NBA Player Efficiency Analysis

Read in Data

nba_data <- read.csv("C:/Users/patty/Documents/INFOSCI 310/2022-2023_NBA_player_stats_formatted.csv")

Data Inspection

str(nba_data)

'data.frame':   679 obs. of  30 variables:
 $ Rk    : int  1 2 3 4 5 6 6 6 7 8 ...
 $ Player: chr  "Precious Achiuwa" "Steven Adams" "Bam Adebayo" "Ochai Agbaji" ...
 $ Pos   : chr  "C" "C" "C" "SG" ...
 $ Age   : int  23 29 25 22 22 24 24 24 27 24 ...
 $ Tm    : chr  "TOR" "MEM" "MIA" "UTA" ...
 $ G     : int  55 42 75 59 77 59 36 23 72 68 ...
 $ GS    : int  12 42 75 22 20 3 3 0 70 68 ...
 $ MP    : num  20.7 27 34.6 20.5 21.8 15 14.7 15.5 27.4 32.6 ...
 $ FG    : num  3.6 3.7 8 2.8 3.2 2.2 2.3 2.1 3.4 5.9 ...
 $ FGA   : num  7.3 6.3 14.9 6.5 6.8 5 4.7 5.4 7.7 9.2 ...
 $ FG.1  : num  0.485 0.597 0.54 0.427 0.47 0.444 0.488 0.384 0.44 0.644 ...
 $ X3P   : num  0.5 0 0 1.4 1.2 1 1 1.1 2 0 ...
 $ X3PA  : num  2 0 0.2 3.9 3.5 2.7 2.4 3.1 5.1 0.1 ...
 $ X3P.  : num  0.269 0 0.083 0.355 0.353 0.384 0.402 0.361 0.399 0.1 ...
 $ X2P   : num  3 3.7 8 1.4 2 1.2 1.3 1 1.4 5.9 ...
 $ X2PA  : num  5.4 6.2 14.7 2.7 3.4 2.3 2.3 2.3 2.7 9.1 ...
 $ X2P.  : num  0.564 0.599 0.545 0.532 0.591 0.515 0.578 0.415 0.518 0.653 ...
 $ eFG.  : num  0.521 0.597 0.541 0.532 0.56 0.547 0.591 0.488 0.571 0.645 ...
 $ FT    : num  1.6 1.1 4.3 0.9 1.4 0.7 0.8 0.6 1.6 2.4 ...
 $ FTA   : num  2.3 3.1 5.4 1.2 1.9 1 1.1 0.9 1.8 3.3 ...
 $ FT.   : num  0.702 0.364 0.806 0.812 0.75 0.667 0.692 0.619 0.905 0.733 ...
 $ ORB   : num  1.8 5.1 2.5 0.7 1.1 0.3 0.2 0.3 0.8 3.3 ...
 $ DRB   : num  4.1 6.5 6.7 1.3 3.7 1.5 1.4 1.5 2.4 6.5 ...
 $ TRB   : num  6 11.5 9.2 2.1 4.8 1.7 1.6 1.8 3.3 9.8 ...
 $ AST   : num  0.9 2.3 3.2 1.1 1.3 1.8 2.1 1.4 2.3 1.7 ...
 $ STL   : num  0.6 0.9 1.2 0.3 0.6 0.5 0.7 0.3 0.9 0.8 ...
 $ BLK   : num  0.5 1.1 0.8 0.3 0.6 0.4 0.4 0.3 0.2 1.2 ...
 $ TOV   : num  1.1 1.9 2.5 0.7 0.8 0.9 1.3 0.4 1 1.4 ...
 $ PF    : num  1.9 2.3 2.8 1.7 1.9 1.5 1.6 1.3 1.6 2.3 ...
 $ PTS   : num  9.2 8.6 20.4 7.9 9 6.2 6.3 5.9 10.4 14.3 ...

summary(nba_data)

       Rk       
 Min.   :  1.0  
 1st Qu.:132.5  
 Median :264.0  
 Mean   :266.0  
 3rd Qu.:399.5  
 Max.   :539.0  
    Player         
 Length:679        
 Class :character  
 Mode  :character  
                   
                   
                   
     Pos           
 Length:679        
 Class :character  
 Mode  :character  
                   
                   
                   
      Age       
 Min.   :19.00  
 1st Qu.:23.00  
 Median :25.00  
 Mean   :26.03  
 3rd Qu.:29.00  
 Max.   :42.00  
      Tm           
 Length:679        
 Class :character  
 Mode  :character  
                   
                   
                   
       G        
 Min.   : 1.00  
 1st Qu.:22.00  
 Median :45.00  
 Mean   :43.34  
 3rd Qu.:65.50  
 Max.   :83.00  
       GS       
 Min.   : 0.00  
 1st Qu.: 0.00  
 Median : 6.00  
 Mean   :20.07  
 3rd Qu.:36.50  
 Max.   :83.00  
       MP       
 Min.   : 1.00  
 1st Qu.:12.15  
 Median :18.80  
 Mean   :19.46  
 3rd Qu.:27.55  
 Max.   :41.00  
       FG        
 Min.   : 0.000  
 1st Qu.: 1.500  
 Median : 2.600  
 Mean   : 3.249  
 3rd Qu.: 4.200  
 Max.   :11.200  
      FGA        
 Min.   : 0.000  
 1st Qu.: 3.400  
 Median : 5.700  
 Mean   : 6.921  
 3rd Qu.: 9.200  
 Max.   :22.200  
      FG.1       
 Min.   :0.0000  
 1st Qu.:0.4150  
 Median :0.4540  
 Mean   :0.4622  
 3rd Qu.:0.5050  
 Max.   :1.0000  
      X3P        
 Min.   :0.0000  
 1st Qu.:0.3000  
 Median :0.8000  
 Mean   :0.9965  
 3rd Qu.:1.5000  
 Max.   :4.9000  
      X3PA       
 Min.   : 0.000  
 1st Qu.: 1.000  
 Median : 2.400  
 Mean   : 2.783  
 3rd Qu.: 4.150  
 Max.   :11.400  
      X3P.       
 Min.   :0.0000  
 1st Qu.:0.2860  
 Median :0.3460  
 Mean   :0.3181  
 3rd Qu.:0.3880  
 Max.   :1.0000  
      X2P        
 Min.   : 0.000  
 1st Qu.: 0.900  
 Median : 1.600  
 Mean   : 2.255  
 3rd Qu.: 3.000  
 Max.   :10.500  
      X2PA       
 Min.   : 0.000  
 1st Qu.: 1.700  
 Median : 3.100  
 Mean   : 4.138  
 3rd Qu.: 5.550  
 Max.   :17.800  
      X2P.       
 Min.   :0.0000  
 1st Qu.:0.4840  
 Median :0.5350  
 Mean   :0.5271  
 3rd Qu.:0.5940  
 Max.   :1.0000  
      eFG.       
 Min.   :0.0000  
 1st Qu.:0.4960  
 Median :0.5370  
 Mean   :0.5329  
 3rd Qu.:0.5790  
 Max.   :1.0000  
       FT        
 Min.   : 0.000  
 1st Qu.: 0.400  
 Median : 0.900  
 Mean   : 1.367  
 3rd Qu.: 1.700  
 Max.   :10.000  
      FTA        
 Min.   : 0.000  
 1st Qu.: 0.600  
 Median : 1.200  
 Mean   : 1.769  
 3rd Qu.: 2.300  
 Max.   :12.300  
      FT.        
 Min.   :0.0000  
 1st Qu.:0.6670  
 Median :0.7600  
 Mean   :0.7116  
 3rd Qu.:0.8410  
 Max.   :1.0000  
      ORB        
 Min.   :0.0000  
 1st Qu.:0.3000  
 Median :0.7000  
 Mean   :0.8423  
 3rd Qu.:1.1000  
 Max.   :5.1000  
      DRB      
 Min.   :0.00  
 1st Qu.:1.40  
 Median :2.30  
 Mean   :2.62  
 3rd Qu.:3.40  
 Max.   :9.60  
      TRB        
 Min.   : 0.000  
 1st Qu.: 1.800  
 Median : 3.000  
 Mean   : 3.461  
 3rd Qu.: 4.500  
 Max.   :12.500  
      AST        
 Min.   : 0.000  
 1st Qu.: 0.800  
 Median : 1.300  
 Mean   : 2.012  
 3rd Qu.: 2.700  
 Max.   :10.700  
      STL        
 Min.   :0.0000  
 1st Qu.:0.3000  
 Median :0.5000  
 Mean   :0.6018  
 3rd Qu.:0.8000  
 Max.   :3.0000  
      BLK        
 Min.   :0.0000  
 1st Qu.:0.1000  
 Median :0.3000  
 Mean   :0.3704  
 3rd Qu.:0.5000  
 Max.   :3.0000  
      TOV       
 Min.   :0.000  
 1st Qu.:0.500  
 Median :0.900  
 Mean   :1.067  
 3rd Qu.:1.400  
 Max.   :4.100  
       PF       
 Min.   :0.000  
 1st Qu.:1.200  
 Median :1.600  
 Mean   :1.661  
 3rd Qu.:2.200  
 Max.   :5.000  
      PTS        
 Min.   : 0.000  
 1st Qu.: 4.100  
 Median : 6.900  
 Mean   : 8.859  
 3rd Qu.:11.550  
 Max.   :33.100  

head(nba_data)

NA

Data Cleaning (if needed)

nba_data <- na.omit(nba_data)

Compute metrics Calculate points per minute as an efficiency indicator

nba_data$Efficiency <- nba_data$PTS / nba_data$MP

Statistical Analysis a)Descriptive statistics

mean(nba_data$Efficiency)

[1] 0.4215845

median(nba_data$Efficiency)

[1] 0.4049296

sd(nba_data$Efficiency)

[1] 0.1566084

Group Analysis Examine efficiency by player positions

aggregate(nba_data$Efficiency, by=list(nba_data$Pos), mean)

3. Correlation Analysis

1. Efficiency vs Points per game

cor(nba_data$Efficiency, nba_data$PTS)

[1] 0.7428214

2. Efficiency vs Total Rebounds

cor(nba_data$Efficiency, nba_data$TRB)

[1] 0.3615683

3. Efficiency vs Assist

cor(nba_data$Efficiency, nba_data$AST)

[1] 0.4172295

4. Efficiency vs effective Field Goal Percentage

cor(nba_data$Efficiency, nba_data$eFG.)

[1] 0.4044894

5. Efficiency vs 3-Point Percentage

cor(nba_data$Efficiency, nba_data$X3P.)

[1] 0.2269389

6.Efficiency vs Free Throw Percentage

cor(nba_data$Efficiency, nba_data$FT.)

[1] 0.2536861

7. Efficiency vs Minutes Played

cor(nba_data$Efficiency, nba_data$MP)

[1] 0.4436417

8. Efficiency vs Turnovers

cor(nba_data$Efficiency, nba_data$TOV)

[1] 0.5650747

9. Efficiency vs Personal Fouls

cor(nba_data$Efficiency, nba_data$PF)

[1] 0.2586232

4. Visualization

install.packages("ggplot2")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/patty/AppData/Local/R/win-library/4.3’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.3/ggplot2_3.4.4.zip'
Content type 'application/zip' length 4299925 bytes (4.1 MB)
downloaded 4.1 MB

package ‘ggplot2’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\patty\AppData\Local\Temp\RtmpIVyESS\downloaded_packages

library("ggplot2")

Warning: package ‘ggplot2’ was built under R version 4.3.2

1. Efficiency vs Points Per Game

ggplot(nba_data, aes(x=Efficiency, y=PTS)) +
  geom_point() +
  geom_smooth(method="lm", color="blue") +
  ggtitle("Efficiency vs Points Per Game") +
  xlab("Efficiency") + ylab("Points Per Game")

2. Efficiency vs Total Rebounds

ggplot(nba_data, aes(x=Efficiency, y=TRB)) +
  geom_point() + 
  geom_smooth(method="lm",color="blue") +
  ggtitle("Efficiency vs Total Rbounds") +
  xlab("Efficiency") + ylab("Total Rebounds")

3. Efficiency vs Assists

ggplot(nba_data, aes(x=Efficiency, y=AST)) +
  geom_point() +
  geom_smooth(method="lm", color="blue") +
  ggtitle("Efficiency vs. Assists") +
  xlab("Efficiency") + ylab("Assists")

4. Efficiency vs Field Goal Percentage

ggplot(nba_data, aes(x=Efficiency, y=eFG.)) +
    geom_point() +
    geom_smooth(method="lm", color="blue") +
    ggtitle("Efficiency vs. Field Goal Percentage") +
    xlab("Efficiency") + ylab("Field Goal Percentage")

5. Efficiency vs 3-point percentage

ggplot(nba_data, aes(x=Efficiency, y=X3P.)) +
    geom_point() +
    geom_smooth(method="lm", color="blue") +
    ggtitle("Efficiency vs. 3-Point Percentage") +
    xlab("Efficiency") + ylab("3-Point Percentage")

6. Efficiency vs Free Throw percentage

ggplot(nba_data, aes(x=Efficiency, y=FT.)) +
    geom_point() +
    geom_smooth(method="lm", color="blue") +
    ggtitle("Efficiency vs. Free Throw Percentage") +
    xlab("Efficiency") + ylab("Free Throw Percentage")

7. Efficiency vs Minutes Played

ggplot(nba_data, aes(x=Efficiency, y=MP)) +
    geom_point() +
    geom_smooth(method="lm", color="blue") +
    ggtitle("Efficiency vs. Minutes Played") +
    xlab("Efficiency") + ylab("Minutes Played")

8. Efficiency vs Turnovers (TOV)

ggplot(nba_data, aes(x=Efficiency, y=TOV)) +
    geom_point() +
    geom_smooth(method="lm", color="blue") +
    ggtitle("Efficiency vs. Turnovers") +
    xlab("Efficiency") + ylab("Turnovers")

9. Efficiency vs Personal Fouls (PF)

ggplot(nba_data, aes(x=Efficiency, y=PF)) +
    geom_point() +
    geom_smooth(method="lm", color="blue") +
    ggtitle("Efficiency vs. Personal Fouls") +
    xlab("Efficiency") + ylab("Personal Fouls")

Boxplot of Efficiency by Position

ggplot(nba_data, aes(x=Pos, y=Efficiency)) +
  geom_boxplot() +
  ggtitle("Distribution of Efficiency by Position") +
  xlab("Position") + ylab("Efficiency")

Scatter Plot of Efficiency vs Points per Game, Colored by Position

ggplot(nba_data, aes(x=Efficiency, y=PTS, color=Pos)) +
  geom_point() +
  ggtitle("Efficiency vs Points per Game by Position") +
  xlab("Efficiency") + ylab("Points per Game")

Facetted Scatter Plot for Each Postion

ggplot(nba_data, aes(x=Efficiency, y=PTS)) + 
  geom_point() +
  facet_wrap(~Pos) +
  ggtitle("Efficiency vs Points per Game for Each position") +
  xlab("Efficiency") + ylab("Points per Game")

Boxplot of Efficiency by Team

ggplot(nba_data, aes(x=Tm, y=Efficiency)) +
  geom_boxplot() +
  ggtitle("Distribution of Player Efficiency Across NBA Teams") +
  xlab("Team") + ylab("Efficiency") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Team Avg Eff

team_efficiency <- aggregate(Efficiency ~ Tm, data = nba_data, mean)

ggplot(team_efficiency, aes(x=Tm, y=Efficiency)) +
  geom_bar(stat="identity") +
  ggtitle("Average Efficiency of NBA Teams") +
  xlab("Team") + ylab("Average Efficiency") +
  theme(axis.text.x = element_text(angle= 90, hjust = 1))

Histogram of Player Efficiency

ggplot(nba_data, aes(x=Efficiency)) +
    geom_histogram(binwidth = 0.1, fill="blue", color="black") +
    ggtitle("Histogram of Player Efficiency") +
    xlab("Efficiency") + ylab("Frequency")

Efficiency vs Age Scatter Plot

ggplot(nba_data, aes(x=Age, y=Efficiency)) +
    geom_point() +
    geom_smooth(method="lm", color="red") +
    ggtitle("Efficiency vs. Age") +
    xlab("Age") + ylab("Efficiency")

Efficiency Heatmap by Position and Team

ggplot(nba_data, aes(x=Tm, y=Pos, fill=Efficiency)) +
    geom_tile() +
    scale_fill_gradient(low="white", high="red") +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8),  # Adjust angle and size
          axis.text.y = element_text(size = 8)) +
    ggtitle("Efficiency Heatmap by Position and Team") +
    xlab("Team") + ylab("Position")

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