This analysis uses ANOVA and linear regression to explore factors that influence NBA Game Score (GmSc). The ANOVA test examines whether Game Score differs between playoff and regular season games, while the regression model evaluates how points scored relates to Game Score. Results show a strong relationship between scoring and overall game performance.

This analysis compares unexpected NBA player performances in the playoffs versus the regular season. Using Game Score for Neyman-Pearson test and 30+ point games for Fisher's Significance Testing, playoff performances were significantly higher both in average Game Score and in the likelihood of scoring 30+ points. Results suggest that playoff intensity, star usage and game context contribute to elevated player performance.

This data dive examines how Game Score relates to statistical “unexpectedness” in NBA performances. By creating derived variables and analyzing their relationships, I evaluate correlation, outliers, and confidence intervals while also reflecting on how documentation and metric construction affect interpretation. The analysis highlights the importance of understanding how performance metrics are calculated before drawing conclusions.

This data dive emphasizes the importance of documentation when it comes to data and explores some of the attributes that can be confusing at first glance. It also looks for both explicit and implicit missing values as well as empty groups within the dataset. Outliers are also discussed towards the end.

This data dive explores the effects of different sample size within NBA unexpected player performance data.

This data dive explores NBA player data of unexpected performance, using grouping and probabilities to spot common patterns and rare cases, with visualizations and short interpretations along the way.

Explores summary statistics and visual patterns in unexpected NBA player performance data with a focus on scoring and overall impact between both regular season and playoff games.