final project

 Introduction 

I looked at whether high-volume shooters in the NBA are actually less efficient than low-volume shooters. Most people assume that players who take a lot of shots end up shooting a lower percentage, but I wanted to test if there is a real statistical relationship between shot volume and field goal percentage. The reason I picked this because I love sports there are so many different statistics and oddities in it I can talk about sports forever the sports world as only gotten increasingly analytical, so it made sense for this to be my topic  

Data & Methods  

The dataset included 1,003 NBA player seasons with stats like field goal attempts (FGA), field goal percentage (FG%), points, rebounds, and more. I used the median number of field goal attempts to split players into high-volume and low-volume shooters, with 501 players in each group. 

To analyze the relationship, I ran a two-sample t test comparing FG% between the two groups, a correlation test between FGA and FG%, and a simple linear regression to see how much FGA predicts FG%. 

Results  






T-test Results  

Low-volume shooters had a higher average field goal percentage (FG%) (.501) than high-volume shooters (.481). A two-sample t-test showed that this difference in mean FG% was statistically significant, t(933.31) = -6.82, p < .001. This means that, on average, low-volume shooters are a bit more efficient from the field than high-volume shooters. 

t.test(fg_pct ~ volume_group, data = nba) 

 

Correlation results 

 

I also looked at the overall relationship between field goal attempts per game (FGA) and field goal percentage (FG%). There was a significant negative correlation, r = -0.27, p < .001. This indicates that as players take more shots per game, their field goal percentage tends to go down. 

cor.test(nba$fga, nba$fg_pct) 

Regression results 

To quantify how much shot volume affects efficiency, I ran a simple linear regression with FG% as the outcome and FGA as the predictor. The regression showed a significant negative slope, b = -0.0035, p < .001, with R² ≈ .07. This means that for every additional field goal attempt per game, a player’s field goal percentage decreases slightly (about 0.35 percentage points), and shot volume explains around 7% of the variation in FG%. 

model <- lm(fg_pct ~ fga, data = nba) 
summary(model) 
 

Discussion & Conclusion 

All three analyses pointed in the same direction: high-volume shooters tend to be less efficient than low-volume shooters. This makes sense in real basketball. High-volume shooters usually take tougher, contested shots, while low-volume shooters tend to get easier, open looks, which helps their percentage. Overall, the results show that shot volume does affect field goal percentage in the NBA. 

Comments

Post a Comment

Popular posts from this blog

Module 10

 lm(cystfibr$spemax ~ age + weight + bmp + fev1, data=cystfibr) anova(lm(cystfibr$spemax ~ age + weight + bmp + fev1, data=cystfibr))   I used the cystfibr dataset from the ISwR package In completing this assignment. I ran a regression on spemax Based on the predictor variables age, weight, BMI an FEV 1 with spemex is being predicted. My results indicated that my overall model model was statistically significant. Therefore, at least one of the predictor variables is significantly associated with spemax .   Based upon the coefficients produced in this analysis, I examined age. Since the coefficient for age was negative, it can be concluded that the age increases spemex We'll decrease while controlling for the effects of the other predictor variables. This indicates that older patients generally exhibit lower maximum specific power.  
Read more

basic graph

Image
 For this assignment I created a histogram in R using the built in mtcars dataset to show the distribution of miles per gallon (mpg). The visualization makes it easy to see that most cars fall between about 15 and 25 mpg, with only a few vehicles above 30 mpg. This fits with Few’s and Yau’s discussion on basic descriptive visualization because it focuses on clarity and simplicity rather than decoration. The chart communicates one clear message about the spread of the data and avoids unnecessary visual elements. Overall, it works well as a basic example of descriptive analysis in R.
Read more

Module 3 assignemnt

  1. Central Tendency Set #1: 10, 2, 3, 2, 4, 2, 5 Mean = 4.0 Median = 3.0 Mode = 2 Set #2: 20, 12, 13, 12, 14, 12, 15 Mean = 14.0 Median = 13.0 Mode = 12 2. Variation Set #1 Range = 8 IQR = 2.5 Variance = 8.33 Standard Deviation = 2.89 Set #2 Range = 8 IQR = 2.5 Variance = 8.33 Standard Deviation = 2.89 3. Comparison Set #1 stays pretty low overall with an average of 4, a median of 3, and a mode of 2. Set #2 is higher across the board with an average around 14, a median of 13, and a mode of 12. Basically, Set #2 looks like Set #1 just shifted up by about 10 points. The spread is the same in both sets though. They have the exact same range, IQR, variance, and standard deviation. That means the consistency of the data is identical, even though one set has smaller values and the other has larger ones. So overall, the difference is just where the numbers sit . Set #1 clusters around lower values, while Set #2 clusters higher...
Read more