![]() Y = "Long Midrange FG%\n(Shots taken between 14 ft. Labs(x = "Short Midrange FG%\n(Shots taken between 4 - 14 ft.)", # add axis titles, plot titles, subtitles, and caption Plot.title = element_text(face = 'bold'), Name %in% c("LeBron James", "Stephen Curry", "Jrue Holiday", "Khris Middleton")), # add labels for players that meet certain criteria Long_mid_range_accuracy = long_mid_range_fgm / long_mid_range_fga) %>%įilter(short_mid_range_fga >= 100 & long_mid_range_fga >= 100) Long_mid_range_fgm = sum(long_mid_range_fgm, na.rm = TRUE), Long_mid_range_fga = sum(long_mid_range_fga, na.rm = TRUE), Short_mid_range_accuracy = short_mid_range_fgm / short_mid_range_fga, Short_mid_range_fgm = sum(short_mid_range_fgm, na.rm = TRUE), Summarise(short_mid_range_fga = sum(short_mid_range_fga, na.rm = TRUE), LongMidRangeFGA, LongMidRangeFGM, LongMidRangeAccuracy, LongMidRangePctAssisted) %>% ShortMidRangeFGA, ShortMidRangeFGM, ShortMidRangeAccuracy, ShortMidRangePctAssisted, Select(Name, EntityId, Minutes, FG2A, FG3A, I’m not going to go line by line here, but here’s the code for how to re-create the other chart from this week’s newsletter, which showed players career FG% on short and long midrange shots during the postseason. Ggsave("RidgeLinePlot.png", w = 6, h = 6, dpi = 300) Subtitle = "Minimum 50 Uninterrupted FTA | 2020 - 2021 Regular Season", Title = "Real Time Elapsed Between Consecutive Free Throw Attempts", Labs(x = "Real Time Elapsed (In Seconds)", # add axis titles, plot title, subtitle, and caption Plot.title = element_text(face = 'bold')) + Today’s tutorial is going to go over how I got the data on timestamps during NBA games to measure how long Giannis Antetokounmpo spends at the free throw line relative to the rest of the league. Ggplot(aes(x = elp, y = fct_reorder(playerNameI, avgtime))) + It’s 5 a month or 50 for a full year and you get access to the full archive of R tutorials that you can view online at. You can fiddle with the bandwidth option to make the distributions smoother (higher bandwidth value) or less smooth (smaller bandwidth value) if you want. But first, we need to get a list of all the game IDs from this season, which we can get with package to draw little neon-colored distributions for each player and then order them by their average time elapsed between free throws. ![]() The fastest way to get all the data is with a function. To make our chart we need the play-by-play data for every game from the regular season. Here’s a snapshot of what it should look like: Json_resp <- fromJSON(content(res, "text"))ĭf <- ame(json_resp]]) Res <- GET(url = url, add_headers(.headers=headers)) The following code uses the headers we specified and extracts the play-by-play data and stores it in a dataframe called df. `X-NewRelic-ID` = 'VQECWF5UChAHUlNTBwgBVw=', `Accept` = 'application/json, text/plain, */*', ![]() But first, we need to specify some headers in advance so that when we make a request to the NBA’s API we don’t get timed out ( h/t Ryan Davis ). To get the data in more traditional format, like a dataframe, we just need to run a few lines of code in R. The data, like most stuff on, is stored in JSON format. ![]()
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