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ORIGINAL RESEARCH article

Long-term analysis of elite basketball players’ game-related statistics throughout their careers.

$\r\nJorge Lorenzo*$

1 Sport Science Department, Universidad Politécnica de Madrid, Madrid, Spain
2 Institute of Sport Science and Innovations, Lithuanian Sports University, Kaunas, Lithuania

The aim of the present study was to analyze the changes of game-related statistics in expert players across their whole sports careers. From an initial sample including 252 professional basketball players competing in Spanish first division basketball league (ACB) in the 2017–2018 season, 22 met the inclusion criteria. The following game-related statistics were studied: average points, assist, rebounds (all normalized by minute played), 3-point field goals percentage, 2-point field goals percentage, and free throws percentage per season. Each variable was individually investigated with a customized excel spreadsheet assessing individual variations and career trends were calculated. The results showed a positive trend in most of the investigated players in assists (91% of cases) and free throw percentages (73% of cases). Similar percentages of positive and negative trends were observed for all the other game-related statistics (range: 41–59% for negative and positive, respectively). In conclusion, an increase in assist and free throw performance was shown in the investigated players across their playing career. This information is essential for basketball coaches suggesting the use of most experienced players in the final moments of the game.

Introduction

Basketball is a team sport characterized by the execution of series of skills in multiple situations occurring across the game. In particular, game-related statistics are fundamental and their level might depend on the players’ characteristics and training experience. Most of the game related statistics depends on multifactorial variables (i.e., offensive and defensive tactics) determining a complex dynamic system during games, which is difficult to control in its totality. The use of performance analysis in sport with the determination of the most important game related statistics during the game aims to improve the team performance, increasing the knowledge of the performance of each player. Specifically, game-related statistics are key tools for basketball coaches providing reliable information about teams’ performance such as those distinguishing between successful and unsuccessful teams. Previous investigations widely studied the game-related statistics mostly assessing team performance in order to determine the most valuable players and the importance of certain positions such as guards, forward and centers (e.g., Sampaio et al., 2006a ), to evaluate the impact of rule changes (e.g.; Gómez et al., 2006a ; Ibáñez et al., 2018 ), the effect of home advantage (e.g.; Carron et al., 2005 ; Pollard, 2008 ; Watkins, 2013 ), the importance of starters and bench players regarding their contribution to the game (e.g.; Sampaio et al., 2006b ), the scoring strategies differentiating between winning and losing teams in women’s basketball FIBA Eurobasket (e.g.; Conte and Lukonaitiene, 2018 ). It is important to note that in basketball several game related statistics have been used, while only some of them were deemed fundamental. Previous discriminant analyses quantitatively determined the team performance indicators (TPI), identified as a variable able to define the most important aspect of performance ( Hughes and Bartlett, 2002 ) and compare different leagues ( Sampaio and Leite, 2013 ), which most affect the game outcome ( Gomez et al., 2008 ; Ibánez et al., 2008 ). In particular, Yu et al. (2008) , established a list of the most influential TPI’s (Technical Performance Indices) such as points per game (PPG), field goals made (FGM), rebounds, assists, turnovers, blocks, fouls, and steals. Sampaio et al. (2013) included also free throws as an important technical performance indicator. The TPIs with the most impact on the outcome of a season in Spanish first division (ACB) teams were shooting percentage (both 2-point and 3-point percentage), assists and rebounds ( García et al., 2013 ; Gómez et al., 2008 ). However, to the best of our knowledge, no previous investigations assessed players’ individual game related statistics across a long period of time. Indeed, players’ experience might play a fundamental role in improving players’ game related statistics effectiveness. Therefore, studies addressing this topic are warranted.

The performance of a player across his career might play a fundamental role in distinguishing between elite and non-elite players. Indeed, acquiring playing experience, players could have a better performance due to the demand of basketball game to perform complex actions that require high anticipatory skills in difficult situations. Indeed, these high anticipatory skills can be translated into scoring and passing related variables concerning about game-related statistics ( Sampaio et al., 2015 ), and therefore they become an important variable deeming further analysis in basketball. In fact, elite players perceive better their environmental information and are capable of adapting their behavior accordingly and consequently perform better compared to other non-elite players ( Aglioti et al., 2008 ). Therefore, playing experience might be essential in increasing players’ anticipatory skills and consequently their game performance.

It has been previously showed that performance slowly decrease after reaching the peak period of the player career ( Baker et al., 2013 ). In basketball, Baker et al. (2013) , found that the typical basketball career lasts about 11 years, with the longest career studied being 23 years of playing at an elite level. However, it is not clear the performance changes across players career, and their trend (i.e., positive or negative) calling for further studies in this area. Therefore, the aim of this study was to descriptively analyze TPI changes throughout the career of expert basketball players, assessing the possible performance trend.

Materials and Methods

Participants.

From an initial sample of 252 professional basketball players competing in ACB, 22 players (9 backcourt and 13 frontcourt) were selected for this study based on the following inclusion criteria determined by a group of experts, who were identified according to Swann et al. (2015) guidelines: (a) male players, currently playing in the ACB league in the season 2017–2018; (b) to have a minimum playing experience of 10 years (including only season in which they effectively played) in the first division of any country with at least an average of 25% of number of games and minutes played per season; (c) to possess a minimum of 5 years playing experience in first division of any league amongst the top 30 countries in the FIBA Ranking (at February 28, 2018); (d) to have played at least 75% of their professional careers in any country’s first division league, consequently no years played in lower division leagues were analyzed. The aim of these criteria was to ensure the highest quality of the sample for expert players with a solid number of games and minutes played each season ( Swann et al., 2015 ).

The databases used to obtain the game related statistics of each season for the studied players were the ACB official web page 1 for any season played in the ACB league, and the RealGM website 2 , or the official ACB guide released by the Spanish Basketball Association for any season played outside Spain. These databases are normally used in studies related with basketball, and basketball statistics and are considered valid and reliable ( Gómez et al., 2018 ).

The following game-related statistics for each season were recorded and analyzed: average points, assist, rebounds, 3-point field goals percentage, 2-point field goals percentage and free throws percentage per season. The variable point, assists and rebounds were normalized by minute played with the following formula (example for points scored: mean seasonal points scored/mean seasonal minute played ∗ 40 min). All the data for these game-related statistics, for every season and every player included in this study were storage in a database and once they were used for the statistical analysis.

Statistical Analysis

All statistical analyses were performed with a customized excel spreadsheet specifically developed to monitor individual changes and trends in a rigorous quantitative way ( Hopkins, 2017 ). Recently, this excel spreadsheet has been adopted to assess individual changes in team sports ( Siahkouhian and Khodadadi, 2013 ; Loturco et al., 2017 ; Colyer et al., 2018 ; Hurst et al., 2018 ) and specifically in basketball ( Pliauga et al., 2018 ). This statistics approach could be used as a possible alternative to previously used methodologies such as the ANOVA factor ( Yu et al., 2008 ) or the Jonckheere–Terpstra test ( Leite and Sampaio, 2012 ). The individual trends across playing career for each investigated player were then quantified and the percentage of players documenting a positive, negative or steady (when the result is zero) slope were calculated using the following formula y = m⋅x+n. Figures 1 – 6 are an example of the individual points and trendlines obtained via the Hopkins spreadsheet and that were later analyzed.

Figure 1. Individual trend of one participant for average points per season normalized by minute.

Figure 2. Individual trend of one participant for average rebounds per season normalized by minute.

Figure 3. Individual trend of one participant for average assists per season normalized by minute.

Figure 4. Individual trend of one participant for 3-point percentage per season.

Figure 5. Individual trend of one participant for 2-point percentage per season.

Figure 6. Individual trend of one participant for free throw percentage per season.

The mean slope for each performance indicator and the number of cases in which the slope was positive, steady, or negative are shown in Table 1 . Results revealed that most of the players have a positive trend in assists (91% of the cases) free throws (73% of the cases), and 3-point percentage although with a lower value (59%). Conversely, there were no differences of positive and negative trends reported for the other investigated parameters ( Table 1 ).

Table 1. Mean slope and number of cases of each variable.

The aim of this study was to analyze the trends TPIs throughout the career of expert basketball players. The results revealed that assists and free throws were the two TPIs mostly showing a positive trend during players’ careers. Specifically, the 91% of the studied players have a positive tendency in assists, with a mean slope of 0.15, and 73% of them have a positive tendency in free throws, with a mean slope of 0.95. Also, 59% of the players increase their 3-point percentage, but this result might have been influenced by the fact that more frontcourt than backcourt players met our inclusion criteria.

Basketball is a sport where situations change quickly and continuously as a result of the combination of factors such as the position of opponents in the field and their tactical behavior, the position of the ball and the timing of the offensive movements ( Altavilla and Raiola, 2014 ). Therefore, players are required to decide an appropriate response with a proper timing and executing it in a correct spacing. Often, players are subject to defensive pressure and the more skilled and experienced players might be able to anticipate events and perform unhurried actions as a result of their improved ability to “read the game” ( Sampaio et al., 2004 ). In this context, executing a successful pass (i.e., assist) assume a fundamental importance in basketball. Indeed, when analyzing the mechanism of this technical action, the assist requires a combination of good decision making in court, coordination, anticipation, timing, and a good execution ( Melnick, 2001 ; Gómez et al., 2006b ). Previous research demonstrated that assists and free throw percentage are two of the most factors to win a game ( Dias, 2007 ; Gómez and Lorenzo, 2007 ; Sampaio et al., 2015 ). Moreover, Sampaio et al. (2004) , suggested that assists are indicators of players’ maturity and experience, increasing in number as the player gets a better ability to read the game due to the years of playing experience. The results of our investigation highlighted supporting results, since most of the investigated players increased their assist performance across their playing career. This information seems essential for basketball coaches, who can rely on the performance of more experienced basketball players characterized by a better tactical awareness in order to execute successful passes and increase the scoring possibilities during the game. Indeed, Melnick (2001) showed a positive correlation between number of assists of a team and a better win-loss record through a season.

Free throws have also been demonstrated to be performance indicators differentiating between winning and losing teams in particular in close games ( Ibánez et al., 2008 ; Conte et al., 2018 ; Gómez et al., 2018 ). Therefore, it was expected that players increasing their experience and possibly assuming a leadership and fundamental role in their team would increase their free throw performance during their career. Accordingly, our results demonstrated an increased trend across players’ career for free throws and therefore possibly increasing their teams’ possibility to be successful. In this sense, experience accumulated in games and practices is the most crucial factor for developing expertise in one aspect ( Gómez et al., 2018 ). An increase in the percentages of free throws can be associated with the fact that players have already mastered the shooting during their years of training. Interestingly, a previous investigation showed that free throws shooting trajectories are more efficient and possess a lower variability in more experienced players compared to less experienced players ( Button et al., 2003 ). The practical application of our result is that coaches should favor the participation of most experienced players in last minutes of close games, when usually there are higher number of fouls generating free throws opportunities.

Other variables such as points, rebounds, and 2-point percentage did not show any trend increase across the players’ career. A possible reason for this finding is that these variables might be more influenced by physical factors (i.e., strength, power, and fitness), which showed a decrease during the lifespan ( Horton et al., 2008 ). Even though experienced players compensate this decrease in their physical abilities with a better understanding of the games’ tactical aspects, better timing and spacing and better decision-making abilities, it seems not enough to show a positive trend according to the results of our investigation.

Although this investigation provides basketball coaches with useful information, some limitations should be mentioned. Firstly, the results might have been influenced by some confounding factors such as injuries across the season, the playing status (i.e., starting vs. bench players), economical aspects such as players’ contracts and players and/or coaching staffs changing teams during investigated period. Therefore, future studies are warranted in order to overcome these limitations possibly controlling these factors. However, to the best of our knowledge, this investigation provides the first evidence about the individual trend in players’ performance across their playing career and notably increase the knowledge in this field. Moreover, further studies should be designed in order to assess players’ individual season-by-season changes across their playing career.

The results of this investigation suggest that as the players acquire years of experience in first division elite teams, their assists per game and free throw percentage increase. Conversely, other game-related statistics such as points, rebounds, 3-point percentage, and 2-point percentage showed both positive and negative trends in the investigated players resulting in a high between players variability. Finally, further research is required in this field using an individualized approach to increase the knowledge about players’ performance across their playing career.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

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.

^ http://www.acb.com
^ https://basketball.realgm.com

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Keywords : evolution, statistics, tendencies, career, team sports, professional

Citation: Lorenzo J, Lorenzo A, Conte D and Giménez M (2019) Long-Term Analysis of Elite Basketball Players’ Game-Related Statistics Throughout Their Careers. Front. Psychol. 10:421. doi: 10.3389/fpsyg.2019.00421

Received: 30 November 2018; Accepted: 12 February 2019; Published: 27 February 2019.

Reviewed by:

Copyright © 2019 Lorenzo, Lorenzo, Conte and Giménez. 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: Jorge Lorenzo, [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.

A friendly pat on the back can improve performance in basketball

A free throw in basketball will have every eye glued to one person. It's an intensely stressful situation. A research team led by the University of Basel studied whether a friendly tap on the shoulder increases the odds of making a shot.

In difficult situations, physical touch like a hug or a pat on the back can reduce stress. Whether this influences performance in stressful life situations has not yet been studied in detail. A team of researchers headed by Christiane Büttner at the Faculty of Psychology of the University of Basel investigated this question in the context of basketball games. Their results appeared in the journal Psychology of Sport & Exercise .

One of the most stressful situations during a game is a free throw. A player receives a free throw if they were fouled while attempting to score. In most cases, the fouled player gets two free throws and can win one point per successful shot. Many games are decided by free throws.

Büttner and her colleagues at the University of Landau and Purdue University studied precisely this situation using videos of basketball games. The study included a total of 60 games played by women's basketball teams in the National Collegiate Athletic Association (NCAA) in the US. The games contained 835 incidents of two free throws.

Your team has your back

The researchers counted how many of her four teammates touched the shooter before a shot, for example by tapping her on the shoulder or squeezing her hand. They then calculated whether there was a statistical association between the number of touches by teammates and the success rate of the subsequent shot.

The data showed that the chance of scoring rose when teammates showed their support through touch. The effect only appeared after a failed first shot. "So support from teammates is most helpful when your stress level is already high because you've missed the first of the two shots," Büttner says in summary.

It's conceivable that a pat on the back or squeeze of the hand could also help manage stress and improve performance in other team situations, says the psychologist.

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Story Source:

Materials provided by University of Basel . Original written by Angelika Jacobs. Note: Content may be edited for style and length.

Journal Reference :

Christiane M. Büttner, Christoph Kenntemich, Kipling D. Williams. The power of human touch: Physical contact improves performance in basketball free throws . Psychology of Sport and Exercise , 2024; 72: 102610 DOI: 10.1016/j.psychsport.2024.102610

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College basketball is changing. UConn’s crushing superiority stays the same

Transfer portals, NIL deals and the lure of the NBA make sustained success difficult. But the Huskies are building a mini-dynasty under Dan Hurley

T he University of Connecticut already had a men’s basketball dynasty. How else to describe a program that had won five national titles since 1999, with those victories coming under three different head coaches? If dynasties are hereditary, the Huskies passed theirs from Jim Calhoun to Kevin Ollie to their current boss, Dan Hurley. All that happened on Monday night was a set of affirmations – that Hurley has built something even better than Connecticut had before. And that, for now, the only other team that compares is Dawn Staley’s unbeaten South Carolina in the women’s game.

A 75-60 win over Purdue in the national championship game was a master class in every sense. The Boilermakers had a player who has been the best in men’s college basketball for two years: the running, hulking center Zach Edey. Hurley’s team had a plan, which revealed itself as the night went on: UConn would take their chances and let Edey bang away near the basket, but they would otherwise cut Purdue’s strengths out from under them. The Boilermakers were one of the country’s best three-point-shooting teams all season, making more than 40% of their shots from beyond the arc. On Monday, they went 1-for-7 from that range. Edey’s 37 points didn’t matter when the other Purdue players had 23 combined. UConn’s scoring attack, on the other hand, came in waves, a perfect testament to the depth Hurley has built at his outpost in Storrs, Connecticut.

Hurley is flatly the best coach in men’s college basketball right now. His profession is at a generational crossroads, as several legends of the game have retired over the past few years, or will soon. (So long, Mike Krzyzewski, Jim Boeheim, Roy Williams, and Jay Wright.) Not exactly young at 51 but with lots of career left if he wants it, Hurley has established himself as the leader of a new generation of college coaches. He has done that by winning: Two national titles in a row put him in rare air, and all 12 opponents in the past two NCAA tournaments lost to him by at least 13 points. “What could you say? We won. By a lot, again,” Hurley told reporters afterward. Indeed they did.

But Hurley’s Connecticut stand out for their manner of victory as much as the extent of it. He has staged his ascent at a moment of change in college sports. Players can now collect money from third parties , and the combination of those payments and recently loosened transfer rules mean that roster management is nothing like what it was in the days of Hurleys’ retired peers. At the same time, programs that rely too heavily on the recruitment of high school superstars have fallen short in tournament after tournament. Kentucky’s John Calipari, the poster boy for that method, looks to be bolting .

In that chaotic environment, UConn are a picture of stability. The Huskies do recruit NBA talent. Several players from last year’s champs have joined the league, and more are coming. (Guard Stephon Castle and center Donovan Clingan are about to become wealthy young men.) But the Huskies are not a magnet for legions of star recruits who leave college after one season, even if Castle soon does. At the same time, Hurley has worked the transfer portal effectively without blocking the growth of too many players on his own roster. Tristen Newton, the guard who led this team in scoring, played three years at East Carolina before joining up at UConn and playing a vital role on back-to-back title teams.

None of this is all that splashy, and it’s why a program that has two titles in two years and six in 26 will never have the same mainstream cachet of Duke or Kentucky. Those programs are eternal stopovers for future top NBA picks, and they hold a cultural perch that will remain elusive to everyone else. The genius of Connecticut is that the Huskies don’t care about those style points, nor have they let a lack of them prevent the program from lapping the likes of Duke and Kentucky in recent championships. (Since the turn of the century, the count of championships is UConn five, Duke and Kentucky a combined four.)

UConn have done their business over a long enough timespan that there is not much reason to expect it to abate soon. The Huskies have had a few messy down years, but their success has been stunningly durable given the transformations that have occurred around them. They have not only won it all under three coaches but in what amounts to three different conferences. They won their first three men’s titles in the old edition of the Big East, which had a handful of Eastern religious colleges and a mix of secular schools that have since made football-driven moves to other leagues. UConn did too, for a moment, and won another title in the American Athletic. They returned home to a reconstituted Big East after realizing that the move made no sense. Two more titles have flowed forth.

All of which is to say: Nobody knows how long Hurley will be at UConn. Nobody knows how the team’s roster may change from year to year. But everyone knows the Huskies have become one of a kind. And after Monday, even fewer could protest that they have crafted a multi-generational dynasty.

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Title: Can Women’s Basketball Sustain its March Madness Success? A Sports Management Professor Answers.

This story is a part of our “Ask a Professor” series, in which Georgetown faculty break down complex issues and use their research to inform trending conversations, from the latest pop culture hits to research breakthroughs and critical global events shaping our world.

Women’s college basketball is entering a new era.

Powered by superstars like Iowa’s Caitlin Clark and LSU’s Angel Reese, ticket prices to this year’s women’s tournament have eclipsed ticket prices for the men’s tournament. In this weekend’s women’s Final Four, ticket prices are more than double that of the men’s Final Four.

In the Elite Eight championship rematch between Iowa and Louisiana State University, a peak of 16 million fans tuned in as the Hawkeyes got their revenge over the Tigers, making it the most-watched college basketball game ever on ESPN platforms.

So how exactly has women’s basketball managed its meteoric ascent, especially since men’s basketball has typically received more resources and attention?

“There has been a continuous and concerted effort to promote and elevate women’s sports,” said La Quita Frederick , faculty director and associate professor of the practice in the Sports Industry Management program in the School of Continuing Studies.

“Superstars like Caitlin Clark and Angel Reese have undoubtedly captured the attention of fans with their exceptional skills and performances … This is not just an anomaly but rather the beginning of a new era,” Frederick said.

To better understand the rise of women’s basketball, read Frederick’s takes on women’s basketball’s March Madness success and how the sports industry can keep the momentum of women’s athletics going.

Ask a Professor: La Quita Frederick on March Madness, NIL and the Future of Women’s Sports

Historically, viewership and demand for women’s sports across all sports at the professional and college levels have been dwarfed by men’s sports. why is that.

One key factor is that culturally competitive sports have been primarily associated with men which has shaped societal perceptions and expectations. Another key factor is that men’s sports have historically benefited from greater investment and infrastructure, including funding, facilities and support at various levels of competition. Additionally, men’s sports typically have received more airtime, resources and marketing efforts from major broadcasting networks and sports channels. As such, media coverage and exposure have played a significant role. This greater visibility has contributed to higher levels of interest and engagement among audiences. These key factors have collectively and cumulatively contributed to the disparity in viewership between men’s and women’s sports.

Above and beyond those key factors, competitive and organized women’s team sports such as basketball and soccer are still relatively young compared to their counterparts. After all, the NCAA was founded in 1906, the NFL in 1920, and the NBA in 1946. In 1972, Title IX federal legislation was a significant catalyst and turning point for women’s competitive sports at the college level.

Nearly a decade later, in 1981, the NCAA and its governing body approved a plan to include women’s athletics programs and services for its membership. With a more institutional support and organizational structure, women’s sports and teams were established across the nation including the WNBA in 1996 — a full 50 years after the NBA and 15 after the NCAA officially included women’s collegiate athletics. Although 50 years in the making, the strategic efforts to promote gender equity and increase visibility for women’s sports are gradually narrowing the gap.

Sponsorship patterns and commercialization trends further reinforce this gap, with companies traditionally favoring men’s sports for investment due to perceived higher returns. Marketing strategies often align with traditional gender stereotypes, hindering the appeal and visibility of women’s sports. Access barriers, cultural representations and regional variations also contribute to disparities, alongside technological advancements that offer new avenues for promoting gender equity in sports viewership. Addressing these multifaceted factors requires sustained efforts to challenge existing norms, increase representation and leverage digital platforms to cultivate a more inclusive sports culture.

Why are more people watching women’s basketball this year than in years past? Is this year’s surge in popularity just an anomaly with superstars like Caitlin Clark and Angel Reese, or will this trend continue?

The increase in viewership for women’s basketball in recent years can be attributed to several factors. Firstly, there has been a continuous and concerted effort to promote and elevate women’s sports, resulting in greater visibility and coverage across various media platforms with social media by far being the most effective. This increased exposure has helped challenge stereotypes and showcase the high level of talent and competition in women’s basketball.

Additionally, the growing success of women’s basketball programs, both at the collegiate and professional levels, has drawn more attention and interest from fans. Superstars like Caitlin Clark and Angel Reese have undoubtedly captured the attention of fans with their exceptional skills and performances, generating excitement and enthusiasm for the sport. Additionally, these players have high profiles and very lucrative name, image, and likeness (NIL) that have given them additional exposure and visibility beyond your classic, niche women’s basketball fans, including national commercials, feature stories and guest appearances through mainstream medium outlets.

T his is not just an anomaly but rather the beginning of a new era. There is now greater investment and infrastructure, including funding, facilities and support at various levels of competition. In 2010, ESPNW was launched to encompass a website, signature events and specialty coverage for women sports. In 2022, the NCAA approved the NCAA Women’s Basketball Tournament to use the March Madness branding across the collegiate spectrum. At the professional level, the WNBA Las Vegas Aces are not only back-to-back WNBA Champions but the first WNBA franchise to have a dedicated headquarters, training facility and venue not shared with their NBA counterparts. If women’s basketball continues to be prioritized and supported, the potential for the upward trajectory of the sport will continue in the coming years.

T his is not just an anomaly but rather the beginning of a new era. La Quita Frederick

Is women’s basketball seeing a surge in the U.S. mainly, or is this a growing global trend?

While the U.S. has traditionally been a powerhouse in women’s basketball, other countries around the world are increasingly investing in and developing their women’s basketball programs. After all, women’s basketball players have been playing professionally overseas in other leagues and during the offseason. Countries in Europe, Asia and Oceania, among others, have seen significant growth in women’s basketball participation, competition and fan engagement.

This global expansion is fueled by various factors, including increased opportunities for women athletes, greater investment in infrastructure and coaching, and growing recognition of the talent and competitiveness of women’s basketball. Therefore, while the surge in popularity may be particularly noticeable in the United States, it reflects a broader global trend towards greater recognition and appreciation of women’s basketball. Women’s sports, including women’s basketball, is on an upward trajectory.

Is this success in college women’s basketball being replicated in other sports? How?

The rising success and popularity of women’s college basketball are mirrored in various other sports, albeit through diverse channels and to varying extents. One significant factor contributing to this trend is the increased attention and media coverage given to women athletes across different sports. As women’s college basketball gains traction, other sports like soccer, volleyball, softball and gymnastics are also experiencing heightened visibility through expanded coverage by leagues, governing bodies and media outlets.

Moreover, the emergence of professional opportunities for women athletes is closely linked to their success in college sports. This progression creates a pathway for talented athletes to compete at the professional level, fostering growth and investment in professional leagues both domestically and internationally. Additionally, a broader cultural shift toward supporting women athletes and advocating for gender equity in sports has propelled the popularity of various women’s sports beyond college basketball.

Digital platforms and social media have also played a pivotal role in amplifying the voices and achievements of women athletes across different sports, enabling them to engage directly with fans and build their personal brands. Furthermore, international success in global competitions such as the Olympics and World Championships has elevated the profiles of women’s teams and attracted attention to their respective sports on a global scale.

How has NIL affected the popularity of women’s sports relative to men’s sports?

NIL has the potential to significantly impact the popularity of women’s sports relative to men’s sports in several ways. First and foremost, NIL allows athletes, regardless of gender, to profit from their own name, image and likeness. This opens up new avenues for women athletes to monetize their talents and build their personal brands, which can enhance their visibility and attract more fans. However, the full impact of NIL on women’s sports popularity will depend on various factors, including the extent of endorsement opportunities, media coverage and continued efforts to promote gender equity in sports.

Moreover, NIL offers distinct advantages for women athletes and teams compared to their male counterparts. One key advantage is the opportunity for women athletes to build their personal brands independently, addressing historical disparities in media coverage and investment. Women dominate as consumers and strongly influence purchase decisions individually and collectively in their households. Through NIL, women athletes can showcase their personalities and interests, attracting endorsement deals that reflect their individual identities.

Additionally, NIL empowers women’s sports by enabling athletes to generate revenue and support their athletic endeavors. By monetizing their talents, women athletes challenge stereotypes about the profitability of women’s sports and assert their economic agency. Furthermore, NIL can drive cultural change by reinforcing the value of women’s sports and promoting diversity and inclusion.

How do you feel personally about this surge in interest in women’s basketball?

Personally, I am loving it because I always knew the women’s game was competitive, entertaining and exceptionally talented. As an undergraduate at NC State University, my suitemates were women’s basketball players. I remember going to NC State women’s basketball games including games against our ACC opponents.

When I still worked in college athletics, I oversaw the marketing for the NC State’s women’s basketball program. Not only was NC State a nationally ranked team with occasional sellouts long before the age of social media, streaming, etc., but they were coached by the legendary Naismith Hall of Fame and former Olympics Coach Kay Yow. It was my pleasure to work for them, and with them, including being the co-creator of Hoops for Hope, which is the direct predecessor for what is now known nationally as Play 4 Kay.

When I worked for the Orlando Magic fresh out of graduate school, I also had the opportunity to work at the first-ever WNBA Draft Camp held at the ESPN Disney Wide World of Sports. I saw the first WNBA superstars like Sheryl Swoopes, Tina Thompson, Cynthia Miller, etc., and some of them have gone on to coach. I have witnessed this “overnight” success story unfold over the past 25+ years. I was not a player or a coach but I was working behind the scenes of the business at every stage of women’s basketball growth both at the collegiate and professional levels of the game. Quite honestly, it is very special to me personally and professionally to have been a part of history in the making and knowing many of those and all of the efforts to arrive at this moment in time.

Who are you rooting for in the NCAA tournament?

Who am I rooting for?! That’s an easy answer. I am originally from North Carolina, so I was born and bred to love Tobacco Road basketball and all things ACC. More importantly, I am a proud graduate of NC State University and a former associate director of marketing for the NC State Department of Athletics, so I’m all in for my Wolfpack family given both our men’s and women’s basketball teams are in their respective Final Fours this year. As for me, I’m “Red & White for Life” so GO PACK, LIGHT IT RED, and WHY NOT US? Or rather, WHY NOT BOTH?

After a Season Starting With Loss, Women’s Basketball Coach Darnell Haney Ends on a High Note

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Open Access

Peer-reviewed

Research Article

Examination of National Basketball Association (NBA) team values based on dynamic linear mixed models

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Science, Department of Statistics, Cankiri Karatekin University, Cankiri, Turkey

Efehan Ulas

Published: June 17, 2021
https://doi.org/10.1371/journal.pone.0253179
Reader Comments

In the last decade, NBA has grown into a billion-dollar industry where technology and advanced game plans play an essential role. Investors are interested in research examining the factors that can affect the team value. The aim of this research is to investigate the factors that affect the NBA team values. The value of a team can be influenced not only by performance-based variables, but also by macroeconomic indicators and demographic statistics. Data, analyzed in this study, contains of game statistics, economic variables and demographic statistics of the 30 teams in the NBA for the 2013–2020 seasons. Firstly, Pearson correlation test was implemented in order to identify the related variables. NBA teams’ characteristics and similarities were assessed with Machine Learning techniques (K-means and Hierarchical clustering). Secondly, Ordinary linear regression (OLS), fixed effect and random effect models were implemented in the statistical analyses. The models were compared based on Akaike Information Criterion (AIC). Fixed effect model with one lag was found the most effective model and our model produced consistently good results with the R 2 statistics of 0.974. In the final model, we found that the significant determinants of team value at the NBA team level are revenue, GDP, championship, population and key player. In contrast, the total number of turnovers has a negative impact on team value. These findings would be beneficial to coaches and managers to improve their strategies to increase their teams’ value.

Citation: Ulas E (2021) Examination of National Basketball Association (NBA) team values based on dynamic linear mixed models. PLoS ONE 16(6): e0253179. https://doi.org/10.1371/journal.pone.0253179

Editor: Roy Cerqueti, Sapienza University of Rome, ITALY

Received: January 6, 2021; Accepted: May 28, 2021; Published: June 17, 2021

Copyright: © 2021 Efehan Ulas. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The value of NBA teams has increased significantly in the last years. After 2014, the tremendous increase in the value of NBA franchises has attracted the attention of owners and researchers. NBA owners also have an interest in determinants of NBA team values. Although the NBA does not release detailed financial reports to the public, these financial reports can be obtained from Forbes reports and other sport websites. In the last seven years, franchise values of the NBA have grown around 30% from 2015 to 2020 ( Fig 1 ). The reason behind the increase in the team values cannot be explained only by the significant performance statistics of the teams. Effective game performances of the teams do not always equal success in the team values. For example, New York Knicks has not made the playoffs since 2012, but is still the most valuable team in the NBA according to Forbes in 2020. Therefore, it is important to investigate other factors which may affect team values such as economic indicators, demographic statistics and financial variables.

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https://doi.org/10.1371/journal.pone.0253179.g001

The population and gross national product (GDP) in the city where the team is located may have a positive effect on the value of the team. For instance, the population of the city might positively affect basketball marketing. The higher population can help the sales of the souvenirs like team uniforms and other products. Similarly, the number of wealthier supporters is high in the metropolitan cities and this can help to increase team values directly. To our knowledge, there are only a few studies related to determinants of NBA franchise values and these studies do not considered NBA teams individually. [ 1 ] used panel data on NBA franchises for the years between 2009 and 2016 to determine significant indicators of franchise value in the NBA and the valuation approaches of the study predicted the franchise in Seattle to be $1.4 billion in 2017. [ 2 ] compared the determinants of firm values in the United States and Europe over the period of 2004–2011. They considered NBA, Major League Baseball (MBL), National Football League (NFL), National Hockey League (NHL) and European soccer values and found that determinants of team values in the USA were not the same as those in Europe. [ 3 ] examined the effect of team nomenclature, team relocation and stadiums on franchise values for NBA, MBL, NFL and NHL. They highlighted that team performance; market size and the new stadium rises the team value in the MLB but not in the NBA. However, moving from an old facility into a new one increases the values of NBA teams. [ 4 ] investigated the franchise values of American professional sports teams in the 3 national leagues. Their findings showed that new facilities were not effective in the increasing of franchise values of the NBA.

Many other studies consider the NBA panel data in order to analyze performances of the teams. Performance analysis in the NBA is evaluated by statistical estimation of available data. Thus, effective parameters need to be determined in order to analyze the panel data. Furthermore, performance indicators may have effect on team values. Therefore, it is important to understand the studies related to performance analysis of game dynamics. In basketball, technical and physical performances are considered as the most important influences on the team performance during a match [ 5 ]. In addition, offensive factors determine sport performance in the NBA [ 6 ]. Coaches consider the quality of opposition when playing against stronger and weaker opponents [ 7 ]. Thus, such game plans may reduce or increase the total game statistics of the teams. Although many studies indicated the importance of game performance, its effect on the team values has not been examined in detail. Therefore, the goal of this paper is to examine the game performance statistics, economic indicators and demographic parameters that significantly affect the team values based on the dynamic linear panel regression models. In this study, the game performance statistics, economic indicators and demographic parameters and logarithmic transformation of some of the variables has been considered in order to estimate the team values in the models.

Parameter selection plays a critical role in estimating the team values. In this study, the performance statistics and economic indicators used to estimate team values were chosen according to literature. Papers included population [ 8 , 9 ], GDP [ 10 , 11 ], total assists [ 12 , 13 ], revenue [ 14 ], allstar [ 15 ], winning percentage [ 16 ], championship [ 17 ], total points [ 18 ], total turnover [ 19 ], home attendance [ 20 ], key player [ 1 , 21 ], team value [ 1 , 2 ] in their statistical analysis. Therefore, we considered the variables that can potentially affect the team values by evaluating the literature. In addition, it would be misleading to use only performance variables when determining the value of the NBA teams. Variables such as the population and wealth of the city, the average supporter capacity, revenue and the team popularity should be considered in order to understand the sharp increase in the last years.

In literature, examination of professional sports franchise value was rare before the 2000s. The reason behind this can be explained as the owners of professional sport teams are wealthy enough to sustain losses [ 22 ]. After the 2000s, many studies have been published about professional sports franchise values for all professional sports leagues such as MLB, NFL and NHL. These studies can be basically divided into two categories: modelling franchise values by using Forbes reports and examining franchise values from historical growth rates. This is not the case in this research because we do not only focus on franchise value but also on team values individually. Also, analyzing only performance indicators produces low prediction rates for team values. Therefore, economic parameters and demographic variables are included in the analysis. First of all, the data are combined from different sources for the NBA seasons between 2013–2020. Correlation analysis has been implemented to the variables in order to understand the relation between those variables. Then, similarities between NBA teams are identified based on the selected variables by using Machine Learning techniques, K-means clustering and Hierarchical clustering. Thus, besides determining the parameters that affect the team value by using linear mixed models, we aimed to investigate the NBA teams individually and evaluate their similarities with each other. Afterward, based on the selected outcomes, three different dynamic linear models; OLS, fixed effect and random effect models are implemented. The lag value of the dependent variable is considered in the models in order to reduce the correlation problem. The final model is selected based on AIC scores [ 23 ]. Therefore, the aim of this study is to (i) investigate the independent and interactive effects of team performance statistics, economic and demographic indicators on the team values and (ii) to examine these indicators and performance parameters, that significantly affect the team value, through a case study of the NBA teams using suitable statistical and machine learning methods.

Materials and methods

The data consist of 13 economic and demographic variables and performance parameters that indicate the characteristic of NBA teams in the National Basketball League for the 2013–2020 season. In the dataset, the variables and performance indicators are related to the values of the teams, not to the individual values of the players. The dependent variable, team values, is the variable we aim to estimate by using the remaining 12 variables. The variables which are used in the analysis and their definition are given in Table 1 . All the observations are collected from Forbes, census, opendatanetwork and NBA websites. The detailed list of the websites from where the data is collected is given in the Table 2 .

https://doi.org/10.1371/journal.pone.0253179.t001

https://doi.org/10.1371/journal.pone.0253179.t002

Some variables were directly taken, while others were derived from the existing ones by using mathematical transformation with the goal of supplying different and potentially more advantageous, sensible and insightful knowledge. In this study, the derived variables are as follows:

GDP = Logarithmic transformation of gross domestic product per capita of the team city.
Population = Logarithmic transformation of population of the team city
Home Attendence = Logarithmic transformation of home team attendence (annual)

Since there are 30 teams in the NBA, 30x8 = 240 observations are included in the dataset. The estimation of some of the observations are replaced with missing values.

The list of the data sources is given below. The variables are collected from different sources and all list can be found in Table 2 .

Statistical approach

Basic statistical descriptors (mean and standard deviation (SD)) for the variables were calculated and given in Table 3 .

https://doi.org/10.1371/journal.pone.0253179.t003

After carefully examining the explanatory variables of the dataset, correlation test was implemented to capture if whether there is any relationship between variables. Afterwards, K-means clustering and hierarchical clustering approaches were applied in order to identify NBA team’s similarities based on selected variables. Finally, dynamic linear regression models were applied to investigate the variables that have the biggest influences on the team values. The unstandardized coefficients and standardized coefficients are calculated and compared in order to capture if the coefficients change due to different units. However, there was no remarkable difference between the coefficients of the both techniques. Three different significance levels; 0.1, 0.05, and 0.01 were considered in order to identify the statistically significant variables. The variables which are statistically significant based on the significance level are shown with different Latin letters. A p value less than selected three significance levels were considered to be statistically significant.

All the analyses were performed using R statistical software [ 24 ]. For correlation analysis, corr package [ 25 ] was used, while for illustration of the cluster analysis analysis”ggbiplot” [ 26 ] and for regression analysis”plm” package [ 27 ] were used.

Methodology

Below is the formulation of fixed and random effects models.

Where T t is time as binary variable and δ t is the coefficient for the binary time regressors. The formulation of the random effect model can be seen below.

Correlation analysis

Correlation analysis is a useful approach for understanding the structure of the variables. It might be advantageous to consider correlation analysis before going through detailed statistical methods. Fig 2 illustrates the Pearson correlation scores of the continuous variables. The red boxes represent variables that have a positive relationship and blue boxes represents variables that have a negative relationship.

https://doi.org/10.1371/journal.pone.0253179.g002

As expected, team value and revenue have a strong positive relationship (r = 0.91) which means that teams having higher revenue tend to have higher team value. Similarly, team value and key player have a strong positive relationship (r = 0.67). In contrast, home attendence and turnover have a negative relationship (r = -0.26) which means the high number of turnovers effects the number of home attendence in the teams. Unsurprisingly, winning percentage have a positive effect on the number of allstar. Since there are some independent variables that have correlation coefficient greater than 0.5, the multicollinearity was checked before going through the analysis. Therefore, variance inflation factor (VIF) was calculated in order to capture the multicollinearity. However, only the VIFs of the Point and Revenue were found around 3.5. All other variables change between 1–2. Therefore, we considered all these correlations in the modelling part because VIFs between 1 and 5 suggest that there is a moderate correlation, but it is not substantial enough to remove variables from the model [ 28 ]. The lag value of the dependent variable is included in the models which helps reducing the serial correlation problem.

Fig 3 illustrates the relationship between NBA team values and revenue. In addition, Fig 4 shows the team values and GDP for each team respectively. As each team value increases, its revenue also increases. Although a similar scenario occurs in Fig 4 , some of the GDPs reduced while the team’s value increased. This scenario is generally observed in cities with high numbers of immigrants.

https://doi.org/10.1371/journal.pone.0253179.g003

https://doi.org/10.1371/journal.pone.0253179.g004

It is interesting that the NBA might have an impact on the economy of the city. In Fig 4 , the most of the team values have positive effect on the GDP. If the team value is increases, GDP is also increase for the most of the cities. However, if the value of the team decreases, it has neutral impact on the GDP. This might be due to the NBA brings in large amounts of money into the economy that affects both the team, and the city positively.

Cluster analysis

The aim of cluster analysis is to identify the internal grouping in a set of data. The data divided into k groups in the K-means clustering approach and each cluster is defined by its centroid. Similarities and dissimilarities of the teams are calculated with Euclidean distance in our analysis. This approach helps us to classify teams into groups. The outcome of the calculation is known as the distance matrix and it can be seen in the Fig 5 below.

https://doi.org/10.1371/journal.pone.0253179.g005

High similarities and dissimilarities are shown with red and turquoise colors respectively in Fig 5 . Teams whose revenue and the stadium capacity are more likely to have the least distance between them. The clustering is processed by minimizing the sum of the distances between each observation and the cluster centroid. The algorithm of the K-means clustering has four steps:

Determining the number of clusters (k)
Select randomly k teams from the data as an initial grouping centroid and each team of the data is assigned to its nearest centroid
For each cluster, update the cluster centroid by generating the new mean values of all the team features
Repeat step 2 and 3 until the cluster assignments are completed

The optimal number of clusters are selected based on Gap statistics. The higher value of gap statistics was occurred for k = 5. Therefore, the number of clusters are selected as 5 where the gap statistic peaks and data are divided in to 5 groups based on k-means clustering. Fig 6 shows the final grouping of k-means clustering.

https://doi.org/10.1371/journal.pone.0253179.g006

Light blue represents the Golden State Warriors. This team was separated from other teams and was assigned as a group alone. The most important reason for this outcome is that GSW played in the four NBA finals in the last six seasons and won the championship title 3 times. These outstanding statistics of GSW are the dominant reason that separates the team apart from the other teams in the data set. Therefore, we can call this group as the most successful team.

On the bottom of the plot (yellow color), there are teams with the most populated cities in the USA. The four teams in this group are those belonging to the three most populous cities in the country. Also, these teams are those with the highest average team value in the NBA. Therefore, we can name these groups as wealthiest teams.

The red group is the group with the teams that are less successful than the other teams. Between the 2013–2020 seasons, none of the teams played in the NBA finals. Moreover, none of the teams has championships except the Detroit Pistons and the Sacramento Kings. Therefore, this group can be named as teams with mediocre performance.

All teams in the gray group, except the Denver Nuggets, are on the east coast. This group includes teams with similar average team value and revenue. In addition, this group includes teams with a lower average attendence than other teams. So, this group can be named as less popular and teams with average team value. Finally, between the 2013–2020 seasons, all teams in the blue group, except the Dallas Mavericks and the Los Angeles Clippers, are the teams that have played the Conference final at least once. Furthermore, all teams in this group, played with an average attendence number of over 18,000. Thus, we can name this group as reliable teams.

Alternatively, we can use other inspection method called Hierarchical clustering which does not require a pre-specified number of clusters. The algorithm of this approach considers all teams as single element clusters. Then, the most similar two clusters are combined into a new bigger cluster at each step. This process is repeated until all observations converge to one single cluster. Fig 7 illustrates the results of the Hierarchical Clustering.

https://doi.org/10.1371/journal.pone.0253179.g007

Hierarchical clustering produced the similar results as K-means clustering. Generally, the teams are in the same groups as in the K-means clustering. Differently, the Chicago Bulls, Denver Nuggets and Indiana Pacers were included in different groups. This is because some teams are located on the boundaries of the groupings. Therefore, different clustering methods can assign some of the teams to other groups based on their location on the dendrogram.

Linear models

Summary of the linear models are represented in Table 4 and seven different models; OLS, fixed effect model, linear fixed effect model, dummy variable fixed effect model, random effect model, linear random effect model, dummy variable random effect model, are included in the table. In Table 4 , * denotes a statistically significant at significance level of 0.1, • represents a statistically significant at significance level of 0.05 and † shows a statistically significant at significance level of 0.01.

https://doi.org/10.1371/journal.pone.0253179.t004

The variables of the NBA data have characteristics that may vary from team to team. Therefore, OLS models cannot handle this issue. However, fixed effects models explore the relationship between dependent and independent variables within an entity and each entity has different characteristics that can affect the dependent variables. Fixed effects models help us to overcome this challenge. In contrast, random effects models assume the variation across and within entities are random and uncorrelated with the dependent or independent variables. Therefore, we assume fixed effects models estimate better results than other dynamic models for our NBA data. In Table 4 , models are compared based on their AIC and R 2 scores. The model which has the highest R 2 and lowest AIC scores was selected as the winning model. It can be seen in Table 4 that dummy variable fixed effects model has the highest R 2 and lowest AIC scores.

We also considered the models with lagged dependent variables in order to provide robust estimates of the effects of independent variables. Two different levels of lagged dependent variables specified in the model which accounts for auto-correlation in the error term. In Table 5 , fixed effects models with one and two lags are compared with the winning model. Considering the lag of team value in the model helps us to explain the variation in the team value (for the certain year). Therefore, including the lags in the model yields more accurate parameter estimates. Based on the results in Table 4 , the dummy fixed effects model with lag = 1 is selected as the final model. Including the lag value of dependent variable in the model overcomes the serial correlation issue. In the final model, five variables, i.e. Turnover, Championship, Population, GDP and Revenue are statistically significant.

https://doi.org/10.1371/journal.pone.0253179.t005

According to the final model, for team value at lag = 1, each one of Championship, Population, GDP, and Revenue has a significant positive effect on the team value while turnover has a negative effect. The coefficients of championship and revenue are highly significant on the team value. For instance, the team value increases by approximately $53 million for each championship. This result shows the important effect of the championship on team value. In addition, team value increases $3.347 million for a $1 million increases in the revenue. New York Knicks, Los Angeles Lakers, and Golden State Warriors have the highest average team values and those teams also have the highest revenues compared to other teams. Thus, our result supports the hypothesis that teams with the highest revenue and population tend to have the highest team value.

Turnover and population variables are statistically significant at the significance level of α = 0.05. Turnover has negative impact on the team value. Team value can be decreased by $52.9 million if the average turnover rises by one per game. For each additional 10,000 people in the population of the team city, the team value increases by $8 million. GDP is statistically significant at the significance level of α = 0.1. Each $100 increase in GDP rises the team value by $1 million. These results show that economic indices are more effective than performance variables in the estimation of team value. In other words, it is clear that a team value is highly affected by economic variables such as revenue and GDP. In the final model, the statistically significant variables on the team value at significance level of 0.1 found as: Turnover, Championship, Population GDP and Revenue. In addition, Championship and Revenue were also found statistically significant at significance level of 0.01 and Turnover was also found statistically significant at significance level of 0.05. Although it is found that the significant determinants of team value at the NBA team level are revenue, GDP, championship, and population, it is important to check the impact of the key player in the winning model. To do so, the predictor key player is added in the final model and compared with the winning model. The comparison of the final models is given in Table 6 .

https://doi.org/10.1371/journal.pone.0253179.t006

The team value is a very interesting metric to analyze because it encompasses almost all parts of a team. Although teams spent millions of dollars for a single key player, the key player may have a significant and positive impact on the team value. It can be seen from the Table 6 that key player is statistically significant on the team value. In the final model, we found that the significant determinants of team value at the NBA team level are revenue, GDP, championship, population, key player and turnover.

The main objective of this research is to examine the performance variables and economic indicators that significantly affect the team value. K-means clustering, hierarchical clustering, correlation, ordinary linear regression, fixed effect and random effect models were implemented to conduct the analyses. In correlation analysis, team value and revenue are found to be highly correlated with each other. In addition, there was a positive high correlation between GDP and team value. In contrast, home attendence and turnover have a negative relationship, high number of turnovers effect the number of home attendence in the teams. Unsurprisingly, winning percentage have a positive effect on the number of allstars selected from that team. Modeling has been done by taking these results into consideration in the further analysis.

For the cluster analysis, K-means and hierarchical clustering are applied to compare the teams in terms of overall impact. For clustering, 12 different variables were considered. Distance matrix of the variables were calculated to classify the teams into groups and to understand the characteristics of the overall impact. The optimal k was found to be 5 based on the gap statistic. Therefore, we divided the data into 5 clusters using K-means clustering and then performed the analysis with these results. Similarly, the hierarchical clustering divided the data into 5 clusters. The grouping of the teams was similar between the two approaches except for four teams; Chicago Bulls, Boston Celtics, Indiana Pacers and Denver Nuggets.

The dynamic regression models were considered in the modelling of the team values. Eight different models were compared based on AIC and R 2 scores. The dummy variable fixed effects model was seen to be the optimal model in the first analysis. In all models, revenue was found to be the most significant variable on the team values. These finding are supported by other studies emphasizing the importance of revenue on the team value [ 3 , 4 ]. Furthermore, the three teams with the lowest average revenue in the NBA (2013–2020 seasons) are also the teams with the lowest team value.

Afterward, the models with lagged dependent variables are considered in order to provide robust estimates of the effects of independent variables and the final model was the dummy fixed effects model with lag = 1. According to the final model, each of Championship, Population, GDP, and Revenue has a significant positive effect on the team value while turnover has a negative effect. The coefficients of championship and revenue are highly significant on the team value. This means that the team which has the larger the number of championships, GDP, and revenue, tends to have the highest team values. This finding is consistent with similar studies [ 29 , 30 ].

Besides the economic variables affecting the team, there are also performance variables that have no significant effects on the team value. These include assists, points and winning percentages. Our findings, that relates the effect of the game performances on the team values, differs from the findings of [ 3 ]. The increase in the team value could be due to many indicators such as revenue, the number of championships, and so on. However, the impact of key players should be considered in the analysis. For example, the team value of the Cleveland Cavaliers increased by around 16% when the Lebron James joined the team first time. Similarly, the impact of his participation in Miami Heat helped increase the team value by about 10%. The team value of the Cleveland Cavaliers increased by over 70% after Lebron’s rejoined Cleveland Cavaliers in 2014. In addition, Lebron joined the Los Angeles Lakers in 2018 and the team value of the Los Angeles Lakers increased approximately by 34% in that season [ 21 ]. Similar scenarios were seen for the other key players. For instance, Kevin Durant has joined Golden State Warriors in 2016 and the team value of the GSW rose by around 27% in that year. It is not correct to say that the team value increased strictly only because of the key player. However, there is a strong correlation between the increase in the team value and key player. Our findings for the key player are consistent with other studies [ 1 , 21 ].

The other variables which may affect the team values, such as tv contracts of the teams, advertising agreements and player salaries, should be considered in the future studies. Although accessing such data is difficult and expensive, it would be worth the efforts to reach these data as these variables differ from team to team and may affect team values.

The results of the cluster analysis help us to understand the similarities of the NBA teams based on the variables evaluated in this study. Thus, besides determining the parameters that affect the team value by using linear models, we aimed to investigate the NBA teams individually and examine their similarities with each other. Among all the teams, the results of the cluster analysis for Golden State Warriors (GSW) showed outstanding results and GSW distinguished from other teams. Furthermore, we were able to explain why some of the NBA teams with low performance statistics have the highest team value. In the yellow group of the cluster analysis, Brooklyn Nets, Chicago Bulls, Los Angeles Lakers and New York Knicks are the teams of the most three populous cities in America. Also, these teams are those with the highest average team value in the NBA. These results show that the NBA teams in the cities with a high population tend to have high team value. In the red group, there were teams with mediocre performance and none of the teams played in the NBA finals between the 2013 and 2020 seasons and only Sacramento Kings and Detroit Pistons have championships in the last 10 years. Therefore, the most of these teams have lower team values compared to yellow group. Examining the characteristics of NBA teams with cluster analysis using the selected variables helped us to understand the variables that affect team value in the mixed effect models.

In conclusion, this study clearly shows the importance of some economic variables and demographic indicators on team values. The most important variables on team values were revenue, key players, championship, and GDP. The level of the impact of those variables depends on the team. It is important to note that team value is not only influenced by the performance statistics. Other factors such as GDP and the population of the city should also be considered when invesigating team values in the analysis. It might be misleading to use only performance-based variables when analyzing the team values. Moreover, this study emphasizes the importance of analyzing the economic variables according to the team values. The results of the study could be valuable for owners and managers, but more research should be conducted addressing the impact of economic and demographic indicators on the team values. Our model could help managers and owners on their strategies to enhance team value and they can be prepared for different competitive scenarios.

1. Van Liedekerke M. Determinants of Franchise Value in the National Basketball Association (Doctoral dissertation, Duke University Durham).
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10. Alves LA, Kramer HH, Tschaffon PB, de Mello JS. Assessing efficiency and setting benchmarks for NBA teams through DEA and DMU clustering. InProceedings of the 4th International Conference on Mathematics in Sport 2013.
22. Vine D. The value of sports franchises. Wharton Business School. Unpublished paper. 2004.
24. Team RC. R: A language and environment for statistical computing.
26. Hadley W. Ggplot2: Elegrant graphics for data analysis. Springer; 2016.

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BMJ Open Sport Exerc Med
v.4(1); 2018

Epidemiology of sports injuries in basketball: integrative systematic review

Carlos vicente andreoli.

1 Department of Orthopedics and Traumatology, Universidade Federal de São Paulo (UNIFESP), Escola Paulista de Medicina, São Paulo, Brazil

Bárbara Camargo Chiaramonti

Elisabeth biruel, alberto de castro pochini, benno ejnisman, moises cohen.

2 Universidade Federal de São Paulo (UNIFESP), Escola Paulista de Medicina, São Paulo, Brazil

Introduction

Basketball is a contact sport with complex movements that include jumps, turns and changes in direction, which cause frequent musculoskeletal injuries in all regions of the body.

This is an integrative systematic review of the epidemiology of musculoskeletal injuries in basketball.

This is an integrative review based on the following sources of information: PubMed/MEDLINE, Embase, LILACS, BBO-Biblioteca Brasileira de Odontologia, IBECS-Índice Bibliográfico Espanhol em Ciências da Saúde, nursing journals, dental journals and core clinical journals in the last 10 years with studies addressing the general epidemiology of sports injuries in basketball.

In total, 268 articles were selected, of which 11 were eligible for the integrative review. A total of 12 960 injuries were observed, most of which occurred in the lower limbs (63.7%), with 2832 (21.9%) ankle injuries and 2305 (17.8%) knee injuries. Injuries in the upper limbs represented 12%–14% of the total injuries. Children and adolescents received head injuries more often compared with the other age and skill categories. In the adult category, there was an increased prevalence of injuries in the trunk and spine. In the upper limbs, hands, fingers and wrists were affected more frequently than the shoulders, arms and forearms. In the masters’ category, there was an increase in the incidence of thigh injuries.

The lower limbs were the most affected, with the ankle and knee joints having the highest prevalence of injuries regardless of gender and category. Further randomised studies, increased surveillance and epidemiological data collection are necessary to improve knowledge on sports injuries in basketball and to validate the effectiveness of preventive interventions.

Created in the USA more than a century ago by James Naismith, 1 basketball has become one of the most popular sports in the world, particularly in the USA. 2 3 In Brazil, basketball is one of the four most popular sports according to the Ministry of Sports. 4

Despite all the benefits resulting from participation in sports—such as improved body composition, cardiorespiratory function, increased strength, improved self-esteem/psychosocial well-being, weight control, and less abuse of alcohol and drugs, among others 5–7 —participating in a sport with so much physical demand, such as basketball, where the athlete performs repetitive jumps during games and training, abrupt changes in direction, running and deceleration, 8 may result in a greater risk of injury. This leads to an increase in health expenses and visits to doctors and hospitals, reduction in court time, and increased risk of new injuries. 9 10

Several studies have already been published describing injuries in basketball. Some focus on professional athletes, 11 others focus on college students 12 or high school students, 13 14 and others on adult athletes. 15 Some studies focus only on a specific region of the body or a specific diagnosis, such as concussion, 16 shoulder 17 or ankle injury, 18 and many compare injury rates between sexes. 19

The understanding of basketball injury epidemiology is an important first step in the development of targeted, evidence-based interventions to provide recommendations for injury prevention. The objective of this study was to perform an integrative review of the epidemiology of musculoskeletal injuries in basketball.

Methodology

Literature search.

An electronic search was performed in the following databases: PubMed/MEDLINE, Embase, LILACS, and thematic databases included in the Portal de Pesquisa da Biblioteca Virtual em Saúde (Virtual Health Library Research Portal): BBO-Biblioteca Brasileira de Odontologia (Brazilian Library of Dentistry) and IBECS-Índice Bibliográfico Espanhol em Ciências da Saúde (Spanish Bibliographic Index in Health Sciences). Other sources of information were also included, such as nursing journals, dental journals, core clinical journals, and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Brazilian Federal Agency for the Support and Evaluation of Graduate Education) Thesis Bank, which is a representative of the so-called grey literature and internet sites. Language or region filters were not included. The elaboration of the search strategies gave the research greater sensitivity by combining the terms extracted from the DeCS/MesH and synonyms: basketball, epidemiology, athletic injuries, sprains and strains. Thus, the eligibility criterion was to identify articles that discussed the general epidemiology of sports injuries in basketball.

To determine whether a study should be included, the titles and abstracts of all references obtained were evaluated by two medical reviewers. The extracted studies were assessed based on the inclusion and exclusion criteria. The inclusion criteria were as follows: (1) articles published in the last 10 years; (2) studies addressing the general epidemiology of sports injuries in basketball; and (3) athletes of any age, of both sexes, and professional, amateur and recreational basketball practitioners. The exclusion criteria were as follows: (1) review articles; (2) case reports; (3) articles dealing with Paralympic sports; (4) studies on the general epidemiology of sports injuries, including basketball, that did not present specific injury percentages for each sport; (5) items that solely addressed some type of specific basketball injury, for example, articles reporting knee injuries in basketball, or basketball dental injuries and basketball concussions; (6) articles that presented in the results section the percentages of the injuries without specifying the region of the body (eg, foot, ankle, knee and so on), referring to only general terms as upper limbs, lower limbs and trunk; and (7) articles that only addressed more prevalent injuries in emergency departments or surgical basketball injuries because these data reveal a portion of possible basketball injuries rather than overall totality/epidemiology. The possible inconsistencies were identified, discussed and resolved by consensus.

Procedures for creating the database

To create this database, the following steps were taken: (1) extraction of the number of injuries detailed by the site of each study—when the study did not present the total number of injuries but did report the relative frequency, the absolute frequencies were computed by multiplying the total number of injuries by the relative frequencies; (2) checking and rechecking of total injuries in each study; and (3) organisation of the total absolute frequencies, by sex and by category of the participant.

Eleven studies were included in this review, as shown in figure 1 . The main characteristics of the studies included in the integrative review are described in table 1 .

An external file that holds a picture, illustration, etc.
Object name is bmjsem-2018-000468f01.jpg

Selection of the studies surveyed in the electronic databases and grey literature. Preferred Reporting Items for Systematic Reviews and Meta-Analyses algorithm. BBO, Biblioteca Brasileira de Odontologia (Brazilian Library of Dentistry); IBECS, Índice Bibliográfico Espanhol em Ciências da Saúde (Spanish Bibliographic Index in Health Sciences).

Main characteristics of the studies included in the integrative review on basketball epidemiology

*The study did not present the total number, but rather the relative frequency; the absolute frequencies were computed by multiplying the total number of injuries by the relative frequencies.

HRQoL, health-related quality of life; PA, physical activity; SI, sports injuries.

The objectives of the 11 studies included in the analysis ( table 1 ) were as follows: to verify the association between game schedule and injuries; to compare basketball with other sports/physical activities or sports; to compare subsequent injuries among sports; to verify differences in injuries between sexes; to compare injury patterns in emergency departments and in the athletic training setting; to assess the incidence of injuries; and to assess the association between physical activity level, physical activity dependence and injuries with the dimensions related to the perception of health-related quality of life and health. Despite the different objectives of the included studies, in all studies it was possible to verify the number of basketball injuries in each part of the body, either through the absolute number that was indicated in the study or through the absolute frequencies multiplied by the total number of injuries. In this way, the following results were obtained.

Inferential procedures

In the inferential analysis, the following software were used: R (V.3.3.2) and RStudio (V.1.0.136). The ‘meta’ library was adopted for the estimation of meta-analysis models. The overall proportions of knee and ankle and foot injuries were estimated, along with the respective CIs and weights for each study. Tests were also performed to verify whether the OR between injuries at different sites was the same (OR=1). For this analysis, the knee was adopted as a reference site.

Characteristics of the identified studies

Descriptive statistics of the studies.

A total of 268 articles were selected, of which 11 articles were eligible for the integrative review. Of the studies included in the study, it was possible to extract the number of absolute injuries in females in seven studies. 20–26 For males, five studies were used to extract the number of injuries. 20 24 26–28 Two other studies did not specify the gender, and the data were only used for the total sum of the injuries. 29 30

With regard to the region of the participants, seven (63.6%) studies were from the USA, two (18.2%) were from Brazil, one (9.1%) was from France and one (9.1%) was from Nigeria. With regard to the age and level of skill category, most of the studies addressed injuries in adolescents (45.5%, 5 studies), followed by injuries in professionals (36.4%, 4 studies, table 1 ). Children and masters were cited in only one study each, which represented 9.1% and 9.1% of the studies, respectively.

Descriptive statistics of injuries

In total, 12 960 injuries were computed and extracted from the studies ( table 2 ). The knee and the ankle were the most affected sites, with 2832 (21.9%) and 2305 (17.8%) injuries, respectively. When analysed separately, this trend was repeated for injuries in both females and males. With regard to injuries in females, 19.5% of injuries occurred in the ankle and 20.6% occurred in the knee. The third most affected region in females was the thigh, hip and leg, which accounted for 17.5% of injuries. In males, 28.4% of injuries occurred in the ankle and foot, followed by the thigh, hip and leg (19.3%) and the knee (17.5%). When the injuries were analysed according to the age/level of skill category, the site with the most injuries in children and adolescents was the ankle and the foot (37.7%, 2807 injuries), followed by the knee (16.3%, 1214 injuries), head and neck (13.7%, 1024 injuries), and the hands, fingers and wrists (8.9%, 662 injuries). For professional athletes, the injury frequencies were as follows: 24.8% (1310 injuries) of injuries occurred in the foot and the ankle, followed by the thigh, hip and leg with 1074 injuries (20.4%), the knee with 19.5% (1027 injuries), and the trunk and spine with 586 injuries (11.1%). Only one study referred to the masters’ category, and in this study 31.4% of the injuries (75 injuries) occurred in the thigh, hip and leg, followed by the knees with 64 injuries (26.8%) and the ankle and the foot with 39 injuries (16.3%) ( table 2 ).

Injury percentages by anatomical segments in relation to total number, female sex, male sex, children and adolescents, professionals, and masters

*Includes all studies, whether for females, males or both.

†For comparison and simplification purposes, seven lesions classified as head and chest in the original study were included in the head and neck category.

‡Includes all studies presenting discrimination of lesions for females.

§Includes all studies, whether for females, males or both for children and adolescents.

¶Includes all studies, whether for females, males or both for professional athletes.

**Only one study addresses a masters’ category.

Inferential analysis

The proportions of injuries were estimated by meta-analysis models, which weighted injury frequencies by study sizes. Figure 2 presents the proportions of knee and ankle and foot injuries estimated by fixed-effects and random-effects models and the CIs and weight of each study in this estimation. Notably, there is significant heterogeneity in the studies, measured by the I 2 statistic (95% and 97%, respectively). The estimated proportion of knee injuries was 0.18 (95% CI 0.17 to 0.19) for the fixed-effects model and 0.22 (95% CI 0.18 to 0.27) for the random-effects model. The estimated proportion of ankle and foot injuries was 0.33 (95% CI 0.32 to 0.33) for the fixed-effects model and 0.29 (95% CI 0.24 to 0.34) for the random-effects model.

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Object name is bmjsem-2018-000468f02.jpg

Proportions of knee and ankle and foot injuries estimated by fixed-effects and random-effects models and the CIs and weight of each study in this estimation knee.

ORs between injury sites

The ORs between injuries were also estimated using a meta-analysis model, using knee injuries as the reference, which was the most frequent site of injury. With ankle and foot combined, this category of injuries proportionally exceeded that of knee injuries. However, this category is less specific.

The results of the OR comparison tests in the fixed-effect models are presented in table 3 . The test evaluates whether the OR is equal to 1 and rejects the hypothesis in cases of low p values (p<0.05). All OR values were greater than 1, except for the ankle and foot, which had a value less than 1. The p value for all comparisons was statistically significant (p<0.0001). Thus, the probability of knee injury was greater than the probability of injury to the thigh, hip and leg, head and neck, trunk and spine, shoulder arm and forearm, and hands, fingers and wrists. The probability of knee injury was only lower than the probability of ankle or foot injury. It was not possible to calculate the comparisons of knee with ankle only and knee with foot only using the set of studies.

Estimated values of OR compared with knee injuries, limits of the 95% CI and p value for the hypothesis of OR=1

In this study, more than 12 000 basketball injuries were analysed from the 11 included studies, and the results showed that there were more injuries in the lower limbs (63.7% of the injuries), regardless of gender (male, 65.2%; female, 68.4%) or level (professionals 64.7%, master 74.5%, and children and adolescents 62.5%). These data are in accordance with the literature. 31–33 In a Women’s National Basketball Association and NBA six-season retrospective study, Deitch et al 24 concluded that the lower limbs (65%) were the most common site of injury in basketball. Of the 5272 injuries of the professional category included in this study, 3411 occurred in the lower limbs, representing 64.7% of the total injuries reported.

According to the specific anatomical region, the largest proportion of injuries occurred in the ankle (2832 injuries, 21.9%), followed by the knee (2305 injuries, 17.8%). Most authors point to the ankle as the most common site of injury 13 14 31–34 ; however, some authors report that the knee is the most affected region. 35 36

When analysed separately, 19.5% of injuries occurred in the ankle and 20.6% in the knee in females, whereas these values were 14.6% and 17.5% in males, respectively. In the children and adolescent category, ankle injuries accounted for 25.6% and knee injuries accounted for 16.3%. In professionals, ankle and knee injuries accounted for 17.5% and 19.5% of all injuries, respectively. Finally, in master athletes, knee injuries accounted for 26.8%, and foot and ankle injuries accounted for 16.3%. It was not possible to differentiate between foot and ankle injuries for the masters’ category. In that category, only one study and a small number of injuries were found compared with the other categories. The percentages in knee and ankle injuries varied between the sexes and the levels of sport, making it difficult to affirm which is the most prevalent, as previously described.

Both knee and ankle injuries are the most prevalent. As shown by the inferential analysis and the OR values ( table 3 ), which uses knee injuries as a reference and compares it with the other anatomical regions categorised in this study, the probability of knee injury occurring is higher than that of all regions except for the ankle and foot, which is statistically significant because in all comparisons p was <0.001.

Because basketball is a sport that involves sudden changes in direction, side shifts, jumps, and more importantly landings, these results are not surprising. It would be logical and expected that injuries in the lower limbs would be the most prevalent. 8

With regard to upper limb injuries, injuries to the hands, fingers and wrists (1133, 8.7%) predominated over shoulder, arm and forearm injuries (585, 4.5%). 37 This was observed in all categories analysed: for females, hands, fingers and wrists represented 8.6% (369) of injuries, and shoulders, arms and forearms represented 4.2% (182) of injuries; for males, hands, fingers and wrists represented 8.4% (386) of injuries, and shoulders, arms and forearms represented 5.8% (267) of injuries. For the age/level of skill categories, the data were similar except for the masters’ category, which presented essentially the same number of injuries in the hands, fingers and wrists, and in the shoulder, arm and forearm. However, for this category, only one study and a small number of injuries were reported. For children and adolescents, injuries in the hands, fingers and wrists represented 8.9% (662) of injuries, and injuries in the shoulders, arms and forearms represented 3.2% (238) of injuries. For professionals, injuries in the hands, fingers and wrists represented 8.6% (454) of injuries, and injuries in the shoulder, arm and forearm represented 6.2% (328) of injuries. For masters, injuries in the hands, fingers and wrists represented 7.1% (17) of injuries, and injuries in the shoulders, arms and forearms represented 7.9% (19) of injuries. These data are also consistent with the literature, with some studies reporting 12%–14% of injuries occurring in the upper limbs. 33 38

The percentage of injuries in the upper limbs increases when the sample is obtained from emergency departments, as reported by the studies. 39 40 When hand, finger and wrist injuries were analysed separately among children and adolescents and professionals, 662 injuries occurred in children and adolescents and 454 occurred in professionals. These numbers represent 50.81% and 49.19% of all injuries in children and adolescents and professionals, respectively. Therefore, for these categories, the probability of injury to the hands, fingers and wrists is the same.

Considering the importance of the increase in the diagnosis of concussions, a brief analysis of the injuries in this anatomical region is valid, although combined with neck injuries. In total, there were 1468 injuries in this region, representing 11.3% of total injuries. In females, 417 injuries (9.7%) occurred in this region, and in males 384 injuries (8.3%) occurred in this region. Because in some studies it was not possible to differentiate injuries by sex, the sum of injuries between males and females was not equal to the total injuries reported. The value reported by the studies for the injuries in these anatomical regions varied between 8.9% and 14%. 24 31–33

Excluding the masters’ class, which had a much smaller number of reported injuries, head and neck injuries were compared between the professional and children and adolescents categories, with 437 and 1024 injuries, respectively. Proportionally, in relation to the total number of injuries reported by each category, these numbers represent 62% and 38% of injuries in children and adolescents and adults, respectively. Therefore, there is a tendency for children and adolescents to suffer more head and neck injuries than those in the professional category. There is also a study showing that this is a trend in teenage male basketball players because of the increase in the level of physical contact now observed among players of this category. 31

A total of 975 injuries occurred in the trunk and spine, representing 7.5% of all injuries. Of these 975 injuries, only 371 occurred in children and adolescents, and 586 occurred in professionals. Of the total trunk and spine injuries reported, 31% and 69% occurred in children and adolescents and professionals, respectively. A higher prevalence of trunk and spine injuries was observed in professionals.

Only 5% of all injuries reported for children and adolescents occurred in the trunk and spine. For professionals, this number was 11.1%. According to Starkey 33 in a study on NBA players, only 6.9% of all injuries occurred in this region; however, only injuries to the lumbar and thoracic spine were counted, and other injuries that occurred in the trunk were not considered. This fact could explain the observed difference between the values reproduced by this study and the study by Starkey. 33

Other authors have obtained results for adolescents in which trunk and spine injuries account for 11.4% to 13.5% of all injuries. 31 32 In this study, there was not a category for adolescents only, which could explain the difference in results: adolescents may have a greater proportion of injuries in the trunk and spine compared with the group of children and adolescents together. Because adolescence is an intermediary phase between the child and the adult phases, it seems logical that adolescents have a higher spinal injury rate than children. The trunk and spine was the fourth most prevalent injury region in professionals. For the masters’ category, only 18 trunk and spine injuries were reported; this number is very small compared with the other categories. Again, this occurred because there was only one study reporting injuries in the masters’ category.

Limitations of the study

Among the existing limitations is the fact that after applying the search strategy, only one study was found in the masters’ category and it included only male athletes, accounting for a total of 239 injuries. In addition, only one study in the children category was also found, with only 84 injuries in 162 female athletes. For the descriptive statistics of the injuries, the study with children was included along with the adolescents’ category, resulting in 7449 injuries among children and adolescents.

Another limitation was the set-up of the database: because each study used a standard to divide the injuries between the various parts of the body, some regions were common, such as the knee. However, the injuries were often grouped into categories such as the foot and ankle and trunk and spine. These regions were divided according to the previous tables, minimising possible errors, but at times it was impossible to differentiate injuries grouped into specific categories, such as separating foot and ankle injuries in some studies. Other times, injuries classified as head and thorax in the original study were included in the head and neck category.

The most adequate technique for the calculation of this epidemiology would be the injury index given the athlete’s exposure (in game, training or total) per thousand hours. Limitations prevented calculating the index of injuries due to the athlete’s exposure, given the different calculation methods. To obtain this index, each athlete’s hours spent training or playing and the number of injuries must be considered. Not all studies have reported this injury index given the athlete’s exposure. Additionally, there was great heterogeneity in the studies, measured by the I 2 statistic (95% and 97%, respectively).

Knowledge of the general epidemiology of basketball injuries is a first step for effective preventive measures to be implemented to reduce the incidence of injuries and their losses, including expenses associated with doctors, hospitals and athletes’ leave of absence. The lower limbs are the most affected injury region in basketball players, regardless of sex and category. Within anatomical regions, knee and ankle injuries are the most prevalent. The probability of injury to the hands, fingers and wrist is the same for children, teenagers and professional adults. In the category of children and adolescents, there was a higher prevalence of head and neck injuries compared with the other categories. For professionals, there was a higher prevalence of trunk and spine injuries.

Contributors: Federal University of São Paulo.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Patient consent for publication: Not required.

Provenance and peer review: Not commissioned; internally peer reviewed.

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basketball, game played between two teams of five players each on a rectangular court, usually indoors. Each team tries to score by tossing the ball through the opponent's goal, an elevated horizontal hoop and net called a basket. (Read James Naismith's 1929 Britannica essay on his invention of basketball.) The only major sport strictly of U.S. origin, basketball was invented by James ...
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ORIGINAL RESEARCH article

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Conflict of Interest Statement

A friendly pat on the back can improve performance in basketball

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College basketball is changing. UConn’s crushing superiority stays the same

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Title: Can Women’s Basketball Sustain its March Madness Success? A Sports Management Professor Answers.

Ask a Professor: La Quita Frederick on March Madness, NIL and the Future of Women’s Sports

Why are more people watching women’s basketball this year than in years past? Is this year’s surge in popularity just an anomaly with superstars like Caitlin Clark and Angel Reese, or will this trend continue?

Is women’s basketball seeing a surge in the U.S. mainly, or is this a growing global trend?

Is this success in college women’s basketball being replicated in other sports? How?

How has NIL affected the popularity of women’s sports relative to men’s sports?

How do you feel personally about this surge in interest in women’s basketball?

Who are you rooting for in the NCAA tournament?

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Examination of National Basketball Association (NBA) team values based on dynamic linear mixed models

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Epidemiology of sports injuries in basketball: integrative systematic review

Bárbara Camargo Chiaramonti

Introduction

Methodology

Procedures for creating the database

Inferential procedures

Characteristics of the identified studies

Descriptive statistics of injuries

Inferential analysis

ORs between injury sites

Limitations of the study

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