Search for a players name to view an extensive player profile including projections and custom averages. 1.Rank Traffic rank of si.Website Domain, incl.Category Similarweb.Change Change in ra.1nba.comSports > Basketball2sports.sina.cnSports > Basketball23basketballreference.Sports > BasketballView 47 more rowsJorge Lorenzo 1*, Alberto Lorenzo 1, Daniele Conte 2 and Mario Giménez 1Fantasy Basketball Player Analysis. Click on any stat to review video clips of those moments. Interactive match reports uncover insights and help you and your teams gain the competitive edge. Who can see my basket ball analysis iSportsAnalysis online basket ball analysis software makes it easy to create interactive reports for your coaches and players to engage with.Our datathe most robust in DFSadjusts in real-time based on changing information. Build and backtest DFS models, analyze trends, and ultimately leverage everything we offer to build winning lineups. 1Sport Science Department, Universidad Politécnica de Madrid, Madrid, SpainThe premium tools and analytics platform supports each sport without the need for multiple resources. Basketball Prospectus.com, an on-based online basketball analysis site. We compare players minutes against their average over the past 7 days and display the biggest increases and decreases in playing time.Advanced metrics is the term for the empirical analysis of sports, particularly statistics.
Basketball Analytics Sites Software Makes It![]() The results showed a positive trend in most of the investigated players in assists (91% of cases) and free throw percentages (73% of cases). Each variable was individually investigated with a customized excel spreadsheet assessing individual variations and career trends were calculated. 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. ![]() 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). (2013) included also free throws as an important technical performance indicator. (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. 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). 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. 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. 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, players’ experience might play a fundamental role in improving players’ game related statistics effectiveness. Therefore, the aim of this study was to descriptively analyze TPI changes throughout the career of expert basketball players, assessing the possible performance trend. 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. (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. In basketball, Baker et al. 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). External users of financial statementsThese 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. ProcedureThe 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. 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). (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. Figures 1– 6 are an example of the individual points and trendlines obtained via the Hopkins spreadsheet and that were later analyzed.The aim of this study was to analyze the trends TPIs throughout the career of expert basketball players. 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. 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). 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). Statistical AnalysisAll statistical analyses were performed with a customized excel spreadsheet specifically developed to monitor individual changes and trends in a rigorous quantitative way ( Hopkins, 2017). 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. Usb access denied registryAlso, 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. 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.
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