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\"The story of how a new generation of tech-savvy franchise owners is reshaping every aspect of professional sports. In the last two decades, innovation, data analysis, and technology have driven a tectonic shift in the sports business. Game of Edges is the story of how sports franchises evolved, on and off the field, from raggedly run small businesses into some of the most systematically productive companies around. In today's game, everyone from the owners to the marketing staff are using information-data-to give their team an edge. For analysts, an edge is their currency. Figuring out that bunting hurts your offense? That's an edge. So is discovering metrics that can predict the career arc of your free agent shooting guard. Or combing through a decade of ticket-buying data to target persuadable fans. These small, incremental steps move a sports franchise from merely ordinary to the leading edge. Franchises today are more than just sports; they integrate a whole suite of other businesses-television and digital content, gambling and real estate, fashion and apparel, entertainment, catering and concessions, and much more. But an optimized franchise has no room for error. Teams must do what the numbers say, reducing the element of chance, limiting those random moments of athletic heroism that make sports thrilling to watch. Optimization also means the franchise's main goal isn't championships anymore; it's keeping you, the viewer, engaged with the product. Drawing on extensive interviews with franchise owners, general managers, executives, and players, Bruce Schoenfeld introduces dynamic leaders who are radically reimagining the operations of these decades-old teams-and producing mind-boggling valuations. He joins the architects of the Golden State Warriors dynasty for an exclusive reception before tip-off. He stands among the faithful at Anfield, watching Liverpool's analytics guru size up a prized midfielder. And he watches the president of the Chicago Cubs break ground on a new DraftKings gambling parlor at Wrigley Field, not ten miles from the site of the original Black Sox betting scandal. Essential reading for anyone interested in sports, business, or technology, Game of Edges explores a world where winning the game is only the beginning\"-- Provided by publisher.

Background The impact of gambling advertisements shown during sporting events on young people is an important public health issue. While extensive research has taken place in Australia, there is still only a limited understanding of this issue in the United Kingdom (UK). Method A mixed methods study was conducted with 71 family groups comprised of 99 young people (8–16 years) and 71 adults recruited at six sites across South London, England (May–July 2018). Interviewer-assisted surveys investigated recall and awareness of sports betting brands using interviews and a magnet placement board activity developed in Australia. Quantitative data were analysed using descriptive statistics, with qualitative data interpreted using thematic analysis techniques. Results Just under half of young people ( n  = 46, 46%) and more than two thirds of adults ( n  = 49, 71%) were able, unprompted, to name at least one gambling brand. Boys had a significantly higher recall of brands than girls, as did young people who watched a lot of football on television. Almost two thirds of young people ( n  = 63, 63%) correctly placed one or more shirt sponsor magnets next to the corresponding football team, and 30% ( n  = 30) correctly placed three or more sponsors magnets next to the corresponding football team. Just under two thirds of adults ( n  = 44, 62%) correctly placed one or more shirt sponsors magnets next to the corresponding football team. Young people recalled seeing gambling advertising on television ( n  = 78), technology/screens ( n  = 49), and in association with sports teams ( n  = 43). Adults recalled seeing advertising on television ( n  = 56), on technology/screens ( n  = 37), in sports stadiums ( n  = 34), and in betting venues ( n  = 34). Over three quarters of young people ( n  = 74 out of 95 responses, 78%) and 86% of adults ( n  = 59 out of 69 responses) thought that betting had become a normal part of sport. Conclusion In order to reduce the exposure of young people to gambling advertising, policymakers in the UK should consider comprehensive approaches, similar to those applied in tobacco control, which cover all forms of advertising, including promotion and sponsorship.

This study investigates the impact of injuries on National Basketball Association (NBA) player performance over 20 seasons, using large-scale performance data and a statistical evaluation. Injury events were matched with player–game performance metrics to assess how various injury types influence short-, medium-, and long-term performance outcomes, measured across 2-, 5-, and 10-game windows. Using paired sample t-tests and Cohen’s d, we quantified both the statistical significance and effect size of changes in key performance metrics before and after injury. The analysis applies paired t-tests and Cohen’s d to quantify the statistical and practical significance of performance deviations pre- and post-injury. Our results show that while most injury types are associated with measurable performance declines, especially in offensive and defensive ratings, certain categories, such as cardiovascular injuries, demonstrate counterintuitive improvements post-recovery. These patterns suggest that not all injuries have equivalent consequences and highlight the importance of individualized recovery protocols. This work contributes to the growing field of sports injury analytics by combining statistical modeling and sports analytics to deliver actionable insights for coaches, medical staff, and performance analysts in managing player rehabilitation and optimizing return-to-play decisions.

The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5-40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling resolution associated with the windowing of data over fixed periods. Accordingly, this study compared rolling average (ROLL) and fixed-time (FIXED) epochs for quantifying the peak movement demands of international rugby union match-play as a function of playing position. Elite players from three different squads (n = 119) were monitored using 10 Hz GPS during 36 matches played in the 2014-2017 seasons. Players categorised broadly as forwards and backs, and then by positional sub-group (FR: front row, SR: second row, BR: back row, HB: half back, MF: midfield, B3: back three) were monitored during match-play for peak values of high-speed running (>5 m·s-1; HSR) and relative distance covered (m·min-1) over 60-300 s using two types of sample-epoch (ROLL, FIXED). Irrespective of the method used, as the epoch length increased, values for the intensity of running actions decreased (e.g., For the backs using the ROLL method, distance covered decreased from 177.4 ± 20.6 m·min-1 in the 60 s epoch to 107.5 ± 13.3 m·min-1 for the 300 s epoch). For the team as a whole, and irrespective of position, estimates of fixed effects indicated significant between-method differences across all time-points for both relative distance covered and HSR. Movement demands were underestimated consistently by FIXED versus ROLL with differences being most pronounced using 60 s epochs (95% CI HSR: -6.05 to -4.70 m·min-1, 95% CI distance: -18.45 to -16.43 m·min-1). For all HSR time epochs except one, all backs groups increased more (p < 0.01) from FIXED to ROLL than the forward groups. Linear mixed modelling of ROLL data highlighted that for HSR (except 60 s epoch), SR was the only group not significantly different to FR. For relative distance covered all other position groups were greater than the FR (p < 0.05). The FIXED method underestimated both relative distance (~11%) and HSR values (up to ~20%) compared to the ROLL method. These differences were exaggerated for the HSR variable in the backs position who covered the greatest HSR distance; highlighting important consideration for those implementing the FIXED method of analysis. The data provides coaches with a worst-case scenario reference on the running demands required for periods of 60-300 s in length. This information offers novel insight into game demands and can be used to inform the design of training games to increase specificity of preparation for the most demanding phases of matches.

The presented field experiment in an 11 vs. 11 soccer game set-up is the first to examine the impact of different formations (e.g. 4-2-3-1 vs. 3-5-2) on tactical key performance indicators (KPIs) using positional data in a controlled experiment. The data were gathered using player tracking systems (1 Hz) in a standardized 11 vs. 11 soccer game. The KPIs were measured using dynamical positioning variables like Effective Playing Space, Player Length per Width ratio, Team Separateness, Space Control Gain, and Pressure Passing Efficiency. Within the experimental positional data analysis paradigm, neither of the team formations showed differences in Effective Playing Space, Team Separateness, or Space Control Gain. However, as a theory-based approach predicted, a 3-5-2 formation for the Player Length per Width ratio and Pressure Passing Efficiency exceeded the 4-2-3-1 formation. Practice task designs which manipulate team formations therefore significantly influence the emergent behavioral dynamics and need to be considered when planning and monitoring performance. Accordingly, an experimental positional data analysis paradigm is a useful approach to enable the development and validation of theory-oriented models in the area of performance analysis in sports games.

Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

This study aimed to measure the differences in commonly used summary acceleration metrics during elite Australian football games under three different data processing protocols (raw, custom-processed, manufacturer-processed). Estimates of distance, speed and acceleration were collected with a 10-Hz GNSS tracking technology device from fourteen matches of 38 elite Australian football players from one team. Raw and manufacturer-processed data were exported from respective proprietary software and two common summary acceleration metrics (number of efforts and distance within medium/high-intensity zone) were calculated for the three processing methods. To estimate the effect of the three different data processing methods on the summary metrics, linear mixed models were used. The main findings demonstrated that there were substantial differences between the three processing methods; the manufacturer-processed acceleration data had the lowest reported distance (up to 184 times lower) and efforts (up to 89 times lower), followed by the custom-processed distance (up to 3.3 times lower) and efforts (up to 4.3 times lower), where raw data had the highest reported distance and efforts. The results indicated that different processing methods changed the metric output and in turn alters the quantification of the demands of a sport (volume, intensity and frequency of the metrics). Coaches, practitioners and researchers need to understand that various processing methods alter the summary metrics of acceleration data. By being informed about how these metrics are affected by processing methods, they can better interpret the data available and effectively tailor their training programs to match the demands of competition.

This study aimed to quantify the time-motion demands and intra-team movement synchronization during the pre-season matches of a professional soccer team according to the opposition level. Positional data from 20 players were captured during the first half of six pre-season matches of a Portuguese first league team. Time-motion demands were measured by the total distance covered and distance covered at different speed categories. Intra-team coordination was measured by calculating the relative phase of all pairs of outfield players. Afterwards, the percentage of time spent in the -30° to 30° bin (near-in-phase mode of coordination) was calculated for each dyad as a measure of space-time movement synchronization. Movement synchronization data were analyzed for the whole team, according to each dyad average speed and by groups of similar dyadic synchronization tendencies. Then, these data were compared according to the opponent team level (first league; second league; amateurs). Time-motion demands showed no differences in total distance covered per opposition levels, while matches opposing teams of superior level revealed more distance covered at very high intensity. Competing against superior level teams implied more time in synchronized behavior for the overall displacements and displacements at higher intensities. These findings suggest that playing against higher-level opponents (1st league teams) increased time-motion demands at high intensities in tandem with intra-team movement synchronization tendencies.

Predicting basketball game outcomes is a critical area in sports science and data analysis, providing concrete benefits for optimizing coaching strategies, improving team management, and informing betting decisions. This methodological review systematically evaluates the effectiveness of specific artificial intelligence technologies in predicting professional basketball game outcomes over the past five years from 2019 to 2024, providing detailed insights into current methodologies and identifying emerging trends and challenges in this domain. Following PRISMA-SCR guidelines, a comprehensive keyword search was conducted across four electronic bibliographic databases: PubMed, Web of Science, Scopus, and EBSCO. Studies were included if they utilized artificial intelligence techniques, focused on professional leagues, and aimed to predict game outcomes. This review incorporated 34 studies that met the predefined eligibility criteria, examining various artificial intelligence techniques used to predict professional basketball game outcomes over the past five years. The findings reveal that artificial intelligence models, particularly the multilayer perceptron neural network, achieved a high prediction accuracy of 98.90%. The random forest model, based on four factors, reached an accuracy of 93.81%, while the voting regression ensemble model achieved 93.3%. The studies underscore the importance of effective data processing and feature selection in enhancing model performance. Additionally, dynamic prediction models that adapt to real-time changes in the game were shown to be particularly useful for tactical decisions and betting strategies. Artificial intelligence significantly improves the accuracy of predicting outcomes in professional basketball games. Future research should include diverse basketball leagues and employ more advanced validation techniques to enhance model robustness and applicability. Integrating real-time data and exploring transfer learning will likely improve prediction accuracy and decision-making support.

Integrated information systems in sports have the potential to improve the efficiency of data management and support the managers’ decision-making. However, this implementation faces challenges such as inefficiency, data duplicity, and time demands. This study represents a comparative analysis of sports information systems’ implementation in four countries—Slovakia, Czech Republic, England, and Denmark. The originality of this study stems from the fact that there is currently no research background examining this issue to the same extent. This study’s methodology focuses on the identification of the benefits and challenges occurring while implementing sports information systems and performing data management and analysis. This study also focuses on the potential of these systems to support managerial decision-making in this area. Data were collected from national sports databases and other relevant sources. Verification of the hypotheses showed that the implementation of sports information systems in Slovakia is inefficient in terms of costs and technology. Nevertheless, the systems that were implemented support managerial decision-making and their success is comparable to other EU countries within the aspects studied. Following the results, the main recommendation is to ensure transparency, automation, and strategic planning in the implementation of sports information systems. Future research directions include ethical and legal issues related to the utilization of technology in sports and the improvement of the user experience.