Visualizing NBA Seasons Part 3: Shootings

Seasons 1950-2017

Part 3: Shootings (FG and FT)

Introduction & Preparation

Part 1: Players

Part 2: Points

Part 4: Shootings (3-Points and Mixed)

Part 5: Skills

Part 6: Roles

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Setup Stage

Loading necessary packages.

library(dplyr)
library(tidyr)
library(ggplot2)
library(RColorBrewer)
library(plotly)
library(ggridges)
library(kableExtra)
library(wordcloud2)
library(forcats)

Create additional functions

# Mode average
getmode <- function(v) {
   uniqv <- unique(v)
   uniqv[which.max(tabulate(match(v, uniqv)))]
}

Load the dataset

Dataset taken from: https://www.kaggle.com/drgilermo/nba-players-stats/version/2

If you need to see the glossary and the data tidying process, please visit first part here.

Loading datasets…

NBA <- read.csv("NBA_TidySet.csv")
NBA_Scaled <- read.csv("NBA_Scaled_TidySet.csv")
NBA$Pos <- factor(NBA$Pos, levels = c("C", "PF", "SF", "SG", "PG"))
PosColorCode <- c("C"="#FF0000", "PF"="#FFA500", "SF"="#DDDD00" ,"SG"="#0000FF", "PG"="#32CD32")

Displaying main table

NBA

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SHOOTING

In this session we explore shooting statistics of the NBA players. As discussed in the previous session, there are 3 basic shooting category in basketball: Field Goals (FG), Three-Points Field Goal (3P), and Free-Throw (FT). We will analyze in-depth each one of them.

Since there’s size limit to upload to rpubs, I must divide this shooting category into two seperated session. Here I will make exploratory analysis on the general shooting stats in the league, Field GOals (FG) and Free Throws (FT). 3-Points and the mixed shooting stats will be for the next session.

Again, I use basic RGB as color-code for shooting type: red for FT, green for 3P and blue for FG.

Start from a wider scope, let’s see the general shooting comparison in the league.

NOTE: While I don’t have time or an inspiration about the narrative, Lorem ipsum texts will be in display.

Shooting Percentage Density

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NBA %>%
    group_by(Pos) %>%
    summarise(FG2m = sum(X2P, na.rm=T),
              FG2a = sum(X2PA, na.rm=T),
              FG2. = round(FG2m/FG2a, 3),
              FG3m = sum(X3P, na.rm=T),
              FG3a = sum(X3PA, na.rm=T),
              FG3. = round(FG3m/FG3a, 3),
              FTm = sum(FT, na.rm=T),
              FTa = sum(FTA, na.rm=T),
              FT. = round(FTm/FTa, 3)) %>%
    select(-c(FG2m:FG2a, FG3m:FG3a, FTm:FTa)) %>%
    mutate(Pos = cell_spec(Pos, color = "white", align = "c", 
                    background = factor(Pos, c("C", "PF", "SF", "SG", "PG"), 
                                        PosColorCode))) %>%
    kable(escape = FALSE, align = "c", caption = "Average Shooting Percentages by Position") %>%
    kable_styling("striped", full_width = F, position = "float_left") %>%
    column_spec(1:4, width = "5em")

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NBA %>%
    group_by(Pos, Player) %>%
    summarise(FG2m = sum(X2P, na.rm=T),
              FG2a = sum(X2PA, na.rm=T),
              FG2. = round(FG2m/FG2a, 3),
              FG3m = sum(X3P, na.rm=T),
              FG3a = sum(X3PA, na.rm=T),
              FG3. = round(FG3m/FG3a, 3),
              FTm = sum(FT, na.rm=T),
              FTa = sum(FTA, na.rm=T),
              FT. = round(FTm/FTa, 3)) %>%
    select(-c(FG2m:FG2a, FG3m:FG3a, FTm:FTa)) %>%
    gather(Parameter, Pct, FG2.:FT., -c(Pos, Player)) %>%
    ggplot(aes(Pct, Pos, fill=Parameter)) +
    ggtitle("Shooting Percentage Density by Position") +
    geom_density_ridges(alpha=0.25) +
    scale_y_discrete(expand = c(0.01, 0), limits = rev(levels(NBA$Pos))) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_fill_manual("Parameter", values = c("blue", "green", "red")) +
    xlab("Percentage") +
    ylab("Position") +
    theme(legend.position="bottom")

NBA Shooting by Year

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NBA %>%
    group_by(Year) %>%
    summarise(FG2m = sum(X2P, na.rm=T),
              FG2a = sum(X2PA, na.rm=T),
              FG2. = round(FG2m/FG2a, 3),
              FG3m = sum(X3P, na.rm=T),
              FG3a = sum(X3PA, na.rm=T),
              FG3. = round(FG3m/FG3a, 3),
              FTm = sum(FT, na.rm=T),
              FTa = sum(FTA, na.rm=T),
              FT. = round(FTm/FTa, 3)) %>%
    select(-c(FG2m:FG2a, FG3m:FG3a, FTm:FTa)) %>%
    gather(Parameter, Value, FG2.:FT., -Year) %>%
    ggplot() +
    geom_line(aes(Year, Value, group=Parameter, color=Parameter), size = 1.2, alpha = 1) +
    ggtitle("Shooting Percentage by Year") +
    scale_x_continuous(breaks = seq(1950, 2020, 10)) +
    scale_color_manual("Parameter",
                       values = c("blue", "green", "red"),
                       labels = c("FG2%", "FG3%", "FT%")) +
    guides(group=FALSE) +
    theme(legend.position="bottom")

Hit/Missed Shoots by Position

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NBA %>%
    group_by(Pos) %>%
    summarise(FG2m = sum(X2P, na.rm = T),
              FG2a = sum(X2PA, na.rm = T) - FG2m,
              FG3m = sum(X3P, na.rm = T),
              FG3a = sum(X3PA, na.rm = T) - FG3m,
              FTm = sum(FT, na.rm = T),
              FTa = sum(FTA, na.rm = T) - FTm) %>%
    gather(Parameter, Count, FG2m:FTa, -Pos) %>%
    mutate(ShootType = as.factor(ifelse(Parameter %in% c("FG2m", "FG2a"), "FG2",
                               ifelse(Parameter %in% c("FG3m", "FG3a"), "FG3",
                                      "FT")))) %>%
    group_by(Pos, ShootType) %>%
    ggplot(aes(ShootType, Count, fill = Parameter)) + 
    geom_bar(stat = 'identity', position = 'stack') +
    facet_grid(~ Pos) +
    ggtitle("Hit/Missed Shoots by Position") +
    guides(fill=FALSE) +
    scale_fill_manual(values=c("#7F7FFF", "#0000FF", "#66B266", "#008000", "#FF4C4C","#FF0000"))

Shooting Hit-Miss by Year

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HitMissFG2 <- NBA %>% group_by(Year) %>%
    summarise(ShootType = "FG2",
              Hit = sum(X2P, na.rm = T) / sum(G),
              Miss = sum(X2PA, na.rm = T) / sum(G))
HitMissFG3 <- NBA %>% group_by(Year) %>%
    summarise(ShootType = "FG3",
              Hit = sum(X3P, na.rm = T) / sum(G),
              Miss = sum(X3PA, na.rm = T) / sum(G))
HitMissFT <- NBA %>% group_by(Year) %>%
    summarise(ShootType = "FT",
              Hit = sum(FT, na.rm = T) / sum(G),
              Miss = sum(FTA, na.rm = T) / sum(G))
HitMissShot <- rbind(HitMissFG2, HitMissFG3, HitMissFT)
HitMissShot %>%
    ggplot(aes(x=Year,
               y=Hit,
               ymin=Hit,
               ymax=Miss,
               fill=ShootType,
               color=ShootType)) + 
    geom_line(size=2) + 
    geom_ribbon(alpha=0.3) +
    ylab("Hit / Missed Shots") +
    scale_linetype_manual(name = "", values = c(1, 1, 1)) +
    scale_color_manual(values=c("#0000FF", "#008000", "#FF0000")) +
    theme(legend.position="bottom")

The thick line shows shooting made and the thin line shows the attempts.

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Field Goals(FG)

Field Goals is anything other than Free Throws that convert to points. That means 3-points FG also included in the Field Goals stats. However, this doesn’t necessarily measure ‘shooting’ skill per se, since it also included other method of scoring such slam-dunk, lay-up, even accidental scoring.

Field Goals % Annual League Leaders

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TopFG. <- NBA %>%
    filter(FG. > 0) %>%
    group_by(Year, Player) %>%
    summarise(Pos = getmode(Pos),
              Team = getmode(Tm),
              Games = sum(G),
              MPG = round(sum(MP)/Games, 1),
              PPG = round(sum(PTS)/Games, 1),
              FGm = sum(FG),
              FGa = sum(FGA),
              FG. = round(FGm/FGa, 3)) %>%
    filter(ifelse(Year %in% c(1950:1951, 1961-1962), FGm >= 200,
                             ifelse(Year %in% c(1952:1955, 1963-1964, 1966), FGm >= 210,
                               ifelse(Year %in% 1956:1959, FGm >= 230,
                                 ifelse(Year %in% 1960:1961, FGm >= 190,
                                   ifelse(Year %in% c(1965, 1967:1968), FGm >= 220,
                                     ifelse(Year %in% 1969, FGm >= 230,
                                       ifelse(Year %in% 1970, Games >= 70 & FGa >= 700,
                                         ifelse(Year %in% 1971:1972, FGa >= 700,
                                           ifelse(Year %in% 1973:1974, FGa >= 560,
                                             ifelse(Year %in% 1999, FGm >= 183,
                                               ifelse(Year %in% 2012, FGm >= 241,
                                                 FGm >= 300)))))))))))) %>%
    group_by(Year) %>%
    summarise(Player = Player[which.max(FG.)],
              Team = Team[which.max(FG.)],
              Pos = Pos[which.max(FG.)],
              Games = Games[which.max(FG.)],
              MPG = MPG[which.max(FG.)],
              PPG = PPG[which.max(FG.)],
              FGm = FGm[which.max(FG.)],
              FGa = FGa[which.max(FG.)],
              FG. = max(FG., na.rm = T)) %>%
    arrange(desc(Year))
TopFG.  %>%
    mutate(Pos = cell_spec(Pos, color = "white", align = "c", 
                    background = factor(Pos, c("C", "PF", "SF", "SG", "PG"), 
                                        PosColorCode))) %>%
    kable(escape = FALSE, caption = "Annual League Leaders Field Goals %") %>%
    kable_styling("striped", "hover", full_width = T) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(2, bold = T) %>%
    column_spec(10, bold = T, color = "white", background = "#777777") %>%
    scroll_box(width = "100%", height = "300px")

Annual League Leaders Field Goals %

Year	Player	Team	Pos	Games	MPG	PPG	FGm	FGa	FG.
2017	DeAndre Jordan	LAC	C	81	31.7	12.7	412	577	0.714
2016	DeAndre Jordan	LAC	C	77	33.7	12.7	357	508	0.703
2015	DeAndre Jordan	LAC	C	82	34.4	11.5	379	534	0.710
2014	DeAndre Jordan	LAC	C	82	35.0	10.4	348	515	0.676
2013	DeAndre Jordan	LAC	C	82	24.5	8.8	314	488	0.643
2012	Tyson Chandler	NYK	C	62	33.2	11.3	241	355	0.679
2011	Nene Hilario	DEN	C	75	30.5	14.5	402	654	0.615
2010	Dwight Howard	ORL	C	82	34.7	18.3	510	834	0.612
2009	Shaquille O'Neal	PHO	C	75	30.0	17.8	512	841	0.609
2008	Andris Biedrins	GSW	C	76	27.3	10.5	340	543	0.626
2007	Mikki Moore	NJN	C	79	26.4	9.8	308	506	0.609
2006	Shaquille O'Neal	MIA	C	59	30.6	20.0	480	800	0.600
2005	Shaquille O'Neal	MIA	C	73	34.1	22.9	658	1095	0.601
2004	Shaquille O'Neal	LAL	C	67	36.8	21.5	554	948	0.584
2003	Eddy Curry	CHI	C	81	19.4	10.5	335	573	0.585
2002	Shaquille O'Neal	LAL	C	67	36.1	27.2	712	1229	0.579
2001	Shaquille O'Neal	LAL	C	74	39.5	28.7	813	1422	0.572
2000	Shaquille O'Neal	LAL	C	79	40.0	29.7	956	1665	0.574
1999	Shaquille O'Neal	LAL	C	49	34.8	26.3	510	885	0.576
1998	Shaquille O'Neal	LAL	C	60	36.2	28.3	670	1147	0.584
1997	Gheorghe Muresan	WSB	C	73	25.3	10.6	327	541	0.604
1996	Gheorghe Muresan	WSB	C	76	29.5	14.5	466	798	0.584
1995	Chris Gatling	GSW	PF	58	25.3	13.7	324	512	0.633
1994	Shaquille O'Neal	ORL	C	81	39.8	29.3	953	1591	0.599
1993	Cedric Ceballos	PHO	SF	74	21.7	12.8	381	662	0.576
1992	Buck Williams	POR	PF	80	31.5	11.3	340	563	0.604
1991	Buck Williams	POR	PF	80	32.3	11.7	358	595	0.602
1990	Mark West	PHO	C	82	29.3	10.5	331	530	0.625
1989	Dennis Rodman	DET	SF	82	26.9	9.0	316	531	0.595
1988	Kevin McHale	BOS	PF	64	37.3	22.6	550	911	0.604
1987	Kevin McHale	BOS	PF	77	39.7	26.1	790	1307	0.604
1986	Steve Johnson	SAS	C	71	25.7	13.8	362	573	0.632
1985	James Donaldson	LAC	C	82	29.2	11.3	351	551	0.637
1984	Artis Gilmore	SAS	C	64	31.8	15.3	351	556	0.631
1983	Artis Gilmore	SAS	C	82	34.1	18.0	556	888	0.626
1982	Artis Gilmore	CHI	C	82	34.1	18.5	546	837	0.652
1981	Artis Gilmore	CHI	C	82	34.5	17.9	547	816	0.670
1980	Cedric Maxwell	BOS	SF	80	34.3	16.9	457	750	0.609
1979	Cedric Maxwell	BOS	SF	80	37.1	19.0	472	808	0.584
1978	Bobby Jones	DEN	PF	75	32.5	14.5	440	761	0.578
1977	Kareem Abdul-Jabbar	LAL	C	82	36.8	26.2	888	1533	0.579
1976	John Shumate	PHO	PF	75	26.3	11.7	332	592	0.561
1975	Don Nelson	BOS	SF	79	26.0	14.0	423	785	0.539
1974	Bob McAdoo	BUF	C	74	43.0	30.6	901	1647	0.547
1973	Wilt Chamberlain	LAL	C	82	43.2	13.2	426	586	0.727
1972	Wilt Chamberlain	LAL	C	82	42.3	14.8	496	764	0.649
1971	Johnny Green	CIN	PF	75	28.6	16.7	502	855	0.587
1970	Johnny Green	CIN	PF	78	29.2	15.6	481	860	0.559
1969	Wilt Chamberlain	LAL	C	81	45.3	20.5	641	1099	0.583
1968	Wilt Chamberlain	PHI	C	82	46.8	24.3	819	1377	0.595
1967	Wilt Chamberlain	PHI	C	81	45.5	24.1	785	1150	0.683
1966	Wilt Chamberlain	PHI	C	79	47.3	33.5	1074	1990	0.540
1965	Wilt Chamberlain	SFW	C	73	45.2	34.7	1063	2083	0.510
1964	Jerry Lucas	CIN	PF	79	41.4	17.7	545	1035	0.527
1963	Wilt Chamberlain	SFW	C	80	47.6	44.8	1463	2770	0.528
1962	Walt Bellamy	CHP	C	79	42.3	31.6	973	1875	0.519
1961	Wilt Chamberlain	PHW	C	79	47.8	38.4	1251	2457	0.509
1960	Kenny Sears	NYK	PF	64	32.8	18.5	412	863	0.477
1959	Kenny Sears	NYK	PF	71	35.2	21.0	491	1002	0.490
1958	Jack Twyman	CIN	SF	72	30.2	17.2	465	1028	0.452
1957	Neil Johnston	PHW	C	69	36.7	22.8	520	1163	0.447
1956	Neil Johnston	PHW	C	70	37.1	22.1	499	1092	0.457
1955	Larry Foust	FTW	C	70	32.3	17.0	398	818	0.487
1954	Ed Macauley	BOS	C	71	39.3	18.9	462	950	0.486
1953	Ed Macauley	BOS	C	69	42.1	20.3	451	997	0.452
1952	Paul Arizin	PHW	SF	66	44.5	25.4	548	1222	0.448
1951	Alex Groza	INO	C	66	NA	21.7	492	1046	0.470
1950	Alex Groza	INO	C	64	NA	23.4	521	1090	0.478

To qualify for league leaders, a player must meet the Rate Statistic Requirements

Generating plot…

meanFG.Year <- NBA %>%
    group_by(Year) %>%
    filter(FG. > 0) %>%
    summarise(FG. = sum(FG)/sum(FGA))

TFG. <- TopFG. %>%
    ggplot() +
    geom_bar(aes(Year, FG., fill = Pos, text = paste("Player:", Player)), stat = "identity") +
    geom_line(data=meanFG.Year, aes(Year, FG., linetype = "Average line"), color = "black") +
    ggtitle("Annual Field Goal Percentage Leaders") +
    geom_hline(yintercept = mean(TopFG.$FG.), col = "blue", alpha = 0.5) +
    scale_x_continuous(breaks = seq(1950, 2017, 10)) +
    scale_fill_manual("Pos", values = PosColorCode) +
    theme(legend.position="none")

pTFG <- ggplotly(TFG.)

#api_create(pTFG, filename = "AnnualFG.Leaders")

click image for interactive plotly graph

• Average top FG% is 0.567.
• Highest top FG% is 0.727, in season 1973.
• Lowest top FG% is 0.447, in season 1957.
• The Centers dominated the league by 72.1%, followed by PF with 17.6%, then SF
  complete the leaderboard with 10.3%. No guard ever be in FG% leaderboard, this because guards territory is outside, while post players (C / PF) positioned near the basket have greater chance of success in delivering the ball to the basket.

All-Time Field Goals % Leaders

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AllTimeFG. <- NBA %>%
    filter(FG. > 0) %>%
    group_by(Player) %>%
    summarise(Pos = getmode(Pos),
              Team = getmode(Tm),
              ActiveYears = paste(getmode(YearStart), "-", getmode(YearEnd)),
              Games = sum(G),
              MPG = round(sum(MP) / Games, 1),
              PPG = round(sum(PTS) / Games, 1),
              FGm = sum(FG),
              FGa = sum(FGA),
              FG. = round(FGm/FGa, 3)) %>%
    filter(FGm >= 2000) %>%
    arrange(desc(FG.), desc(FGm)) %>%
    mutate(Rank = min_rank(desc(FG.))) %>%
    select(Rank, everything())
AllTimeFG. %>%
    head(n=10) %>%
    mutate(Pos = cell_spec(Pos, color = "white", align = "c", 
                    background = factor(Pos, c("C", "PF", "SF", "SG", "PG"), 
                                        PosColorCode))) %>%
    kable(escape = FALSE, caption = "All-Time Field Goals % Leaders") %>%
    kable_styling("striped", full_width = T) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(2, bold = T) %>%
    column_spec(11, bold = T, color = "white", background = "#777777") %>%
    scroll_box(width = "100%", height = "300px")

All-Time Field Goals % Leaders

Rank	Player	Pos	Team	ActiveYears	Games	MPG	PPG	FGm	FGa	FG.
1	DeAndre Jordan	C	LAC	2009 - 2018	673	27.7	9.1	2487	3674	0.677
2	Artis Gilmore	C	CHI	1972 - 1988	909	32.7	17.1	5732	9570	0.599
3	Tyson Chandler	C	CHI	2002 - 2018	1033	28.4	8.7	3362	5660	0.594
4	Dwight Howard	C	ORL	2005 - 2018	954	34.9	17.5	5988	10237	0.585
5	Shaquille O'Neal	C	LAL	1993 - 2011	1207	34.7	23.7	11330	19457	0.582
6	Mark West	C	PHO	1984 - 2000	1089	18.6	5.7	2528	4355	0.580
7	Amir Johnson	PF	TOR	2006 - 2018	745	22.3	7.5	2339	4073	0.574
8	Darryl Dawkins	C	PHI	1976 - 1989	726	23.7	12.0	3477	6079	0.572
8	Steve Johnson	C	KCK	1982 - 1991	622	21.9	11.8	2841	4963	0.572
10	James Donaldson	C	DAL	1981 - 1995	957	27.4	8.6	3105	5442	0.571

To qualify for league leaders, a player must meet the Rate Statistic Requirements

Generating plot…

LabelFix <- AllTimeFG. %>%
    head(10) %>%
    mutate(Player = fct_reorder(Player, desc(FG.), fun=mean),
           FGa = FGa-FGm,
           FG. = paste(FG. * 100, "%")) %>%
    gather(Parameter, Count, FGm:FGa, -c(Player, FG.)) %>%
    filter(Parameter == "FGa")
    
AllTimeFG. %>%
    head(10) %>%
    mutate(Player = fct_reorder(Player, desc(FG.), fun=mean),
           FGa = FGa-FGm) %>%
    gather(Parameter, Count, FGm:FGa, -c(Player, FG.)) %>%
    ggplot(aes(Player, Count, fill = Parameter)) + 
    geom_bar(stat = 'identity', position = 'stack') +
    geom_label(aes(label = FG.,
                   y = 21000),
               data = LabelFix,
               stat = 'identity',
               position = "stack",
               fill = "#FF4000",
               color = "#00FFFF",
               size = 4.5) +
    ggtitle("All-Time Field Goals Percentage Leaders") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    xlab("") +
    scale_fill_manual(labels=c("FG-missed  ", "FG-made"), values=c("#7F7FFF", "#0000FF")) +
    theme(legend.title=element_blank()) +
    theme(legend.position="bottom")

Since the leaderboard is totally occupied by post players, naturally I’d like to see the best FG% in each position, so here it is.

TopFG.Pos <- AllTimeFG. %>%
    group_by(Pos) %>%
    summarise(Player = Player[which.max(FG.)],
              Team = Team[which.max(FG.)],
              ActiveYears = ActiveYears[which.max(FG.)],
              Games = Games[which.max(FG.)],
              MPG = MPG[which.max(FG.)],
              PPG = PPG[which.max(FG.)],
              FGm = FGm[which.max(FG.)],
              FGa = FGa[which.max(FG.)],
              FG. = FG.[which.max(FG.)])

Best FG% by Position

C

DeAndre Jordan

0.677

• FG: 2487 / 3674
• PPG: 9.1
• Years: 2009 - 2018

PF

Amir Johnson

0.574

• FG: 2339 / 4073
• PPG: 7.5
• Years: 2006 - 2018

SF

Cedric Maxwell

0.546

• FG: 3433 / 6293
• PPG: 12.5
• Years: 1978 - 1988

SG

Lewis Lloyd

0.524

• FG: 2172 / 4144
• PPG: 13.2
• Years: 1982 - 1990

PG

Maurice Cheeks

0.523

• FG: 4906 / 9374
• PPG: 11.1
• Years: 1979 - 1993

Field Goals per 36 Minutes

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AllTimeFGma <- NBA %>%
    group_by(Player) %>%
    filter(sum(G) >= 400) %>%
    summarise(Pos = getmode(Position),
              Team = getmode(Tm),
              ActiveYears = paste(getmode(YearStart), "-", getmode(YearEnd)),
              Games = sum(G),
              Minutes = sum(MP),
              MPG = round(Minutes / Games, 1),
              PPG = round(sum(PTS)/sum(G), 1),
              FGm_36 = round((sum(FG) / Minutes) * 36, 1),
              FGm_G = round((sum(FG) / Games), 1),
              FGa_36 = round((sum(FGA) / Minutes) *36, 1),
              FGa_G = round((sum(FGA) / Games), 1),
              FG. = round(sum(FG)/sum(FGA), 3)) %>%
    select(-"Minutes")
AllTimeFGma %>%
    arrange(desc(FGm_36), desc(FG.)) %>%
    head(n=10) %>%
    mutate(Rank = min_rank(desc(FGm_36))) %>%
    select(Rank, everything()) %>%
    kable(escape = FALSE, caption = "Field Goals Made per 36 Minutes") %>%
    kable_styling("striped", full_width = T, font_size = 11) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(c(2, 13), bold = T) %>%
    column_spec(9, bold = T, color = "white", background = "#777777") %>%
    scroll_box(width = "100%", height = "300px")

Field Goals Made per 36 Minutes

Rank	Player	Pos	Team	ActiveYears	Games	MPG	PPG	FGm_36	FGm_G	FGa_36	FGa_G	FG.
1	George Gervin	SG	SAS	1973 - 1986	791	33.5	26.2	10.9	10.2	21.4	19.9	0.511
2	Michael Jordan	SG	CHI	1985 - 2003	1072	38.3	30.1	10.7	11.4	21.5	22.9	0.497
3	Walter Davis	SG	PHO	1978 - 1992	1033	27.9	18.9	10.1	7.9	19.8	15.4	0.511
3	Alex English	SF	DEN	1977 - 1991	1193	31.9	21.5	10.1	8.9	19.9	17.6	0.507
5	Kareem Abdul-Jabbar	C	LAL	1970 - 1989	1560	36.8	24.6	9.9	10.2	17.7	18.1	0.559
6	Shaquille O'Neal	C	LAL	1993 - 2011	1207	34.7	23.7	9.7	9.4	16.7	16.1	0.582
7	Bernard King	SF	NYK	1978 - 1993	874	33.7	22.5	9.6	9.0	18.5	17.3	0.518
8	Wilt Chamberlain	C	LAL	1960 - 1973	1045	45.8	30.1	9.5	12.1	17.7	22.5	0.540
9	Bob McAdoo	C	BUF	1973 - 1986	852	33.2	22.1	9.4	8.7	18.7	17.3	0.503
9	Dominique Wilkins	SF	ATL	1983 - 1999	1074	35.5	24.8	9.4	9.3	20.4	20.1	0.461

AllTimeFGma %>%
    arrange(desc(FGa_36), desc(FG.)) %>%
    head(n=10) %>%
    mutate(Rank = min_rank(desc(FGa_36))) %>%
    select(Rank, everything()) %>%
    kable(escape = FALSE, caption = "Field Goal Attempts per 36 Minutes") %>%
    kable_styling("striped", full_width = T,font_size = 11) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(c(2, 13), bold = T) %>%
    column_spec(11, bold = T, color = "white", background = "#777777") %>%
    scroll_box(width = "100%", height = "300px")

Field Goal Attempts per 36 Minutes

Rank	Player	Pos	Team	ActiveYears	Games	MPG	PPG	FGm_36	FGm_G	FGa_36	FGa_G	FG.
1	Tom Heinsohn	PF	BOS	1957 - 1965	654	29.4	18.6	8.9	7.3	22.0	18.0	0.405
2	Michael Jordan	SG	CHI	1985 - 2003	1072	38.3	30.1	10.7	11.4	21.5	22.9	0.497
3	George Gervin	SG	SAS	1973 - 1986	791	33.5	26.2	10.9	10.2	21.4	19.9	0.511
3	Elgin Baylor	SF	LAL	1959 - 1972	846	40.0	27.4	9.2	10.3	21.4	23.8	0.431
5	Pete Maravich	SG	NOJ	1971 - 1980	658	37.0	24.2	9.2	9.4	20.8	21.3	0.441
6	Dominique Wilkins	SF	ATL	1983 - 1999	1074	35.5	24.8	9.4	9.3	20.4	20.1	0.461
6	Sam Jones	SG	BOS	1958 - 1969	871	27.9	17.7	9.3	7.2	20.4	15.8	0.456
6	Clyde Lovellette	C	MNL	1954 - 1964	704	27.1	17.0	9.0	6.8	20.4	15.3	0.443
9	Rick Barry	SF	GSW	1966 - 1980	794	36.3	23.2	9.1	9.1	20.2	20.4	0.449
10	Alex English	SF	DEN	1977 - 1991	1193	31.9	21.5	10.1	8.9	19.9	17.6	0.507

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AllTimeFGma %>%
    arrange(desc(FGm_36), desc(FG.)) %>%
    mutate(Player = reorder(Player, desc(FGm_36), FUN=mean)) %>%
    head(n=10) %>%
    ggplot(aes(group = 1)) +
    geom_segment(aes(x=Player, xend=Player, y=FGm_36, yend=FGa_36), color="black") +
    geom_point(aes(Player, FGm_36, color="#FF5800"), size=5) +
    geom_point(aes(Player, FGa_36, color="#FF5800"), size=2.5) +
    geom_point(aes(Player, FGm_G, color="#009dff"), size=5, shape=18) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ggtitle("FG Made per 36 Minutes vs. FG Made per Game") +
    xlab("") +
    ylab("FG Made") +
    scale_color_manual(name="", values=c("#009dff", "#FF5800"),
                       labels=c("FGa/m per Game", "FGa/m per 36 minutes"),
                       guide = guide_legend(reverse=TRUE)) +
    theme(legend.position='none')

BIG names in Field Goals

For shooting, in general, I’d like to apply simpler approach, emphasizing percentage over made by doubling it. Field Goals will be sorted according to the following arbritary criteria :

• Annual league leader in Field Goals percentage category earn 2 point.
• Top 10 all-time leaders in Field Goals percentage category earn 2 points.
• Top 10 all-time leaders in Field Goals made category with FG% more than 55% earn 1 point.
• Top 10 all-time leaders in Field Goals made per 36 minutes category with FG% more than 55% earn 1 point.

Calculate the points and we have the rank!

# Filter total Field Goals made with FG% > .55
AllTimeFGm <- NBA %>%
    group_by(Player) %>%
    summarise(FGm = sum(FG),
              FGa = sum(FGA),
              FG. = FGm / FGa) %>%
    filter(FG. > 0.55) %>%
    arrange(desc(FGm)) %>% head(10)
# Filter Field Goals made per 36 minutes with FG% > .55
AllTimeFGm36 <- AllTimeFGma %>%
    filter(FG. >= 0.55) %>%
    arrange(desc(FGm_36)) %>%
    head(n=10)
NamesFG <- data.frame(Name = c(as.character(rep(TopFG.$Player, 2)),
                               as.character(rep(head(AllTimeFG.$Player, 10), 2)),
                               as.character(AllTimeFGm$Player),
                               as.character(AllTimeFGm36$Player)))
NamesFG %>% count(Name) %>% arrange(desc(n)) %>% 
    wordcloud2(size=0.4, color='random-light', backgroundColor="black")

---

Free Throw (FT)

In the two categories above (FG and 3P), a player can deliberately choose to make a shot attempt. However, in FT they need to be fouled by opponent player to attempt the shot.

Quite often, a player with good FG% but bad FT%, which is common for a big guy (Centers or Power Forward), will get fouled intentionally by the opponent. Shaquille O’Neal (C), with his 58.2% FG and 52.7% FT, is the most notable example for this, hence the term Hack-a-Shaq.

Now let’s get started..

Annual Free Throws % League Leaders

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TopFT. <- NBA %>%
    filter(FT. > 0) %>%
    group_by(Year, Player) %>%
    summarise(Pos = getmode(Pos),
              Team = getmode(Tm),
              Games = sum(G),
              MPG = round(sum(MP)/Games, 1),
              PPG = round(sum(PTS)/Games, 1),
              FTm = sum(FT),
              FTa = sum(FTA),
              FT. = round(FTm/FTa, 3)) %>%
    filter(ifelse(Year %in% 1950:1951, FTm >= 170,
             ifelse(Year %in% 1952:1955, FTm >= 180,
               ifelse(Year %in% 1956:1959, FTm >= 190,
                 ifelse(Year %in% 1960, FTm >= 185,
                   ifelse(Year %in% 1961:1962, FTm >= 200,
                     ifelse(Year %in% 1963:1966, FTm >= 210,
                       ifelse(Year %in% 1967:1968, FTm >= 220,
                         ifelse(Year %in% 1969, FTm >= 230,
                           ifelse(Year %in% 1970, Games >= 70 & FTa >= 350,
                             ifelse(Year %in% 1971:1972, FTa >= 350,
                               ifelse(Year %in% 1973:1974, FTa >= 160,
                                 ifelse(Year %in% 1999, FTm >= 76,
                                   ifelse(Year %in% 2012, FTm >= 100,
                                     FTm >= 125)))))))))))))) %>%
    group_by(Year) %>%
    summarise(Player = Player[which.max(FT.)],
              Team = Team[which.max(FT.)],
              Pos = Pos[which.max(FT.)],
              Games = Games[which.max(FT.)],
              MPG = MPG[which.max(FT.)],
              PPG = PPG[which.max(FT.)],
              FTm = FTm[which.max(FT.)],
              FTa = FTa[which.max(FT.)],
              FT. = max(FT., na.rm = T)) %>%
    arrange(desc(Year))
TopFT. %>%
    mutate(Pos = cell_spec(Pos, color = "white", align = "c", 
                    background = factor(Pos, c("C", "PF", "SF", "SG", "PG"), 
                                        PosColorCode))) %>%
    kable(escape = FALSE, caption = "Annual Free Throws % League Leaders") %>%
    kable_styling("striped", full_width = T) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(2, bold = T) %>%
    column_spec(10, bold = T, color = "white", background = "#777777") %>%
    scroll_box(width = "100%", height = "300px")

Annual Free Throws % League Leaders

Year	Player	Team	Pos	Games	MPG	PPG	FTm	FTa	FT.
2017	C.J. McCollum	POR	SG	80	35.0	23.0	268	294	0.912
2016	Stephen Curry	GSW	PG	79	34.2	30.1	363	400	0.908
2015	Stephen Curry	GSW	PG	80	32.7	23.8	308	337	0.914
2014	Brian Roberts	NOP	PG	72	23.2	9.4	125	133	0.940
2013	Kevin Durant	OKC	SF	81	38.5	28.1	679	750	0.905
2012	Jamal Crawford	POR	PG	60	26.9	13.9	191	206	0.927
2011	Stephen Curry	GSW	PG	74	33.6	18.6	212	227	0.934
2010	Steve Nash	PHO	PG	81	32.8	16.5	211	225	0.938
2009	Jose Calderon	TOR	PG	68	34.3	12.8	151	154	0.981
2008	Peja Stojakovic	NOH	SF	77	35.2	16.4	130	140	0.929
2007	Kyle Korver	PHI	SF	74	30.9	14.4	191	209	0.914
2006	Steve Nash	PHO	PG	79	35.4	18.8	257	279	0.921
2005	Reggie Miller	IND	SG	66	31.9	14.8	250	268	0.933
2004	Peja Stojakovic	SAC	SF	81	40.3	24.2	394	425	0.927
2003	Allan Houston	NYK	SG	82	37.9	22.5	363	395	0.919
2002	Reggie Miller	IND	SG	79	36.6	16.5	296	325	0.911
2001	Reggie Miller	IND	SG	81	39.3	18.9	323	348	0.928
2000	Jeff Hornacek	UTA	SG	77	27.7	12.4	171	180	0.950
1999	Reggie Miller	IND	SG	50	35.7	18.4	226	247	0.915
1998	Chris Mullin	IND	SF	82	26.5	11.3	154	164	0.939
1997	Mark Price	GSW	PG	70	26.8	11.3	155	171	0.906
1996	Mahmoud Abdul-Rauf	DEN	PG	57	35.6	19.2	146	157	0.930
1995	Spud Webb	SAC	PG	76	32.3	11.6	226	242	0.934
1994	Mahmoud Abdul-Rauf	DEN	PG	80	32.7	18.0	219	229	0.956
1993	Mark Price	CLE	PG	75	31.7	18.2	289	305	0.948
1992	Mark Price	CLE	PG	72	29.7	17.3	270	285	0.947
1991	Reggie Miller	IND	SG	82	36.2	22.6	551	600	0.918
1990	Larry Bird	BOS	SF	75	39.3	24.3	319	343	0.930
1989	Magic Johnson	LAL	PG	77	37.5	22.5	513	563	0.911
1988	Jack Sikma	MIL	C	82	35.6	16.5	321	348	0.922
1987	Larry Bird	BOS	SF	74	40.6	28.1	414	455	0.910
1986	Chris Mullin	GSW	SG	55	25.3	14.0	189	211	0.896
1985	Kyle Macy	PHO	PG	65	31.0	11.0	127	140	0.907
1984	Larry Bird	BOS	PF	79	38.3	24.2	374	421	0.888
1983	Calvin Murphy	HOU	PG	64	22.2	12.8	138	150	0.920
1982	Kyle Macy	PHO	PG	82	34.7	14.2	152	169	0.899
1981	Calvin Murphy	HOU	PG	76	26.5	16.7	206	215	0.958
1980	Rick Barry	HOU	SF	72	25.2	12.0	143	153	0.935
1979	Rick Barry	HOU	SF	80	32.1	13.5	160	169	0.947
1978	Rick Barry	GSW	SF	82	36.9	23.1	378	409	0.924
1977	Ernie DiGregorio	BUF	PG	81	28.0	10.7	138	146	0.945
1976	Rick Barry	GSW	SF	81	38.5	21.0	287	311	0.923
1975	Rick Barry	GSW	SF	80	40.4	30.6	394	436	0.904
1974	Ernie DiGregorio	BUF	PG	81	35.9	15.2	174	193	0.902
1973	Rick Barry	GSW	SF	82	37.5	22.3	358	397	0.902
1972	Jack Marin	BAL	SF	78	37.5	22.3	356	398	0.894
1971	Chet Walker	CHI	SF	81	36.1	22.0	480	559	0.859
1970	Flynn Robinson	MIL	PG	81	34.1	21.8	439	489	0.898
1969	Larry Siegfried	BOS	PG	79	32.4	14.2	336	389	0.864
1968	Oscar Robertson	CIN	PG	65	42.5	29.2	576	660	0.873
1967	Adrian Smith	CIN	SG	81	32.5	16.6	343	380	0.903
1966	Larry Siegfried	BOS	PG	71	23.6	13.7	274	311	0.881
1965	Larry Costello	PHI	PG	64	30.7	13.5	243	277	0.877
1964	Oscar Robertson	CIN	PG	79	45.1	31.4	800	938	0.853
1963	Larry Costello	SYR	PG	78	26.5	11.0	288	327	0.881
1962	Dolph Schayes	SYR	PF	56	26.4	14.7	286	319	0.897
1961	Bill Sharman	BOS	SG	61	25.2	16.0	210	228	0.921
1960	Dolph Schayes	SYR	PF	75	36.5	22.5	533	597	0.893
1959	Bill Sharman	BOS	SG	72	33.1	20.4	342	367	0.932
1958	Dolph Schayes	SYR	PF	72	40.5	24.9	629	696	0.904
1957	Bill Sharman	BOS	SG	67	35.9	21.1	381	421	0.905
1956	Bill Sharman	BOS	SG	72	37.5	19.9	358	413	0.867
1955	Bill Sharman	BOS	SG	68	36.1	18.4	347	387	0.897
1954	Bill Sharman	BOS	SG	72	34.3	16.0	331	392	0.844
1953	Bill Sharman	BOS	SG	71	32.9	16.2	341	401	0.850
1952	Bobby Wanzer	ROC	SG	66	37.8	15.7	377	417	0.904
1951	Joe Fulks	PHW	PF	66	NA	18.7	378	442	0.855
1950	Max Zaslofsky	CHS	SG	68	NA	16.4	321	381	0.843

To qualify for league leaders, a player must meet the Rate Statistic Requirements

Generating plot…

meanFT.Year <- NBA %>%
    group_by(Year) %>%
    filter(FT. > 0) %>%
    summarise(FT. = sum(FT)/sum(FTA))

TFT. <- TopFT. %>%
    ggplot() +
    geom_bar(aes(Year, FT., fill = Pos, text = paste("Player:", Player)), stat = "identity") +
    geom_line(data=meanFT.Year, aes(Year, FT., linetype = "Average line"), color = "black") +
    ggtitle("Annual Free Throws Percentage Leaders") +
    geom_hline(yintercept = mean(TopFT.$FT.), col = "blue", alpha = 0.5) +
    scale_x_continuous(breaks = seq(1950, 2020, 10)) +
    scale_fill_manual("Pos", values = PosColorCode) +
    theme(legend.position="none")

pTFT. <- ggplotly(TFT.)

#api_create(pTFT., filename = "AnnualFT% Leaders")

• Average top FT% is 0.903.
• Highest top FT% is 0.981, in season 2009.
• Lowest top FT% is 0.843, in season 1950.
• The PG dominated the league by 41.2%, SG with 27.9%, followed by , then SF
  added with 22.1%, and finally PF completed the leaderboard with only 7.4%. And there’s only 1 center ever in the annual FT% leaderboard, Jack Sikma with 0.922 FT.

All-Time Free Throws % Leaders

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AllTimeFT. <- NBA %>%
    filter(FT. > 0) %>%
    group_by(Player) %>%
    summarise(Pos = getmode(Pos),
              Team = getmode(Tm),
              ActiveYears = paste(getmode(YearStart), "-", getmode(YearEnd)),
              Games = sum(G),
              MPG = round(sum(MP) / Games, 1),
              PPG = round(sum(PTS) / Games, 1),
              FTm = sum(FT),
              FTa = sum(FTA),
              FT. = round(FTm/FTa, 3)) %>%
    filter(FTm >= 1200) %>%
    arrange(desc(FT.), desc(FTm)) %>%
    mutate(Rank = min_rank(desc(FT.))) %>%
    select(Rank, everything())
AllTimeFT. %>%
    head(n=10) %>%
    mutate(Pos = cell_spec(Pos, color = "white", align = "c", 
                    background = factor(Pos, c("C", "PF", "SF", "SG", "PG"), 
                                        PosColorCode))) %>%
    kable(escape = FALSE, caption = "All-Time Free Throws % Leaders") %>%
    kable_styling("striped", full_width = T) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(2, bold = T) %>%
    column_spec(11, bold = T, color = "white", background = "#777777") %>%
    scroll_box(width = "100%", height = "300px")

All-Time Free Throws % Leaders

Rank	Player	Pos	Team	ActiveYears	Games	MPG	PPG	FTm	FTa	FT.
1	Steve Nash	PG	PHO	1997 - 2014	1217	31.3	14.3	3060	3384	0.904
1	Mark Price	PG	CLE	1987 - 1998	722	29.9	15.2	2135	2362	0.904
3	Stephen Curry	PG	GSW	2010 - 2018	574	34.7	22.8	1993	2212	0.901
4	Rick Barry	SF	GSW	1966 - 1980	794	36.3	23.2	3818	4243	0.900
5	Peja Stojakovic	SF	SAC	1999 - 2011	804	33.5	17.0	2237	2500	0.895
6	Chauncey Billups	PG	DET	1998 - 2014	1043	31.6	15.2	4496	5029	0.894
6	Ray Allen	SG	MIL	1997 - 2014	1300	35.6	18.9	4398	4920	0.894
8	Calvin Murphy	PG	HOU	1971 - 1983	1002	30.5	17.9	3445	3864	0.892
9	Scott Skiles	PG	ORL	1987 - 1996	600	28.0	11.1	1548	1741	0.889
10	Reggie Miller	SG	IND	1988 - 2005	1389	34.3	18.2	6237	7026	0.888

The rank table shows there are two players sharing the first spot. But actually, if the number not rounded, Steve Nash has 0.9042553 FT, while Mark Price has 0.903895 FT. So Steve Nash won the top spot!

LabelFix <- AllTimeFT. %>%
    head(10) %>%
    mutate(Player = fct_reorder(Player, desc(FT.), fun=mean),
           FTa = FTa-FTm,
           FT. = paste(FT. * 100, "%")) %>%
    gather(Parameter, Count, FTm:FTa, -c(Player, FT.)) %>%
    filter(Parameter == "FTa")
AllTimeFT. %>%
    head(10) %>%
    mutate(Player = fct_reorder(Player, desc(FT.), fun=mean),
           FTa = FTa-FTm) %>%
    gather(Parameter, Count, FTm:FTa, -c(Player, FT.)) %>%
    ggplot(aes(Player, Count, fill = Parameter)) + 
    geom_bar(stat = 'identity', position = 'stack') +
    geom_label(aes(label = FT.,
                   y = 7500),
               data = LabelFix,
               stat = 'identity',
               position = "stack",
               fill = "#FF4000",
               color = "#00FFFF",
               size = 4.5) +
    ggtitle("All-Time Free Throws Percentage Leaders") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    xlab("") +
    scale_fill_manual(labels=c("FT-missed  ", "FT-made"), values=c("#FF4C4C","#FF0000")) +
    theme(legend.title=element_blank()) +
    theme(legend.position="bottom")

And here they are…

TopFT.Pos <- AllTimeFT. %>%
    group_by(Pos) %>%
    summarise(Player = Player[which.max(FT.)],
              Team = Team[which.max(FT.)],
              ActiveYears = ActiveYears[which.max(FT.)],
              Games = Games[which.max(FT.)],
              MPG = MPG[which.max(FT.)],
              PPG = PPG[which.max(FT.)],
              FTm = FTm[which.max(FT.)],
              FTa = FTa[which.max(FT.)],
              FT. = FT.[which.max(FT.)])

Best FT% by Position

C

Jack Sikma

0.849

• FG: 4292 / 5053
• PPG: 15.6
• Years: 1978 - 1991

PF

Dirk Nowitzki

0.879

• FG: 7104 / 8081
• PPG: 21.7
• Years: 1999 - 2018

SF

Rick Barry

0.9

• FG: 3818 / 4243
• PPG: 23.2
• Years: 1966 - 1980

SG

Ray Allen

0.894

• FG: 4398 / 4920
• PPG: 18.9
• Years: 1997 - 2014

PG

Steve Nash

0.904

• FG: 3060 / 3384
• PPG: 14.3
• Years: 0.904

Free Throws per 36 Minutes Leaders

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus fermentum turpis et justo fermentum viverra. Aliquam ipsum mi, dignissim quis eros fermentum, posuere tincidunt nulla. Quisque sollicitudin, massa quis lacinia accumsan, quam urna finibus augue, in blandit purus nisi id nisi. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras efficitur nisl a interdum accumsan. Suspendisse et maximus metus, vel accumsan arcu. Vivamus mollis quis eros ut molestie.

AllTimeFTma <- NBA %>%
    group_by(Player) %>%
    filter(sum(G) >= 400) %>%
    summarise(Pos = getmode(Pos),
              Team = getmode(Tm),
              ActiveYears = paste(getmode(YearStart), "-", getmode(YearEnd)),
              Minutes = sum(MP),
              Games = sum(G),
              MPG = round(Minutes /sum(G), 1),
              PPG = round(sum(PTS)/sum(G), 1),
              FTm_36 = round((sum(FT) / Minutes) * 36, 1),
              FTm_G = round(sum(FT) / Games, 1),
              FTa_36 = round((sum(FTA) / Minutes) * 36, 1),
              FTa_G = round(sum(FTA) / Games, 1),
              FT. = round(sum(FT)/sum(FTA), 3)) %>%
    select(-"Minutes")
AllTimeFTma %>%
    arrange(desc(FTm_36), desc(FT.)) %>%
    head(n=10) %>%
    mutate(Rank = min_rank(desc(FTm_36))) %>%
    select(Rank, everything()) %>%
    mutate(Pos = cell_spec(Pos, color = "white", align = "c", 
                    background = factor(Pos, c("C", "PF", "SF", "SG", "PG"), 
                                        PosColorCode))) %>%
    kable(escape = FALSE, caption = "Free Throw Made per 36 Minutes") %>%
    kable_styling("striped", full_width = T, font_size = 11) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(c(2, 13), bold = T) %>%
    column_spec(9, bold = T, color = "white", background = "#777777") %>%
    column_spec(13, bold = T) %>%
    scroll_box(width = "100%", height = "300px")

Free Throw Made per 36 Minutes

Rank	Player	Pos	Team	ActiveYears	Games	MPG	PPG	FTm_36	FTm_G	FTa_36	FTa_G	FT.
1	James Harden	SG	HOU	2010 - 2018	615	33.6	22.1	7.4	6.9	8.6	8.1	0.854
2	Bob Pettit	PF	STL	1955 - 1965	792	38.8	26.4	7.3	7.8	9.5	10.3	0.761
3	Adrian Dantley	SF	UTA	1977 - 1991	955	35.8	24.3	7.2	7.2	8.8	8.7	0.818
4	Corey Maggette	SF	LAC	2000 - 2013	827	28.2	16.0	7.1	5.6	8.6	6.8	0.822
4	John Drew	SF	ATL	1975 - 1985	739	29.5	20.7	7.1	5.8	9.5	7.8	0.748
6	Jerry West	PG	LAL	1961 - 1974	932	39.2	27.0	7.0	7.7	8.7	9.4	0.814
7	Kevin Durant	SF	OKC	2008 - 2018	703	37.4	27.2	6.8	7.0	7.7	8.0	0.882
7	Moses Malone	C	HOU	1975 - 1995	1329	33.9	20.6	6.8	6.4	8.9	8.3	0.769
9	Neil Johnston	C	PHW	1952 - 1959	516	35.5	19.4	6.7	6.6	8.7	8.6	0.768
10	Michael Jordan	SG	CHI	1985 - 2003	1072	38.3	30.1	6.4	6.8	7.7	8.2	0.835

AllTimeFTma %>%
    arrange(desc(FTa_36), desc(FT.)) %>%
    head(n=10) %>%
    mutate(Rank = min_rank(desc(FTa_36))) %>%
    select(Rank, everything()) %>%
    mutate(Pos = cell_spec(Pos, color = "white", align = "c", 
                    background = factor(Pos, c("C", "PF", "SF", "SG", "PG"), 
                                        PosColorCode))) %>%
    kable(escape = FALSE, caption = "Free Throw Attempts per 36 Minutes") %>%
    kable_styling("striped", full_width = T, font_size = 11) %>%
    column_spec(1, bold = T, color = "yellow", background = "#FF0000") %>%
    column_spec(c(2, 13), bold = T) %>%
    column_spec(11, bold = T, color = "white", background = "#777777") %>%
    column_spec(13, bold = T) %>%
    scroll_box(width = "100%", height = "300px")

Free Throw Attempts per 36 Minutes

Rank	Player	Pos	Team	ActiveYears	Games	MPG	PPG	FTm_36	FTm_G	FTa_36	FTa_G	FT.
1	Shaquille O'Neal	C	LAL	1993 - 2011	1207	34.7	23.7	5.1	4.9	9.7	9.3	0.527
2	Bob Pettit	PF	STL	1955 - 1965	792	38.8	26.4	7.3	7.8	9.5	10.3	0.761
2	John Drew	SF	ATL	1975 - 1985	739	29.5	20.7	7.1	5.8	9.5	7.8	0.748
4	Moses Malone	C	HOU	1975 - 1995	1329	33.9	20.6	6.8	6.4	8.9	8.3	0.769
4	Dwight Howard	C	ORL	2005 - 2018	954	34.9	17.5	5.1	4.9	8.9	8.7	0.566
4	Wilt Chamberlain	C	LAL	1960 - 1973	1045	45.8	30.1	4.6	5.8	8.9	11.4	0.511
7	Adrian Dantley	SF	UTA	1977 - 1991	955	35.8	24.3	7.2	7.2	8.8	8.7	0.818
8	Jerry West	PG	LAL	1961 - 1974	932	39.2	27.0	7.0	7.7	8.7	9.4	0.814
8	Neil Johnston	C	PHW	1952 - 1959	516	35.5	19.4	6.7	6.6	8.7	8.6	0.768
8	Karl Malone	PF	UTA	1986 - 2004	1476	37.2	25.0	6.4	6.6	8.7	8.9	0.742

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AllTimeFTma %>%
    arrange(desc(FTm_36), desc(FT.)) %>%
    mutate(Player = reorder(Player, desc(FTm_36), FUN=mean)) %>%
    head(n=10) %>%
    ggplot(aes(group = 1)) +
    geom_segment(aes(x=Player, xend=Player, y=FTm_36, yend=FTa_36), color="black") +
    geom_point(aes(Player, FTm_36, color="#FF5800"), size=5) +
    geom_point(aes(Player, FTa_36, color="#FF5800"), size=2.5) +
    geom_point(aes(Player, FTm_G, color="#009dff"), size=5, shape=18) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ggtitle("Free Throw Made per 36 Minutes vs. Free Throw Made per Game") +
    xlab("") +
    ylab("FT Made") +
    scale_color_manual(name="", values=c("#009dff", "#FF5800"),
                       labels=c("FT a/m per Game", "FT a/m per 36 minutes"),
                       guide = guide_legend(reverse=TRUE)) +
    theme(legend.position='none')

Shorter line means better percentage, and vice versa.

BIG Free Thrower Names

The Free Throwers will be sorted according to the following arbritary criteria :

• Annual league leader in Free Throw percentage category earn 1 point.
• Top 10 all-time leaders in Free Throw percentage category earn 2 points.
• Top 10 all-time leaders in Free Throw made category with FT% more than 85% earn 1 point.
• Top 10 all-time leaders in Free Throw made per 36 minutes category with FT% more than 85% earn 1 point.

Calculate the points and we have the rank!

Generating plot…

AllTimeFTm <- NBA %>%
    group_by(Player) %>%
    summarise(FTm = sum(X3P),
              FTa = sum(X3PA),
              FT. = FTm / FTa) %>%
    filter(FT. > 0.85) %>%
    arrange(desc(FTm)) %>% head(10)
AllTimeFTm36 <- AllTimeFTma %>%
    filter(FT. >= 0.85) %>%
    arrange(desc(FTm_36), desc(FT.)) %>%
    head(n=10)
SuperFreeThrowers <- data.frame(Player = c(as.character(TopFT.$Player),
                               as.character(rep(head(AllTimeFT.$Player, 10), 2)),
                               as.character(AllTimeFTm$Player),
                               as.character(AllTimeFTm36$Player))) %>%
    count(Player) %>%
    arrange(desc(n))
# Display the final result:
SuperFreeThrowers %>%
    group_by(Player) %>%
    mutate(Pos = getmode(NBA$Position[NBA$Player %in% Player]),
           FTm = sum(NBA$FT[NBA$Player %in% Player]),
           FTa = sum(NBA$FTA[NBA$Player %in% Player]),
           "FT%" = round(FTm/FTa, 3),
           Tokens = paste(strrep("|", n)),
           TotalTokens = n) %>%
    select(-"n") %>%
    kable(escape = FALSE, caption = "The Most Prominent Free-Throwers") %>%
    kable_styling(bootstrap_options = "striped", full_width = F, position = "center") %>%
    column_spec(1, bold = T) %>%
    column_spec(6, bold = T, color = "gold") %>%
    column_spec(7, bold = T, color = "white", background = "#777777") %>%
    scroll_box(width = "100%", height = "300px")

The Most Prominent Free-Throwers

Player	Pos	FTm	FTa	FT%	Tokens	TotalTokens
Rick Barry	SF	3818	4243	0.900	||||||||	8
Bill Sharman	SG	3143	3559	0.883	|||||||	7
Reggie Miller	SG	6237	7026	0.888	|||||||	7
Mark Price	PG	2135	2362	0.904	|||||	5
Stephen Curry	PG	1993	2212	0.901	|||||	5
Calvin Murphy	PG	3445	3864	0.892	||||	4
Peja Stojakovic	SF	2237	2500	0.895	||||	4
Steve Nash	PG	3060	3384	0.904	||||	4
Chauncey Billups	PG	4496	5029	0.894	|||	3
Dolph Schayes	PF	6712	7904	0.849	|||	3
Larry Bird	SF	3960	4471	0.886	|||	3
Chris Mullin	SF	3616	4178	0.865	||	2
Ernie DiGregorio	PG	461	511	0.902	||	2
Kevin Durant	SF	4935	5596	0.882	||	2
Kyle Macy	PG	717	821	0.873	||	2
Larry Costello	PG	2432	2891	0.841	||	2
Larry Siegfried	PG	1662	1945	0.854	||	2
Mahmoud Abdul-Rauf	PG	1051	1161	0.905	||	2
Oscar Robertson	PG	7694	9185	0.838	||	2
Ray Allen	SG	4398	4920	0.894	||	2
Scott Skiles	PG	1548	1741	0.889	||	2
Adrian Smith	SG	2308	2762	0.836	|	1
Allan Houston	SG	2572	2979	0.863	|	1
Alvin Sims	SG	2	5	0.400	|	1
Bobby Wanzer	SG	2179	2722	0.801	|	1
Brian Roberts	PG	336	370	0.908	|	1
C.J. McCollum	SG	529	627	0.844	|	1
Chet Walker	SF	5079	6384	0.796	|	1
Coty Clarke	SF	0	0	NaN	|	1
Danilo Gallinari	SF	1977	2292	0.863	|	1
Demetrius Jackson	PG	3	6	0.500	|	1
Dirk Nowitzki	PF	7104	8081	0.879	|	1
Eddy Curry	C	1662	2588	0.642	|	1
Eric Anderson	PF	16	27	0.593	|	1
Eric White	SF	79	99	0.798	|	1
Flynn Robinson	PG	1597	1881	0.849	|	1
Georgi Glouchkov	PF	70	122	0.574	|	1
Greg Deane	SG	5	7	0.714	|	1
Gundars Vetra	SF	4	6	0.667	|	1
Isaiah Thomas	PG	2119	2420	0.876	|	1
Ivano Newbill	PF	45	104	0.433	|	1
Jack Marin	SF	2405	2852	0.843	|	1
Jack Sikma	C	4292	5053	0.849	|	1
Jamal Crawford	SG	3499	4063	0.861	|	1
James Harden	SG	4226	4951	0.854	|	1
Jeff Hornacek	SG	2973	3390	0.877	|	1
Joe Fulks	PF	1117	1446	0.772	|	1
Jose Calderon	PG	892	1019	0.875	|	1
Kevin Martin	SG	3561	4091	0.870	|	1
Kiki Vandeweghe	SF	3484	3997	0.872	|	1
Kyle Korver	SG	1138	1293	0.880	|	1
Magic Johnson	PG	4960	5850	0.848	|	1
Max Zaslofsky	SG	1469	1945	0.755	|	1
Micheal Williams	PG	1545	1780	0.868	|	1
Ricky Pierce	SG	3389	3871	0.875	|	1
Spud Webb	PG	1946	2296	0.848	|	1

SuperFreeThrowers %>% wordcloud2(size=0.4, color='random-light', backgroundColor="black")

Free Throw Ratio & Rate

In this section, we will see the ratio and the rate of Free-Throw. The first one is similar to what I did previously, figuring out how many percent of the points earned from the Free Throw, and then sorted it by highest Free-Throw ratio.

NBA %>%
    filter(FT > 1) %>%
    group_by(Player) %>%
    summarise(Pos = getmode(Position),
              Team = getmode(Tm),
              Games = sum(G),
              Pts2 = sum(X2P) * 2,
              Pts3 = sum(X3P, na.rm = T) * 3,
              PtsFT = sum(FT),
              PTS = sum(PTS),
              PPG = round(PTS / Games, 1),
              Pts2r = round(Pts2 / PTS, 3),
              Pts3r = round(Pts3 / PTS, 3),
              PtsFTr = round(PtsFT / PTS, 3),
              FTm = sum(FT),
              FTa = sum(FTA),
              FT. = round(FTm / FTa, 3)) %>%
    filter(FTm > 1200) %>%
    select(-c(FTm, FTa, Pts2:PtsFT)) %>%
    arrange(desc(PtsFTr)) %>%
    mutate(Player = reorder(Player, PtsFTr, FUN=mean)) %>%
    head(10) %>%
    gather(Parameter, Value, Pts2r:PtsFTr) %>%
    ggplot(aes(Player, Value, fill=forcats::fct_rev(Parameter))) +
    geom_bar( stat="identity", position="fill") +
    geom_label(aes(label = paste(Value * 100, "%")),
               stat = 'identity',
               position = position_stack(vjust = 0.5),
               fill = "#FF4000",
               color = "#00FFFF",
               size = 2.5) +
    ggtitle("Highest Free Throw Ratio") +
    coord_flip() +
    xlab("") +
    ylab("Shooting") +
    guides(fill = guide_legend(reverse = TRUE)) +
    theme(legend.position="bottom") +
    theme(legend.title=element_blank())

While in the 3-Points Ratio we will see the highest percentage points came from 3-Points is 79.3%, for Free Throw the highest is 39%, or about half. This is because in Free Throw each FT made a player only earn 1 point, contrast that to 3-points.

That’s why it’s a good idea to look at it from different angle, using data provided by this dataset, we are going to see the rate of free throws attempted per field goal attempted (FTr). And then, again, we sorted it by the FT rate.

In FT ratio above, we get the numbers from FT made and compared it with 2-points FG made times 2 and 3-points FG made times 3. In Free Throw rate, we take the number of Free Throw attempts in comparison with the number of Field Goal attempts (which is includes 3-Points FG), no multiplication on the number, each shooting attempt treated equal.

FTrateAVG <- NBA %>%
    summarise(Player = "Average",
              Games = sum(G, na.rm = T),
              FTa = sum(FTA, na.rm = T),
              FGa = sum(FGA, na.rm = T),
              FTapG = round(FTa / Games, 2),
              FGapG = round(FGa / Games, 2),
              FTr = round(FTa / FGa, 3)) %>%
    select(-c(Games, FGa, FTa))
FTrate <- NBA %>%
    group_by(Player) %>%
    summarise(Pos = getmode(Pos),
              Team = getmode(Tm),
              Games = sum(G),
              PTS = sum(PTS),
              PPG = round(PTS / Games, 1),
              FGa = sum(FGA),
              FTa = sum(FTA),
              FTr = round(FTa / FGa, 3),
              FTapG = round(FTa / Games, 2),
              FGapG = round(FGa / Games, 2)) %>%
    filter(FTa > 1200) %>%
    mutate(Rank = min_rank(desc(FTr))) %>%
    select(Rank, everything()) %>%
    arrange(Rank) %>%
    head(10) %>%
    bind_rows(FTrateAVG)
LabelFix <- FTrate %>%
    mutate(FTapG = FTapG,
           FTr = paste(FTr * 100, "%")) %>%
    gather(Parameter, Count, FTapG, -c(Player, FTr)) %>%
    mutate(Player = factor(Player, levels = unique(Player[order(desc(FTr))])))
FTrate %>%
    gather(Parameter, Count, FGapG:FTapG, -c(Player, FTr)) %>%
    mutate(Player = factor(Player, levels = unique(Player[order(desc(FTr))]))) %>%
    ggplot(aes(Player, Count, fill = Parameter, label = FTr)) +
    geom_bar(position="dodge", stat="identity") +
    geom_label(aes(label = FTr, y = 0.7),
              data = LabelFix,
              stat = 'identity',
              position = "stack",
              fill = "#FFFFFF",
              color = "#000000",
              size = 4) +
    ggtitle("Free Throw Rate") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    xlab("") +
    ylab("FT/FG per game") +
    scale_fill_manual(name="", values=c("#0000FF", "#FF0000"),
                       labels=c("FG per Game", "FT per Game")) +
    theme(legend.position="bottom")

NA

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End of Session

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