Rows: 1703 Columns: 19
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): bbrID, Tm, Opp, Season
dbl (12): TRB, AST, STL, BLK, PTS, GmSc, Year, GameIndex, GmScMovingZ, GmSc...
lgl (1): Playoffs
date (2): Date, Date2
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
ggplot(nba_data, aes(x =factor(Playoffs), y = GmSc, fill =factor(Playoffs))) +geom_boxplot() +labs(title ="Game Score (GmSc) Distribution by Playoff Status",x ="Playoff Participation (0 = No, 1 = Yes)",y ="Game Score (GmSc)") +theme_minimal()
Playoff players show higher and more varied Game Scores.
##Scatter Plot – Rebounds vs Points
Correlation Between Total Rebounds (TRB) and Points Scored (PTS)
ggplot(nba_data, aes(x = TRB, y = PTS)) +geom_point(alpha =0.5) +geom_smooth(method ="lm", col ="blue") +labs(title ="Correlation Between Total Rebounds and Points Scored",x ="Total Rebounds (TRB)",y ="Points Scored (PTS)") +theme_minimal()
`geom_smooth()` using formula = 'y ~ x'
#Weak but statistically significant correlation (r ≈ 0.091, p = 0.000161)
#Hypothesis Test: GmSc
H₀: No difference in GmSc between playoff and non-playoff playersH₁: There is a significant difference
p-value < 0.05 → Reject H₀
✅ Playoff participation correlates with higher performance
#Interpretation – Correlation
Pearson r = 0.091 → weak positive correlation
p = 0.000161 → statistically significant
Players with more rebounds tend to score slightly more points
#Conclusions
Playoff players have higher Game Scores
Rebounding has a weak but significant impact on scoring
Actionable Insight: Prioritize playoff experience and multi-metric evaluation