• As basketball fans we wanted to evaluate how our favorite players impact the game

• Players that have played a minimum 500 minutes this season in order to provide a reasonable sample size

• PER, USG%, TS%, 3PAr, and FTar

• These statistics best evaluate efficiency over volume

-Player efficiency rating (PER)

• The difference between Positive and Negative accomplishments divided by the minutes

• The average is 15 and the highest achieved was 31.98 in 2019-2020

-Usage Percentage (USG%)

• Estimate of team plays that revolved around a certain player while they were on the court

-https://www.basketball-reference.com/about/per.html

-https://www.basketball-reference.com/about/glossary.html

ggplot(df, aes(x = USG, y = PER)) + geom_smooth(method = "lm") +
    geom_point(aes(x = USG, y = PER)) +
    geom_point(aes(x = embiid$USG, y = embiid$PER, color = "Embiid"), 
               size = 1.5) +
    geom_point(aes(x = doncic$USG, y = doncic$PER, color = "Doncic"), 
               size = 1.5) +
    geom_point(aes(x = lillard$USG, y = lillard$PER, color = "Lillard"), 
               size = 1.5) +
    geom_point(aes(x = jokic$USG, y = jokic$PER, color = "Jokic"), 
               size = 1.5) +
    geom_point(aes(x = lebron$USG, y = lebron$PER, color = "Lebron"), 
               size = 1.5) +
    labs(x = "Usage Rate(%)",
         y = "Player Efficiecy Rating",
         color = "Legend") +
    scale_color_manual(values = colors) + ggtitle("PER vs USG%")
    + theme(plot.title = element_text(hjust = 0.5))

-Colors represent MVP candidates

-summary(mod) determines the effectiveness of the data

-r^2 represents how close the data is to the line of best fit

-r^2 = 0.453 which shows a moderate correlation

• Represents 3 different variables

• Higher the win shares the bigger the circle

• A higher PER best relates to the amount of Win shares

• True Shooting Percentage (TS%) -Measure of shooting efficiency taking into accoiunt of total field goals and free throw
• 3par -The percentage of shots that come from the 3-point line
• ftar -The amount of free throw attempts over the amount of field goal attempts
• https://www.basketball-reference.com/about/glossary.html
• https://www.sports-reference.com/blog/2013/10/small-additions-to-basketball-reference/

relation <- lm(TS ~ THPAr + FTr, data=df)
predicted_TS <- data.frame(ts_pred = predict(relation, df), THPAr=df$THPAr, FTr = df$FTr)
fig <- plot_ly(x=df$THPAr, y=df$FTr, z=df$TS,
               type="scatter3d", 
               mode="markers",
               color = df$TS) %>% hide_colorbar()

• Higher True shooting is related to higher 3 point attempt rate
• Most are bunched around similar free throw attempt rate and 3 point attempt rate
• Shows how NBA is becoming more homogeneous in shot selection
• A few data points around very high free throw rates with high true shooting
• P-value of 4.007e-08 and R^2 of .1204

• Moderate correlation between volume and efficieny
• Expected because high volume should theoretically lower efficiency
• PER typically more valuable in wins
• 3 point shots lead to greater efficiency
• NBA is becoming more homogeneous with both shot selection and offensive system