<div id="toc" style="background: #f9f9f9;border: 1px solid #aaa;display: table;margin-bottom: 1em;pa...View More<div id="toc" style="background: #f9f9f9;border: 1px solid #aaa;display: table;margin-bottom: 1em;padding: 1em;width: 350px;"><p class="toctitle" style="font-weight: 700;text-align: center;">Content</p><ul class="toc_list"><li><a href="#toc-0">Looker Vs Tableau: An In-depth Data Analysis Showdown 2024</a></li><li><a href="#toc-1">Data Description</a></li><li><a href="#toc-2">Associated Data</a></li></ul></div><br/><img class='wp-post-image' style='display: block;margin-left:auto;margin-right:auto;' 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width="601px" alt="top basketball performance analysis methods for businesses"/><br/>Organizations like the Houston Rockets, Golden State Warriors, and San Antonio Spurs achieved notable success by leveraging analytics to optimize roster construction, player development, and in-game tactics. With the groundwork laid by basic metrics as well as the influence of the "Moneyball" phenomenon, the NBA entered an era of unprecedented creativity in statistical analysis. This period saw the emergence of advanced metrics that provided a more nuanced comprehension of player performance and team dynamics. Therefore, improving the psychological quality of athletes, permitting them to maintain a rational and sober psychological quality all the time, and ensuring the normal performance of ball skills are linked to the ultimate score of the game. The template was developed to be used post-event, with the ability to extract data as total frequency counts or as successive, discrete possessions.<br/><h2 id="toc-0">Looker Vs Tableau: An In-depth Data Analysis Showdown 2024</h2><br/>The second improvement consists in using algorithmic modeling techniques in order to obtain the scoring probabilities’ estimates. For sports enthusiasts and experts alike, understanding the analytics in basketball could be a game-changer. Simply put, it is a method used to evaluate a player’s performance, potential, and the team’s overall strategy using statistical data. The process involves collecting and analyzing vast volumes of basketball metrics to create informed decisions about players, tactics, and game strategies. In the dynamic world of sports, data-driven insights have grown to be a game-changer, revolutionizing the way teams and individuals analyze and enhance their performance. Basketball, a fast-paced and strategic sport, has embraced the power of artificial intelligence (AI), computer vision, and deep learning to gain a competitive edge.<br/><h3 id="toc-1">Data Description</h3><br/><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width="602px" alt="top basketball performance analysis methods for businesses"/><br/>In this blog, we are going to explore how these advanced technologies are transforming basketball player and team analysis. From recognizing player actions to evaluating team strategies, AI-driven sports analysis is reshaping the future of basketball. As basketball analytics continued to evolve, technological advancements played a crucial role in expanding the scope and depth of statistical analysis in the NBA.<br/><h2 id="toc-2">Associated Data</h2><br/>The current study showed that basketball postures clearly differentiate elite Belgian basketball players based solely on basketball independent tests. In this study, we utilize play-type data with interaction information to categorize native and foreign players within the CBA league into 14 and five distinct offensive roles, respectively, employing a clustering approach. The offensive roles of two categories of native players, whose attributes emphasized both teamwork and off-ball movement, showed a significant influence on team performance.<br/> <a href="https://telegra.ph/Affordable-Lms-Solutions-For-2024-8-Top-Picks-Compared-12-10">https://telegra.ph/Affordable-Lms-Solutions-For-2024-8-Top-Picks-Compared-12-10</a>; Therefore, in addition to confronting the experts with the data obtained, we also asked if they consider that after a timeout, the initial offensive resource should go through a screen. Four of the six coaches interviewed stated that this should not always be the case and even shown surprise at the high result achieved. In their justifications for such a percentage, we emphasized that “the players of that season liked to play the shows a lot and in the end, we ended up playing that resource extra” (coach 5). A fact also confirmed by the polar coordinate of the interviews (Figure 4B, quadrant I, categories D211, D2121, and D2131) where strategies implemented to the players of the squad and its features are adapted. Many truthors affect the stability of psychological quality in basketball games, such as the progress of the basketball game and the difference in score.<br/>As analytics became increasingly integrated into the fabric of society, the NBA found itself at the forefront of a data revolution that might transform the way the game was played and coached. One of the main element takeaways from "Moneyball" was the emphasis on objective analysis along with the willingness to challenge traditional notions of player value. In basketball, this translated into a shift away from traditional box score statistics towards more advanced metrics that provided a more comprehensive comprehension of player contributions. One of the earliest proponents of basketball analytics was initially Bill James, whose groundbreaking work in baseball statistics inspired a brand new generation of analysts across various sports. Although James focused primarily on baseball, his emphasis on objective analysis and the search for undiscovered truths resonated with basketball enthusiasts wanting to apply similar principles with the game of hoops.<br/><ul><li>We have verified that also in these cases, the tuning parameters do not affect much the final result from a graphical viewpoint.</li><li>This study will seek to identify whether artificial neural networks can handle providing additional insights with regards to the position-specific characteristics found in basketball.</li><li>In football, combined with some anthropometric measures, relative age is the most important factor for talent identification. <a href="https://inpaintblog.werite.net/the-six-fundamental-building-hindrances-of-strong-loved-ones-businesses-the">https://inpaintblog.werite.net/the-six-fundamental-building-hindrances-of-strong-loved-ones-businesses-the</a>; </li></ul><br/>Before we proceed, it is worth noting the fact that out-of-sample error and the graphical appeal are intimately linked to each other but, as well, they are not completely overlapped and assess the map from different perspectives. In fact, in presence of overfitting, it is very likely that the map will be undecipherable, nevertheless the same cannot be said in presence of underfitting. In addition, from a genuinely statistical point of view, both assessments will be made with two profoundly different approaches.<br/>The test demonstrated perfect agreement (K1.00) and zero percentage error with 12 categories and almost perfect agreement (K0.981–0.993) and in the five per cent error limit (0.50–1.50%) with five categories (Table 3). The Shot Clock Remaining category recorded the lowest Weighted Kappa coefficient (K0.981) and highest mistake amount (1.50%) resulting from three disagreements. Inter-observer agreement reported using Cohen’s Weighted Kappa (K) and percentage error between agreed observation (Ob3), the coach’s observation (Ob4), plus the performance analyst intern’s observation (Ob5). Diagram showing the systematic research process for developing a new efficiency analysis template [adapted from James et al. (2005) and Thomson et al. (2013)]. From the selected publications, organizations and contents the following data including year of study, characteristic of evaluation, evaluation and results/findings/conclusions were extracted. The components of the PESTLE analysis are then analyzed and given a score from 0-10, where 10 is considered favorable for your business and 0 can be unfavorable.<br/>However, it had been not until the advent of the internet as well as the proliferation of data-driven platforms that basketball analytics truly began to take off. Websites like Basketball-Reference.com and HoopsHype.com provided followers and analysts with access to some wealth of statistical data, empowering them to conduct their very own analyses and draw insights from raw data. Fourth, operational definitions were developed for each one of the 109 variables using various resources (Frogley, 2010; Federation International Basketball Association, 2014; International Wheelchair Basketball Federation, 2014).
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