marta.github.io

NBA Trends Analysis: Knicks vs Nets (2010 & 2014)

This project explores NBA game data to compare the performance of the New York Knicks and the Brooklyn Nets in the 2010 and 2014 seasons. The analysis includes data exploration, visualization, statistical testing, and correlation analysis.

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📊 Dataset

The dataset contains NBA game-level data with the following key variables:

• game_id: Unique identifier for each game
• year_id: Season year
• fran_id: Franchise name (e.g., Knicks, Nets)
• opp_fran: Opponent franchise name
• game_location: Home (H) or Away (A)
• is_playoffs: Indicator for playoff games
• pts: Team points scored
• opp_pts: Opponent points scored
• game_result: Win (W) or Loss (L)
• forecast: Win probability forecasted by FiveThirtyEight
• point_diff: Point difference (team points – opponent points)

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🔎 Exploratory Data Analysis

Example visualization:

• Knicks vs Nets (2010 vs 2014): Histograms and boxplots compare scoring distributions across seasons.
• Home vs Away: A chi-square test was used to check if game results are independent of location.
• Forecast vs Outcome: Scatterplots and correlations were used to evaluate how well FiveThirtyEight’s forecasts aligned with actual point differences.

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📈 Statistical Analysis

• Chi-Square Test: Tested independence between game_result and game_location.
• Correlation: Pearson correlation between forecast and point_diff shows forecasts are positively correlated with actual results.
• Covariance Matrix: Quantified joint variability of forecasts and point differences.

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🛠 Tools & Libraries

• Python (pandas, numpy, matplotlib, seaborn, scipy)
• Jupyter Notebook

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📌 Key Learnings

• How to explore categorical and numerical relationships in sports data.
• Using contingency tables and the chi-square test to analyze categorical variables.
• Evaluating predictive accuracy with correlation analysis.
• Building clear, visual comparisons across seasons and teams.

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🚀 Next Steps

• Extend analysis to other teams and seasons.
• Explore playoff vs regular season differences.
• Build an interactive dashboard (e.g., Streamlit) for dynamic comparisons.

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