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\"The unlikeliest of champions, the 1949-50 City College Beavers were extraordinary by every measure: City College was a tuition-free, merit-based college in Harlem known for its intellectual achievements and political radicalism rather than its athletic prowess. Only two years after Jackie Robinson broke the major league baseball color barrier (and the NBA was still segregated), every single member of the Beavers was either Jewish or African American. Yet this scrappy, come-from-nowhere team thrived in the highly competitive era when college basketball fans dwarfed the numbers that followed the professional teams. Then, less than a year after winning both the NIT and NCAA basketball tournaments in the same season--still the only team to ever have done so--the team's starting five were arrested. Charged with colluding with gamblers to shave points, these celebrated young men became symbols of disillusionment and corruption. Their dramatic story is set against the larger backdrop of post-war New York when gangsters controlled the city's illegal sports gambling, the police were on their payroll, and everyone was getting rich--except the young men actually playing the games. Yet they were the ones who took the fall when the party finally ended\"-- Provided by publisher.

As an important sports venue, the sound environment of the basketball stadium is crucial for the experience of spectators and participants. In order to improve the quality of the acoustic environment of the basketball stadium, the acoustic parameters such as reverberation time, language transmission index and background noise were used to build a comprehensive evaluation system, and the software simulation was improved and optimized. The results show that the language transmission index of most measuring points in the basketball stadium has a high consistency between the on-site measurement values and the simulation results, and the simulation results are relatively accurate. The improved language transmission index values of each measurement point have significantly increased, with an average of 0.58, an increase of 93.33% compared to before the improvement. The improvement strategy effectively improves the acoustic environment of the basketball stadium and better controls the reverberation time in the stadium. The research provides a valuable analysis of the optimization of the sound environment of college basketball stadiums, which provides some reference for the improvement of the sound environment of basketball stadiums, and also has some reference significance for the optimization of the sound environment of other sports venues or entertainment venues. Index terms college basketball stadium; acoustic environment; optimization strategy; acoustic simulation; AHP.

In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort for athletes. Data were collected from 21 collegiate basketball players, and movements such as dribbling, passing, shooting, layup, and standing still were recorded. The collected IMU data underwent preprocessing and feature extraction, followed by the application of machine learning algorithms including KNN, decision tree, Random Forest, AdaBoost, and XGBoost. Among these, the XGBoost algorithm with a window size of 250 and a 75% overlap yielded the highest accuracy of 96.6%. The system demonstrated superior performance compared to other single-sensor systems, achieving an overall classification accuracy of 96.9%. This research contributes to the field by presenting a new dataset of basketball movements, comparing the effectiveness of various feature extraction and machine learning methods, and offering a scalable, efficient, and accurate action recognition system for basketball.

Star college basketball player Caitlin Clark appeared on “Weekend Update,” on the April 13 episode of “Saturday Night Live,” the day before she’s expected to go first overall in the WBNA draft.

The purpose of this study was to evaluate the speed, agility, and explosive strength performance of elite basketball players over an 8-week plyometric training program. Fifteen elite male college basketball players in Taiwan (average age 22.16 ± 0.85 years old) were publicly recruited. All participants received 24 plyometric training courses three times per week for 8 weeks, and the courses were implemented pre- and post-test. The speed and agility test items were divided into a 20 m sprint and a T-shaped run. In the explosive strength test, a force plate was used to measure countermovement jump to understand the pre- and post-test differences in all the test indicators, including the rate of force development, time of the rate of force development, ground reaction forces for the moment of jumping, duration of passage, and jump height. It was found that, after the participants underwent the plyometric training program, the body mass index and body fat percentage were significantly reduced, the skeletal muscle mass was significantly increased, and the post-test scores for speed and agility improved significantly. All the participants exhibited a steeper gradient for the rate of force development (r = −0.816~−0.963) and a shorter time for the rate of force development (0.107~0.232 s). The ground reaction forces reached 1509.61~2387.11 Newtons. The duration of passage reached 0.643 s, and the jump height reached 0.624 m. The conclusion was that the plyometric training program can increase muscle volume in the lower limbs and legs, increase the rate of force development, and shorten the jumping time, thereby enhancing explosive strength.

Background Data are lacking regarding the risk of viral SARS-CoV-2 transmission during a large indoor sporting event involving fans utilizing a controlled environment. We sought to describe case characteristics, mitigation protocols used, variants detected, and secondary infections detected during the 2021 National Collegiate Athletic Association (NCAA) Men’s Basketball Tournament involving collegiate athletes from across the U.S. Methods This retrospective cohort study used data collected from March 16 to April 3, 2021, as part of a closed environment which required daily reverse transcription-polymerase chain reaction (RT-PCR) testing, social distancing, universal masking, and limited contact between tiers of participants. Nearly 3000 players, staff, and vendors participated in indoor, unmasked activities that involved direct exposure between cases and noninfected individuals. The main outcome of interest was transmission of SARS-CoV-2 virus, as measured by the number of new infections and variant(s) detected among positive cases. Secondary infections were identified through contact tracing by public health officials. Results Out of 2660 participants, 15 individuals (0.56%) screened positive for SARS-CoV-2. Four cases involved players or officials, and all cases were detected before any individual played in or officiated a game. Secondary transmissions all occurred outside the controlled environment. Among those disqualified from the tournament (4 cases; 26.7%), all individuals tested positive for the Iota variant (B.1.526). All other cases involved the Alpha variant (B.1.1.7). Nearly all teams ( N  = 58; 85.3%) reported that some individuals had received at least one dose of a vaccine. Overall, 17.9% of participants either had at least one dose of the vaccine or possessed documented infection within 90 days of the tournament. Conclusion In this retrospective cohort study of the 2021 NCAA Men’s Basketball Tournament closed environment, only a few cases were detected, and they were discovered in advance of potential exposure. These findings support the U.S. Centers for Disease Control and Prevention (CDC) guidelines for large indoor sporting events during the COVID-19 pandemic.

Background This study aimed to explore the quarter-by-quarter variations in external and internal load and their interrelationships throughout basketball games among collegiate basketball players. Methods This longitudinal observational study analyzed data from 18 official games, yielding a total of 470 data points (quarter 1 (Q1): 112, Q2: 128, Q3: 122, Q4: 108) from 14 male players of the Chinese University Basketball Association. Only players without injury records who completed at least 15 min per game and 5 min per quarter in at least five games were included in the final analyses. Catapult S7 devices and Ratings of Perceived Exertion (RPE) were employed to assess both external and internal load variables. Linear mixed-effects models and repeated-measures correlations were utilized to assess differences in external and internal load between backcourt and frontcourt players, as well as to examine the relationship between RPE and objective load metrics across game quarters. Decision tree visualizations were employed to analyze load thresholds and identify key quarter-specific features that differentiate player performance. Results Backcourt players exhibited higher RPE (Q1: p  < 0.01, ES  = 0.72; Q2: p  < 0.001, ES  = 1.3), PlayerLoad (PL)·min − 1 (Q1: p  < 0.001, ES  = 1.0; Q2: p < 0.001, ES  = 1.0), and IMA COD (Q1: p  < 0.05, ES  = 0.69; Q2: p  < 0.01, ES  = 0.67) in the first and second quarters compared to frontcourt players. In contrast, frontcourt players showed increased IMA Accel in the third ( p < 0.01, ES = -0.65) and fourth quarters ( p  < 0.05, ES = -0.90) compared to backcourt players. Additionally, significant correlations were found between RPE and PL (Q1: r  = 0.51, p  < 0.001; Q2: r  = 0.46, p  < 0.001; Q3: r  = 0.57, p < 0.001; Q4: r  = 0.61, p  < 0.01) and explosive efforts (Q1: r  = 0.61, p  < 0.01; Q2: r  = 0.46, p < 0.001; Q3: r  = 0.44, p  < 0.001; Q4: r  = 0.45, p  < 0.01) across all quarters, underscoring the connection between physical exertion and athlete perception. Finally, decision tree with heatmap visualizations identified RPE, PL, and PL·min − 1 as critical feature distinguishing the physical demands of each quarter. Conclusion This study highlights position-specific physical demands across game quarters in basketball, with backcourt players showing higher intensity (RPE, PL·min − 1 , and IMA COD) early in games and frontcourt players exhibiting greater IMA Accel later, supporting tailored training and recovery strategies based on RPE and objective load metrics.

This conceptual essay discusses how university athletic departments and local churches can collaborate in providing spiritual care for intercollegiate athletes in the United States. Parachurch organizations such as Athletes in Action (AIA) and the Fellowship of Christian Athletes (FCA) play a vital role in creating and maintaining these partnerships. Additionally, a considerable body of literature is available on the importance of religiosity and spirituality in the lives of intercollegiate athletes in the United States. In the interests of helping intercollegiate student-athletes grow and develop religiously and spirituality, many sports chaplains assist student-athletes with finding a local place to worship while on campus. Chaplains that subscribe to the “urban chaplaincy/congregational model” play a significant role in bringing this task to pass. This essay highlights the primacy of building a relational bridge between university athletic departments and local churches in the United States to collaborate in intercollegiate student-athlete spiritual development and holistic care.

Basketball is one of the popular sports in colleges. Basketball injuries are a common thing, and the use of machine learning and other technologies can effectively reduce basketball injuries, which should start with prevention. Nonstandard basketball movements and lack of physical coordination will not only reduce sports efficiency for athletes but also increase the probability of injury. Therefore, effective reduction and targeted prevention of nonstandard actions are of great significance to college basketball. With the development of science and technology, artificial intelligence technology is closer to our lives. Based on the machine learning platform, this paper studies basketball injuries from the perspective of the integration of sports and medicine. Research on what aspects cause college students’ basketball injuries is needed for the future. Effectively preventing college students from being injured in basketball is an urgent problem in the field of sports medicine. To find the most suitable machine learning platform for college basketball injury research, this article will introduce three different methods for comparative analysis. The techniques used in the experiment in this paper are traditional BP neural network technology, SCG neural network technology, and RBF neural network technology. Through experiments, it is known that, through experiments, RBF neural network technical prediction accuracy rate is as high as 95.4%, which is a relatively good neural network algorithm for studying the basketball loss of college students.

Using data streams from ESPN.com (n.d.), we identified possible contingencies on timeout calling for 594 timeouts in 117 games for four Division 1 NCAA college basketball teams. We found that the probability of calling a timeout increased as the relative points scored by the opposing team increased. In addition to analyzing all timeouts inclusively, we analyzed timeouts called within the last 2 min of each half separately from those called in the rest of the game. Regardless of the window of analysis, the probability of calling a timeout increased as the relative points scored by the opposing team increased. To the extent that shots made and points scored by the opposing team were aversive to the coach (and players), the reductions that immediately followed the timeout may have functioned as negative reinforcement.