| PLAYER_ID | RANK | PLAYER | TEAM_ID | TEAM | GP | MIN | FGM | FGA | FG_PCT | FG3M | FG3A | FG3_PCT | FTM | FTA | FT_PCT | OREB | DREB | REB | AST | STL | BLK | TOV | PTS | EFF | Season |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1629029 | 1 | Luka Doncic | 1610612742 | DAL | 70 | 37.5 | 11.5 | 23.6 | 0.487 | 4.1 | 10.6 | 0.382 | 6.8 | 8.7 | 0.786 | 0.8 | 8.4 | 9.2 | 9.8 | 1.4 | 0.5 | 4 | 33.9 | 36.9 | 2023-24 |
| 203507 | 2 | Giannis Antetokounmpo | 1610612749 | MIL | 73 | 35.2 | 11.5 | 18.8 | 0.611 | 0.5 | 1.7 | 0.274 | 7 | 10.7 | 0.657 | 2.7 | 8.8 | 11.5 | 6.5 | 1.2 | 1.1 | 3.4 | 30.4 | 36.4 | 2023-24 |
| 1628983 | 3 | Shai Gilgeous-Alexander | 1610612760 | OKC | 75 | 34 | 10.6 | 19.8 | 0.535 | 1.3 | 3.6 | 0.353 | 7.6 | 8.7 | 0.874 | 0.9 | 4.7 | 5.5 | 6.2 | 2 | 0.9 | 2.2 | 30.1 | 32.2 | 2023-24 |
| 1628973 | 4 | Jalen Brunson | 1610612752 | NYK | 77 | 35.4 | 10.3 | 21.4 | 0.479 | 2.7 | 6.8 | 0.401 | 5.5 | 6.5 | 0.847 | 0.6 | 3.1 | 3.6 | 6.7 | 0.9 | 0.2 | 2.4 | 28.7 | 25.6 | 2023-24 |
| 201142 | 5 | Kevin Durant | 1610612756 | PHX | 75 | 37.2 | 10 | 19.1 | 0.523 | 2.2 | 5.4 | 0.413 | 4.8 | 5.6 | 0.856 | 0.5 | 6.1 | 6.6 | 5 | 0.9 | 1.2 | 3.3 | 27.1 | 27.7 | 2023-24 |
| 1626164 | 6 | Devin Booker | 1610612756 | PHX | 68 | 36 | 9.4 | 19.2 | 0.492 | 2.2 | 6.1 | 0.364 | 6 | 6.7 | 0.886 | 0.8 | 3.7 | 4.5 | 6.9 | 0.9 | 0.4 | 2.6 | 27.1 | 26.7 | 2023-24 |
A look into three-pointers in the NBA.
Abstract
The prominence of the three-point shot in the NBA is currently a hot topic in the sports world. When the three-point shot was first introduced in the 1979-80 NBA season, the shot was looked at, for quite a while, as a novelty shot. Coaches, players, and executives did not strategize their teams and/or game plans around the three-point shot at all. In fact, many discouraged players from shooting them at all. So what has changed? How much of a impact has the three-point shot had on the scoring the NBA today? Digging deeper into that question, I found that the percentage of shots taken and made from three point land has increase gradually over the course of the last 24 seasons. Notably, I found a significant jump in average three-point attempts by the top 20 scorers in the NBA from the 2015 season to the 2016 season (3.370 - 5.125).
Introduction
The Data
In the making of this project, I extracted data from 3 main sources. 2 of the most influential packages I worked with were BasketballAnalyzeR and HoopR. Both these packages come from GitHub.com. The other source of data I used was BasketballReference.com, the data I used was obtained via webscraping.
Sample Oberservations
Observations for top 6 scorers in the NBA for the 2022-23 season. I used this data set to construct a visualization of the average number of three pointer attempted by the top 20 NBA players over the last 24 seasons.
Variables of Interest
These variables were the only ones used I used throughout my project.
| Variables |
|---|
| PLAYER_NAME |
| EVENT_TYPE |
| PLAYER_ID |
| SHOT_TYPE |
| PTS |
| EFF |
| FG3M |
| FG3A |
| FG3_PCT |
| Season |
Visualization
seasons_choice <- paste0(substr(seq(2023, 1999), 1, 4), "-", substr(seq(2024, 2000), 3, 4)) |> as.factor()
`2000's` <- paste0(substr(seq(2008, 1999), 1, 4), "-", substr(seq(2009, 2000), 3, 4))
`2010's` <- paste0(substr(seq(2018, 2009), 1, 4), "-", substr(seq(2019, 2010), 3, 4))
season_alt <- paste0(seq(2000,2024)) |>
as.factor()top_scorers_df <- tibble(Player = character(),
PTS = numeric(),
Season = character(),
stringsAsFactors = FALSE)
for (season in seasons_choice) {
players <- nba_leagueleaders(season = season,
stat_category = "PTS",
per_mode = "PerGame")
players <- players[["LeagueLeaders"]] |>
mutate(PTS = as.numeric(PTS))
players <- players |>
arrange(desc(PTS)) |>
slice(1:30)
top_scorers_df <- rbind(top_scorers_df,
transform(players, Season = season))
}
top_scorers_df <- top_scorers_df |>
mutate(`Season` = as.factor(`Season`),
`FG3A` = as.numeric(`FG3A`))top_scorers_plot <- top_scorers_df |>
mutate(DECADE = case_when(Season %in% `2000's` ~ "2000s",
Season %in% `2010's` ~ "2010s",
.default = "Early 2020s")) |>
group_by(Season, DECADE) |>
summarise(`Average 3 Point Attempt Per Game` = mean(`FG3A`)) |>
bind_cols(season_alt) |>
rename(`Season Alt` = `...4`) |>
rename(Year = `Season Alt`) |>
mutate(`Average 3 Point Attempt Per Game` = round(`Average 3 Point Attempt Per Game`, 3))`summarise()` has grouped output by 'Season'. You can override using the
`.groups` argument.
New names:timeplot <- ggplot(data = top_scorers_plot, aes(x = `Year`, y = `Average 3 Point Attempt Per Game`, group = DECADE, color = DECADE, label = `Year`)) +
geom_point() +
geom_line() +
facet_grid(~DECADE, scales = "free_x") +
theme_bw() +
theme(legend.position = "bottom",legend.title = element_blank(), legend.background = element_rect(fill = "white", color = "black"), axis.text.x = element_blank(), axis.ticks.x = element_blank()) + labs(x = "Season") +scale_color_viridis_d()
ggplotly(timeplot, tooltip = c("label", "y"))In this visualization, the data was tidyed for the top 30 scorers per game for the last 24 NBA seasons. The graph visualizes the average three pointers attempted per game from those 30 players and plots the points over the course of 24 seasons. The interactivity of the plot allows the user to hover over the points and see what year and the data point represents.
From this plot, I found that the greatest jump over the last 24 seasons, in attempted three pointers per game occurred two years in a row (2015-2016 & 2016-2017). This most likely had to do with Steph Curry’s run of championships and success heaving up a high volume of three-pointers. Although the average attempts are at a record high, this current season actually produced the lowest average, for the top 30 scorers, in the last 6 years. It will be interesting to see if this decrease in three-pointers attempted becomes a trend for years to come.
Conclusion
In the future, I would like to be able to develop predictive models and run statistical tests to come up with more conclusive results.