essay topics. ... Make sure to write an outline of basketball research paper topics first, with a definite introduction and conclusion. 47 essay samples found. Sort & filter. 1 My First Encounter ...
With such a wide influence, basketball has long been a focus of research in the scientific community. Topics of basketball research have included injury prevalence and prevention, player physiology, 7, 8 biomechanics, 9, 10 training methods to optimize development, 11, 12, 13 and the characteristics of winning teams. 14, 15 With such a wide ...
Basketball Schools in the United States. The Moment When Basketball Became a Sports Topic for Essays. Some kinds of sports, like football and hockey, have become a huge part of our lives. The theoretical and athletic training research topics in basketball, football, and hockey will give you a clear understanding of their modern pace.
Most research conducted in basketball has focused on athletic populations. For instance, a review of the 228 studies returned on PubMed for "basketball" in 2019 (up to August 9th) indicates over 25% of studies focused on the incidence, treatment, rehabilitation, or screening of injuries, while 11% of studies described physical, fitness, or functional attributes in competitive basketball ...
Sports research paper topics encompass many interesting themes, each captivating in its own field. Some themes span from physical performance enhancement, delving into nutrition, training regimes, and physiological limits, to the mental aspects of sports psychology, focusing on motivation, team dynamics, and coping with pressure.
61 essay samples found. Basketball is a widely popular sport involving two teams of five players each, striving to shoot a ball through the opponent's hoop to score points. Essays on basketball could delve into its history, the influence it has on communities, the lessons it teaches in teamwork and perseverance, or even the economic aspects ...
66 George Street. Charleston, South Carolina 29424. [email protected]. 843-953-7317. Stephen Litvin is a professor in the School of Business of the College of Charleston. Crystal Lindner and Jillian Wilkie are students at the College of Charleston and Research Assistants within the School's Office of Tourism Analysis.
In turn, basketball research has readily used game-related statistics (3% of PubMed studies in 2019) to describe player and team performance, which provide an expansive reservoir of data, usually publicly available, to link outcomes of interest to performance. Consequently, our Special Issue was open to research exploring various current topics
CBA Continental Basketball Association 1969-70 to 1973-74. EPBL/EBA/CBA 1946-47 to 2000-01. · EPBL/EBA Eastern Pennsylvania Basketball League/Eastern Professional Basketball League/Eastern Basketball Association 1946-47 to 1977-78. · EPBL Statistics. · CBA Continental Basketball Association 1978-79 to 2000-01.
In it I have to write a paper on a topic of my choice that is original work. Basically like a mini-thesis (18-30 pages) that proves I can be a real life historian and contribute in some manner to the field. I have long wanted to do historical research in basketball, and this is a great opportunity to do so.
The National Basketball Association (NBA) so far is the most popular basketball league all over the world. This research paper explores the history of basketball starting from the late 1800s as well as its evolution into the game millions love today. The Creation of Basketball. Be part of a better internet.
Research paper at what level. Upper high school level. Role of NBA (and basketball in general) in the globalization of American culture. If you want more specific to NBA, maybe you can talk about the 3point line (why it was implemented) and how it has evolved ( literally) and also changed the modern game.
Abstract. Basketball is a sport , generally played by two teams of five players on a rectangular court . The objective is to shoot a ball through a hoop 18 inches (46 cm) in diameter and mounted ...
(1) Background: The objective of this systematic review was to collect relevant data in the available contemporary studies about sports injuries of basketball players and explain differences in sports injuries relative to gender, location, sport, and position on the court; (2) Methods: The papers were searched digitally using PubMed, MEDLINE, ERIC, Google Scholar, and ScienceDirect databases ...
With such a wide influence, basketball has long been a focus of research in the scientific community. Topics of basketball research have included injury prevalence and prevention, player physiology, 7, 8 biomechanics, 9, 10 training methods to optimize development,11, 12, 13 and the characteristics of winning teams. 14, 15 With such a wide ...
1 Introduction. Basketball was introduced in 1891 as an alternative physical activity to traditional sports such as American football or baseball ().It is an intermittent sport characterized by the physical demands of requiring players to execute repeated high-intensity actions (2, 3).These actions include rapid changes of direction, jumping, cutting, and high-speed movements over short ...
Research by Sam Yul Cho of Oregon State University and Kim Sang Kyun of Sungkyunkwan University in South Korea suggests that firms run by bosses with short "career horizons" (ie, less time to ...
Sharks living off the coast of Brazil have tested positive for cocaine, according to new research, the first time that the drug has been detected in free-ranging sharks. CNN values your feedback 1.
A woman was found shot in east Oak Cliff early Monday morning, police say. Two suspects were arrested and face murder charges in connection to the shooting, Keiona Hargis, 26, and Deiontay Horn ...