Introduction

Column

Motivation

An Analysis of League Trends Within the NBA

In this presentation, we look at statistical analysis that attempts to display how the NBA has changed over the past 30+ years, but we will also utilize advanced statistics to find certain league trends and attempt to answer multi-era spanning questions. Before I was able to start my analysis, I needed to merge two NBA data sets: one including player salary information, and one including player career statistics and general attributes. My final merged data set contains information from 1984-2018. This time frame completely overlaps with the NBA’s greatest time of growth.

This presentation aims to answer some of the following questions:

  • How has the NBA grown its international presence?

  • What sort of factors affect a player’s career Win Shares?

  • What sort of league positional development has occurred since 1985?

Sources:

Glimpse of Merged Data Set

Rows: 2,408
Columns: 21
$ career_AST    <dbl> 3.6, 3.6, 7.2, 5.3, 10.5, 3.0, 2.4, 3.7, 2.3, 2.5, 4.2, …
$ career_FG.    <chr> "55.9", "51.6", "50.4", "49.7", "51.5", "50.6", "47.1", …
$ career_FG3.   <dbl> 5.6, 27.4, 34.3, 32.7, 38.4, 17.9, 38.0, 27.5, 15.0, 4.5…
$ career_FT.    <chr> "72.1", "74.2", "73.6", "83.5", "82.6", "69.6", "87.9", …
$ career_G      <dbl> 1560, 1476, 1198, 1072, 1504, 1392, 1522, 1462, 1329, 12…
$ career_PER    <dbl> 24.6, 23.9, 27.6, 27.9, 21.8, 24.2, 22.4, 22.7, 21.4, 26…
$ career_PTS    <dbl> 24.6, 25.0, 27.2, 30.1, 13.1, 19.0, 20.7, 17.8, 18.8, 23…
$ career_TRB    <chr> "11.2", "10.1", "7.4", "6.2", "2.7", "10.8", "7.5", "10.…
$ career_WS     <dbl> 273.4, 234.6, 226.6, 214.0, 207.7, 206.4, 206.3, 191.4, …
$ career_eFG.   <dbl> 55.9, 51.8, 54.1, 50.9, 54.6, 50.7, 51.2, 50.1, 58.2, 58…
$ college       <chr> "University of California, Los Angeles", "Louisiana Tech…
$ draft_pick    <dbl> 1, 13, 1, 3, 16, 1, 9, 5, 117, 1, 12, NA, 1, 5, 11, 13, …
$ draft_team    <chr> "Milwaukee Bucks", "Utah Jazz", "Cleveland Cavaliers", "…
$ draft_year    <dbl> 1969, 1985, 2003, 1984, 1984, 1997, 1998, 1995, 1971, 19…
$ height        <chr> "7-2", "6-9", "6-8", "6-6", "6-1", "6-11", "7-0", "6-11"…
$ name          <chr> "Kareem Abdul-Jabbar", "Karl Malone", "LeBron James", "M…
$ position      <chr> "C", "F", "G/F", "G/F", "G", "F/C", "F/C", "F/C", "C", "…
$ median_salary <dbl> 2015000, 3229900, 15779912, 2500000, 2600000, 10865250, …
$ first_season  <int> 1984, 1985, 2003, 1984, 1985, 1997, 1998, 1995, 1984, 19…
$ last_season   <int> 1989, 2004, 2018, 2003, 2003, 2018, 2018, 2017, 1988, 20…
$ birthcountry  <chr> "United States", "United States", "United States", "Unit…

Column

Variable Explanation

Basic Statistics

  • career_AST: Average number of assists earned per game for a player’s career
  • career_FG.: ((Total field goals made) / (Total field goals attempted)) * 100 from a player’s entire career.
  • career_FG3.: ((Total 3-Point field goals made) / (Total 3-Point field goals attempted)) * 100 from a player’s entire career.
  • career_FT.: ((Total free throws made) / (Total free throws attempted)) * 100 from a player’s entire career.
  • career_G: Total career games played
  • career_PTS: Average number of points scored per game for a player’s career
  • career_TRB: Average number of both offensive and defensive rebounds grabbed per game for a player’s career
  • median_salary: Median annual salary earned for a player’s career

Advanced Statistics

  • career_PER: Career player efficiency rating; PER is a player’s per-minute productivity that allows us to compare players with differing minutes played per game
  • career_WS: Career Win Shares Earned; Win Shares is a player statistic which attempts to divvy up credit for team success to the individual players on the team
  • career_eFG.: Career effective field goal percentage; eFG% = (FGM + 0.5 * 3PM) / FGA
  • WS_per_G: Career Win Shares per Game; this statistic accounts for disparities among NBA players’ career lengths by dividing career WS by career games played, thus finding their approximate win shares earned per game.

1985 v. 2018

Column

The following interactive tables show all active players from both the 1985 and 2018 NBA seasons.

1985

2018

NBA Diversity Maps

Column

Over the past 30 or so years, the NBA has seen a great international diversification, with the past four regular-season MVP awards going to international-born players. These world maps display how international representation within the NBA has grown.

1985

2018

How is WS Effected

Column

Explanation

In the NBA, the advanced statistic of Win-share (WS) is one of, if not the best way, of measuring the success of a player’s career.

Through an advanced formula, WS measures a player’s offense, defense, and playing time in order to assign the proportion of a team’s win a player has earned.

The following scatter plots display the possible relationships between WS and both draft pick as well as college drafted from. Only players drafted 1st through 60th were considered for both plots, and only the top colleges that have produced the most NBA talent were considered for the second plot.

WS vs. Draft Pick

WS vs. College

Column

Findings

When looking at the draft pick scatterplot, it is clear that there exists a somewhat strong non-linear negative relationship between draft pick and career WS, with numerically-lower picks typically earning more WS throughout their career. This was expected given lower draft picks are usually viewed as greater talent than higher picks.

The next scatter plot contains the top 10 colleges that have produced the most NBA talent during the span of 1984-2018. Although other colleges have produced more NBA players, it is likely that the University of North Carolina produces the highest caliber NBA players since their alumns have the highest median career WS and are more often than not, lottery picks (1-14 in the draft).

Position Development

Column

Explanation

One altered element of how basketball is played today has been the development of the 3-point shot. Although uncommon 30 years ago, the 3-point shot has become a vital part of how today’s basketball is played at both the professional and collegiate level. The first scatter plot measures the NBA’s progression in shooting efficiency using the advanced statistic eFG%, which takes into account 3-point shots as a higher valued shot, unlike FG%. On this note, centers, the big men of basketball, have began shooting 3-point shots at a higher rate than ever before, but are they improving at an efficient rate? The second scatter plot displays the development of 3-point shooting efficiency among NBA centers using 3P%.

While today’s NBA is often described as “positionless”, the NBA in 1985 could be described more so as traditional. The game was often thought of as being big-man dominated, with teams commonly running their offense through their centers. This offensive style is relatively uncommon in today’s game, but does the current salary distribution among positions agree with this claim? The third and fourth box plots display the salary distribution among pure guards, forwards, and centers (the three main positions of basketball) from both the 1985 and 2018 NBA seasons.

eFG% Development

C 3P% Development

1985 Salaries

2018 Salaries

Column

Findings

Scatter Plots

As seen in the scatter plots the left, there exists a positive but moderate relationship between draft class and shooting efficiency. Thus, shooting efficiency has increased among centers and league-wide over the past 30+ years, but the correlation is not as strong as some believe.

Box Plots

In regards to the salary distribution box plots, there is sufficient enough evidence to suggest that that salary trends in relation to position have changed since 1985; this of course is seen by centers typically being paid more than forwards and guards in 1985, while median salaries by position are quite similar in 2018.

Efficiency Analysis

Fans of the NBA often like to compare different eras of the league. Two of the debates that arise out of this are of both the greatest draft class and the greatest player of all time. One way of measuring this is through efficiency analysis, using Win Shares and PER (Player Efficiency Rating). Note: I used WS/G (Win Shares per Game) to account for disparities in certain players’ career lengths.

Column

Draft Classes

Players

About the Author

Column

My Background

My name is Jesse Devitt and I am an undergraduate student at the University of Dayton. Currently, my projected graduation is May 2024.

Right now, I am working towards completing a B.S. in Applied Mathematical Economics with minors in Data Analytics and AI & Data Science

I am interested in pursuing full time employment in the corporate data analytics field after my graduation.

Last summer, I worked as the general manager of water sports rental operation on the south side of Chicago, where I vastly improved my delegation, leadership, and client service skills. I hope to earn an internship position in a data-related field in Chicago for this summer. I am proficient in R, Java, and Excel, and will be taking a data structures course in Python next semester.

Feel free to connect with me on LinkedIn here.

Project Limitations

Although I was pleased with the addition of advanced statistic variables such as WS and PER, I would have preferred more be included. Variables such as USG% as well as other advanced basketball statistics that dive into the efficiency of individual players would have been useful in my analysis.

The addition of individual player accolades and achievements also would have been useful to my analysis. Variables such as championships, MVP awards, DPOY awards, and number of All-NBA team selections would have allowed me find possible correlations between basic/advanced statistics and career achievements.

In the future, I hope to do a similar analytical project on the NBA, but instead of utilizing individual player statistics, I would be analyzing team statistics over a large time frame. This would allow me to dive deeper into how offensive and defensive trends have changed in the league through time.

Column

Picture

Jesse Devitt

Jesse Devitt 

---
title: "NBA Analysis"
author: "Jesse Devitt"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: cosmo
      primary: "#30D5C8"
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

<style>
.chart-title {  /* chart_title  */
   font-size: 20px;
  }
body{  /* Normal  */
      font-size: 18px;
  }
</style>
  

```{r setup, include=FALSE}
library(flexdashboard)
```

Introduction
===


Column {.tabset data-width=650}
---

### Motivation

<head>
    <base target = "_blank">
</head>

<font size=5>
**An Analysis of League Trends Within the NBA**
</font>

In this presentation, we look at statistical analysis that attempts to display how the NBA has changed over the past 30+ years, but we will also utilize advanced statistics to find certain league trends and attempt to answer multi-era spanning questions. Before I was able to start my analysis, I needed to merge two NBA data sets: one including player salary information, and one including player career statistics and general attributes. My final merged data set contains information from 1984-2018. This time frame completely overlaps with the NBA's greatest time of growth.

This presentation aims to answer some of the following questions:

- How has the NBA grown its international presence?

- What sort of factors affect a player's career Win Shares?

- What sort of league positional development has occurred since 1985?

Sources:

- [Data World](https://data.world/)

- [Basketball Reference](https://www.basketball-reference.com/)



### Glimpse of Merged Data Set


```{r}
knitr::opts_chunk$set(echo = TRUE)
library(pacman)
library(tidyverse)
library(plotly)
library(maps)
library(DT)
# **bold** to make bold
# - to make bullet points
```


```{r package_data, echo=FALSE}
NBA_salaries_1985to2018 <- read.csv("~/Library/Mobile Documents/com~apple~CloudDocs/MTH 209/salaries_1985to2018.csv")
NBA_salaries_1985to2018 <- NBA_salaries_1985to2018 %>% select(-(league))
NBA_salaries <- NBA_salaries_1985to2018 %>% group_by(player_id,) %>%
  summarize(median_salary = median(salary),
            first_season = min(season_start), 
            last_season = max(season_end))
NBAplayers <- read.csv("~/Library/Mobile Documents/com~apple~CloudDocs/MTH 209/NBAplayers.csv")
NBAplayers <- NBAplayers %>% rename(player_id = X_id)
NBA <- NBAplayers %>% inner_join(NBA_salaries)
temp<- data.frame(do.call("rbind", strsplit(as.character(NBA$birthPlace), ", ", fixed = TRUE)))
NBA$birthcity <- temp[,1]
NBA$birthcountry <- temp[,2]
index <- which(NBA$birthcity %in% c("Tbilisi", "Rustavi")&NBA$birthcountry=="Georgia")
NBA$birthcountry<-ifelse(NBA$birthcountry %in% state.name, "United States", NBA$birthcountry)
NBA$birthcountry<-ifelse(NBA$birthcountry=="District of Columbia", "United States", NBA$birthcountry)
NBA$birthcountry[index]<-"Georgia"
NBA$birthcountry <- as.character(NBA$birthcountry)
NBA$draft_pick <- gsub('[ overallstndrdth]', '', NBA$draft_pick[1:2408])
NBA$draft_pick <- as.numeric(NBA$draft_pick)
NBA$career_WS <- as.numeric(NBA$career_WS)
NBA$career_G <- as.numeric(NBA$career_G)
NBA$draft_year <- as.numeric(NBA$draft_year)
NBA$career_PER <- as.numeric(NBA$career_PER)
NBA$career_FG3. <- as.numeric(NBA$career_FG3.)
NBA$median_salary <- as.numeric(NBA$median_salary)
NBA$career_eFG. <- as.numeric(NBA$career_eFG.)
options(scipen=999)
NBA$position <- NBA$position %>% recode(
  `Power Forward` = "F",                                              
  `Center` = "C",                                                         
 `Point Guard` = "G",                                                        
 `Shooting Guard` = "G",                                                     
 `Center and Small Forward and Power Forward` =  "F/C",                       
 `Power Forward and Small Forward` = "F",                                   
 `Center and Power Forward` = "F/C",                                    
 `Small Forward` = "F",                                                   
 `Shooting Guard and Small Forward` = "G/F",                                  
  `Shooting Guard and Point Guard` = "G",                                   
  `Power Forward and Center` = "F/C",                                         
  `Point Guard and Shooting Guard` = "G",                                
`Shooting Guard and Small Forward and Point Guard` = "G/F",                  
`Small Forward and Shooting Guard` = "G/F",                              
`Point Guard and Power Forward and Small Forward and Shooting Guard` = "G/F",
`Small Forward and Power Forward` = "F",                                   
`Small Forward and Power Forward and Shooting Guard` = "G/F",                
`Power Forward and Center and Small Forward` = "F/C",                        
`Small Forward and Center and Power Forward` = "F/C",                        
`Shooting Guard and Small Forward and Power Forward` = "G/F",                
`Shooting Guard and Point Guard and Small Forward` = "G/F",                  
`Center and Power Forward and Small Forward` = "F/C",                        
`Power Forward and Shooting Guard` = "G/F",                                  
`Small Forward and Shooting Guard and Power Forward` = "G/F",                
`Power Forward and Small Forward and Shooting Guard` = "G/F",                
`Small Forward and Shooting Guard and Point Guard` = "G/F",                  
`Small Forward and Power Forward and Center` = "F/C",                        
`Small Forward and Point Guard and Shooting Guard` = "G/F",                  
`Small Forward and Center` = "F/C",                                          
`Shooting Guard and Power Forward and Point Guard` = "G/F",                  
`Power Forward and Shooting Guard and Small Forward` = "G/F",                
`Shooting Guard and Power Forward and Small Forward` = "G/F",                 
`Point Guard and Shooting Guard and Small Forward` = "G/F",                  
`Power Forward and Small Forward and Center` = "F/C",                        
`Point Guard and Small Forward and Shooting Guard` = "G/F",                  
`Point Guard and Small Forward` = "G/F",)
glimpse(NBA %>% arrange(desc(career_WS)) %>% select(-c(highSchool, player_id, birthcity, shoots, birthPlace, birthDate, draft_round, weight)))
```

Column {data-width=350}
---

### Variable Explanation
**Basic Statistics**

- **career_AST**: Average number of assists earned per game for a player's career
- **career_FG.**: ((Total field goals made) / (Total field goals attempted)) * 100 from a player's entire career.
- **career_FG3.**: ((Total 3-Point field goals made) / (Total 3-Point field goals attempted)) * 100 from a player's entire career.
- **career_FT.**: ((Total free throws made) / (Total free throws attempted)) * 100 from a player's entire career.
- **career_G**: Total career games played
- **career_PTS**: Average number of points scored per game for a player's career
- **career_TRB**: Average number of both offensive and defensive rebounds grabbed per game for a player's career
- **median_salary**: Median annual salary earned for a player's career


**Advanced Statistics**

- **career_PER**: Career player efficiency rating; [PER](https://www.espn.com/nba/columns/story?columnist=hollinger_john&id=2850240) is a player's per-minute productivity that allows us to compare players with differing minutes played per game
- **career_WS**: Career Win Shares Earned; [Win Shares](https://www.basketball-reference.com/about/ws.html) is a player statistic which attempts to divvy up credit for team success to the individual players on the team
- **career_eFG.**: Career effective field goal percentage; [eFG%](https://www.washingtonpost.com/what-is-effective-field-goal-percentage/a7e174de-5c62-4c9d-a687-01457731c1c2_note.html) = (FGM + 0.5 * 3PM) / FGA
- **WS_per_G**: Career Win Shares per Game; this statistic accounts for disparities among NBA players' career lengths by dividing career WS by career games played, thus finding their approximate win shares earned per game.


1985 v. 2018
===

Column {.tabset data-width=550}
---

The following interactive tables show all active players from both the 1985 and 2018 NBA seasons.

### 1985

```{r comparison, echo=FALSE}
# I want to arrange tables by descending PER or WS so table includes more recognizable player names
NBA1985 <- NBA %>% filter(first_season == 1984) %>% arrange(desc(career_PER, na.rm = T))
NBA2018 <- NBA %>% filter(last_season == 2018) %>% arrange(desc(career_PER))
datatable(NBA1985, options = list(
  columnDefs = list(list(className = 'dt-center', targets = 5)),
  pageLength = 5,
  lengthMenu = c(5, 10, 15, 20)
))
```




### 2018


```{r compariso, echo=FALSE}
datatable(NBA2018, options = list(
  columnDefs = list(list(className = 'dt-center', targets = 5)),
  pageLength = 5,
  lengthMenu = c(5, 10, 15, 20)
))
```





NBA Diversity Maps
===

Column {.tabset data-width=1000}
---

Over the past 30 or so years, the NBA has seen a great international diversification, with the past four regular-season MVP awards going to international-born players. These world maps display how international representation within the NBA has grown.

### 1985

```{r map1, echo=FALSE}
map <- map_data("world")
NBA1985_map <- NBA1985 %>% left_join(map, by = c("birthcountry"="region"))
count1985 <- NBA1985 %>% group_by(birthcountry) %>% summarize(count = n())
map1985 <- count1985 %>% left_join(map, by = c("birthcountry" = "region")) %>% filter(birthcountry != "United States")
p1 <- map %>% ggplot() +  
  geom_polygon(aes(x=long, y = lat, group = group, text = region), fill="grey", alpha=0.5) +
  geom_polygon(data = map1985, aes(x = long, y = lat, group = group, fill = count,  text = paste0(birthcountry, ":\n", round(count, 1), " NBA Player"))) +
  scale_fill_viridis_c(option = "D") +
  theme_void() + labs(title = "NBA International Diversity in 1985") +
  theme(legend.position="none")
ggplotly(p1, tooltip = "text")
```

### 2018

```{r map2, echo=FALSE}
count2018 <- NBA2018 %>% group_by(birthcountry) %>% summarize(count = n())
map2018 <- count2018 %>% left_join(map, by = c("birthcountry" = "region")) %>% filter(birthcountry != "United States")
p2 <- map %>% ggplot() +  
  geom_polygon(aes(x=long, y = lat, group = group, text = region), fill="grey", alpha=0.5) +
  geom_polygon(data = map2018, aes(x = long, y = lat, group = group, fill = count,  text = paste0(birthcountry, ":\n", round(count, 1), " NBA Player(s)"))) +
  scale_fill_viridis_c(option = "D") +
  theme_void() + labs(title = "NBA International Diversity in 2018")
ggplotly(p2, tooltip = "text")
```



How is WS Effected
===



Column {.tabset data-width=500}
---

### Explanation

In the NBA, the advanced statistic of Win-share (WS) is one of, if not the best way, of measuring the success of a player's career. 

Through an advanced formula, WS measures a player's offense, defense, and playing time in order to assign the proportion of a team's win a player has earned.

The following scatter plots display the possible relationships between WS and both draft pick as well as college drafted from. Only players drafted 1st through 60th were considered for both plots, and only the top colleges that have produced the most NBA talent were considered for the second plot. 

### WS vs. Draft Pick

```{r scatter 1, fig.align='center', out.width="85%", echo=FALSE}
NBA_WS <- NBA %>% 
  group_by(draft_pick, na.rm = T) %>% 
  filter(draft_year > 1983, draft_pick < 61) %>% 
  summarize(median_WS = median(career_WS, na.rm = T))
s1 <- ggplot(NBA_WS, aes(x = draft_pick, y = median_WS)) + geom_point(color = "red", na.rm = T) +
  theme(text = element_text(size = 12)) +
  labs(title = "Distribution Between Draft Pick and Career Win Shares",
       y = "Median WS",x = "Draft Pick (1st and 2nd Round Only)") +
  scale_color_brewer(palette = "Set2")
ggplotly(s1)
```

### WS vs. College

```{r scatter2, fig.align='center', out.width="85%", echo=FALSE}
college_top10 <- NBA %>% group_by(college) %>% filter(draft_year > 1983, draft_pick < 61) %>% summarize(count = n(), na.rm = T, median_WS = median(career_WS, na.rm = T), median_draftpick = median(draft_pick, na.rm = T)) %>% arrange(desc(count)) %>% head(11) 
college_top10 <- college_top10[-1,]
college_top10$count <- as.numeric(college_top10$count)
s2 <- ggplot(college_top10, aes(x = median_WS, y = median_draftpick, color = college,
                                size = count, text = paste0(college, ":\n", round(count, 1), " NBA Players Produced"))) + geom_point() +
  theme(text = element_text(size = 12)) +
  labs(title = "Top 10 NBA Alumn Colleges: WS vs Draft Pick",
       y = "Median Draft Pick (84'-18')",x = "Median WS") +
  scale_color_brewer(palette = "Set3") + theme(legend.position="none")
  
ggplotly(s2, tooltip = "text")
```


Column {.tabset data-width=500}
---

### Findings

When looking at the draft pick scatterplot, it is clear that there exists a somewhat strong non-linear negative relationship between draft pick and career WS, with numerically-lower picks typically earning more WS throughout their career. This was expected given lower draft picks are usually viewed as greater talent than higher picks.

The next scatter plot contains the top 10 colleges that have produced the most NBA talent during the span of 1984-2018. Although other colleges have produced more NBA players, it is likely that the University of North Carolina produces the highest caliber NBA players since their alumns have the highest median career WS and are more often than not, lottery picks (1-14 in the draft).


Position Development
===


Column {.tabset data-width=500}
---

### Explanation

One altered element of how basketball is played today has been the development of the 3-point shot. Although uncommon 30 years ago, the 3-point shot has become a vital part of how today's basketball is played at both the professional and collegiate level. 
The first scatter plot measures the NBA's progression in shooting efficiency using the advanced statistic eFG%, which takes into account 3-point shots as a higher valued shot, unlike FG%.
On this note, centers, the big men of basketball, have began shooting 3-point shots at a higher rate than ever before, but are they improving at an efficient rate?
The second scatter plot displays the development of 3-point shooting efficiency among NBA centers using 3P%.



While today's NBA is often described as "positionless", the NBA in 1985 could be described more so as traditional. The game was often thought of as being big-man dominated, with teams commonly running their offense through their centers. 
This offensive style is relatively uncommon in today's game, but does the current salary distribution among positions agree with this claim?
The third and fourth box plots display the salary distribution among pure guards, forwards, and centers (the three main positions of basketball) from both the 1985 and 2018 NBA seasons.

### eFG% Development

```{r scatter3b, fig.align='center', out.width="85%", echo=FALSE}
NBA_eFG <- NBA %>% 
  group_by(draft_year) %>% 
  filter(draft_year > 1983, career_eFG. != 0) %>%
  summarize(median_eFG = median(career_eFG., na.rm = T))
s3b <- ggplot(NBA_eFG, aes(x = draft_year, y = median_eFG)) + geom_point(color = "red") + labs(y = "Median eFG% Among NBA Players", title = "NBA Draft Classes and Their Corresponding eFG%",x = "Draft Year")
ggplotly(s3b)
```

### C 3P% Development

```{r scatter3a, fig.align='center', out.width="85%", echo=FALSE}
NBA_3P <- NBA %>% 
  group_by(draft_year) %>% 
  filter(position == "C", draft_year > 1983, career_FG3. != 0) %>% 
  summarize(median_FG3 = median(career_FG3., na.rm = T))
s3a <- ggplot(NBA_3P, aes(x = draft_year, y = median_FG3)) + geom_point(color = "red") + labs(y = "Median 3P% Among NBA Centers", title = "NBA Center Draft Classes and Their Corresponding 3P%",x = "Draft Year")
ggplotly(s3a)
```

### 1985 Salaries

```{r histogram1, fig.align='center', out.width="85%", echo=FALSE}
h1<-ggplot(NBA1985 %>% filter(position == "G" | position == "F" | position == "C"), aes(x = position, y = median_salary, fill = position)) +
  geom_boxplot() + 
  labs(x = "NBA Position", y = "Career Median Salary ($)", title = "NBA Salary Distribution by Position in 1985") + scale_x_discrete(labels = c('Center', 'Forward', 'Guard')) + theme(legend.position="none")
ggplotly(h1)
```

### 2018 Salaries

```{r histogram2, fig.align='center', out.width="85%", echo=FALSE}
h2<-ggplot(NBA2018 %>% filter(position == "G" | position == "F" | position == "C"), aes(x = position, y = median_salary, fill = position)) +
  geom_boxplot() +
  labs(x = "NBA Position", y = "Career Median Salary ($)", title = "NBA Salary Distribution by Position in 2018") + scale_x_discrete(labels = c('Center', 'Forward', 'Guard')) + theme(legend.position="none")
ggplotly(h2)
```

Column {.tabset data-width=500}
---

### Findings

***Scatter Plots***

As seen in the scatter plots the left, there exists a positive but moderate relationship between draft class and shooting efficiency. Thus, shooting efficiency has increased among centers and league-wide over the past 30+ years, but the correlation is not as strong as some believe.


***Box Plots***

In regards to the salary distribution box plots, there is sufficient enough evidence to suggest that that salary trends in relation to position have changed since 1985; this of course is seen by centers typically being paid more than forwards and guards in 1985, while median salaries by position are quite similar in 2018.


Efficiency Analysis
===

Fans of the NBA often like to compare different eras of the league. Two of the debates that arise out of this are of both the greatest draft class and the greatest player of all time. One way of measuring this is through efficiency analysis, using Win Shares and PER (Player Efficiency Rating).
Note: I used WS/G (Win Shares per Game) to account for disparities in certain players' career lengths.


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---

### Draft Classes

```{r scatter4, fig.align='center', out.width="90%", echo=FALSE}
best_NBAclass <- NBA %>% group_by(draft_year) %>% filter(draft_year > 1983, draft_pick < 61) %>% summarize(max_PER = max(career_PER), max_WS = max(career_WS), median_PER = median(career_PER, na.rm = T), median_WS = median(career_WS, na.rm = T)) %>% arrange(desc(median_WS), desc(median_PER)) %>% head(10)
best_NBAclass$draft_year <- as.character(best_NBAclass$draft_year)
s4 <- ggplot(best_NBAclass, aes(x = median_PER, y = median_WS, label = draft_year)) + 
  theme(text = element_text(size = 13)) +
  labs(title = "Efficiency Distribution of Top 10 Draft Classes",
       y = "Median WS", x = "Median PER") +
  scale_color_brewer(palette = "Set3") + geom_text(check_overlap = T)
ggplotly(s4)
```

### Players

```{r scatter5, fig.align='center', out.width="90%", echo=FALSE}
NBA <- NBA %>% mutate(WS_per_G = career_WS / career_G)
best_NBAplayer <- NBA %>% group_by(name) %>% filter(draft_year > 1983, draft_pick < 61) %>% arrange(desc(WS_per_G), desc(career_PER)) %>% head(10)
s5 <- ggplot(best_NBAplayer, aes(x = career_PER, y = WS_per_G, label = name)) + 
  theme(text = element_text(size = 13)) +
  labs(title = "Efficiency Distribution of Top 10 WS/G Players",
       y = "WS / Games Played", x = "Career PER") +
  scale_color_brewer(palette = "Set3") + geom_text(check_overlap = T)
ggplotly(s5)
```

About the Author
===

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### My Background

My name is Jesse Devitt and I am an undergraduate student at the University
of Dayton. Currently, my projected graduation is May 2024.

Right now, I am working towards completing a B.S. in Applied Mathematical Economics 
with minors in Data Analytics and AI & Data Science

I am interested in pursuing full time employment in the corporate data analytics field after my graduation.

Last summer, I worked as the general manager of water sports rental operation on the south side of Chicago, where I vastly improved my delegation, leadership, and client service skills. I hope to earn an internship position in a data-related field in Chicago for this summer. I am proficient in R, Java, and Excel, and will be taking a data structures course in Python next semester. 

Feel free to connect with me on LinkedIn [here](https://www.linkedin.com/in/jesse-devitt-0a2305228/).


### Project Limitations

Although I was pleased with the addition of advanced statistic variables such as WS and PER, I would have preferred more be included. Variables such as USG% as well as other advanced basketball statistics that dive into the efficiency of individual players would have been useful in my analysis.

The addition of individual player accolades and achievements also would have been useful to my analysis. Variables such as championships, MVP awards, DPOY awards, and number of All-NBA team selections would have allowed me find possible correlations between basic/advanced statistics and career achievements.

In the future, I hope to do a similar analytical project on the NBA, but instead of utilizing individual player statistics, I would be analyzing team statistics over a large time frame. This would allow me to dive deeper into how offensive and defensive trends have changed in the league through time.

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### Picture

```{r , fig.width=6, echo=FALSE, fig.cap="Jesse Devitt", fig.align='right'}
knitr::include_graphics("IMG_1803.jpeg")
```