Developer diary for 0.1.2 (a package for soccer analytics viz): Implementing Github Actions CI tools (codecov, lintr, etc.The cursed Morgan Stanley Covid-19 visualization.New paper in Computational Brain and Behavior: Sample size determination in Bayesian Linear Mixed Models.Summer School on Statistical Methods for Linguistics and Psychology, Sept.Short course and keynote on statistical methods at Ghent Summer School on Methods in Language Sciences.Batched Imputation for High Dimensional Missing Data Problems.Matrix(ncol = length(col_names) - 2, byrow = TRUE) Html_nodes("table#schedule > tbody > tr > td") %>% Html_nodes("table#schedule > tbody > tr > th") %>% Extracting / Scraping Sports Data from websites. We will need a bit of tinkering to remove the effects of that row: NHL Game Data: Game, team, player and play data including x,y coordinates measured for. The Playoffs row interferes with our web scraping. The only snag here is that the table in the month of April is slightly different, since the playoffs start that month: Next, I will extract the dates and game IDs in a similar manner. (The game_id column cannot be pulled out in this way, and so I’ve added it in manually.) Html_nodes("table#schedule > thead > tr > th") %>%Ĭol_names % work with rvest‘s functions. Year thead > tr > th", and then pull out the value of the attribute "data-stat": We can get the webpage as an xml_document object by using rvest‘s read_html function: We do that in the full R script the explanation below shows the code for scraping for the month of October. As such we will need to loop over the months and scrape the webpage for each month. If you only want the data, you can download it here in RDS format.įirst, let’s load the packages we will use for the web scraping:įrom the screenshot above, you may notice that game data for the season is split over several pages, with one page for the games in a given month. And it was surprisingly easy! In this post, I will walk through the steps for scraping top-level game data for the 2017-2018 NBA season (i.e. I recently found some spare time on my hands and decided that it was time for me to learn how to scrape data from this website. Differences in game-related statistics of basketball performance by game location for men’s winning and losing teams. Performacne difference between winning and losing basketball teams during close, balanced and unbalanced quarters. Investigating the game-related statistics and tactical profile in NCAA division I men’s basketball games. 2009 8: 458–462.Ĭonte D, Tessitore A, Gjullin A, Mackinnon D, Lupo C, Favero T. Effects of consecutive basketball games on the game-related statistics that discriminate winner and losing teams. Ibanez SJ, Garcia J, Feu S, Lorenzo A, Sampaio J. Game related statistics which discriminate between winning and losing under-16 male basketball games. Lorenzo A, Gomez MA, Ortega E, Ibanez SJ, Sampaio J. Also, overall shooting efficiency (i.e., free-throw, 2-point, and 3-point combined) accounted for 23-26% of the total percentage of explained variance. Two key game-related statistics capable of discriminating between winning and losing game outcomes were field goal percentage and defensive rebounding, accounting for 13.6% and 14.2% of the total percentage of explained variance during the regular season and 11.5% and 14.7% during post-season competitive periods. Discriminant function analysis correctly classified winning and losing game outcomes during the regular and post-season competitive periods in 82.8% and 87.2% of cases, respectively. It becomes more conservative (i.e., fewer field goal attempts, assists, steals, turnovers, and points scored), most likely due to greater defensive pressure. Despite small to moderate effect sizes, the findings suggest that NBA teams' style of play (i.e., tactical strategies) changes when transitioning from the regular to post-season competitive period. The total number of games examined in the present investigation was 3933 (3690 regular season and 243 post-season games). The data scraping technique was used to obtain publicly available NBA game-related statistics over a three-year span (2016-2019). The purpose of the present study was to examine differences in game-related statistical parameters between National Basketball Association (NBA) regular and post-season competitive periods and to determine which variables have the greatest contribution in discriminating between winning and losing game outcomes.
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