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Exploring Correlations in Player Performance Indicators: Games Played, Age, and Points Scored

This study investigates various hypotheses regarding the linear relationships between NBA players' performance metrics, including the number of games played, player age, field goal percentages, and salaries. It establishes weak correlations between these variables, such as an increase in games leading to higher points scored and a decrease in points with increasing age. The findings indicate that while trends exist, they are not strong, suggesting more factors may influence player performance. Data was sourced from ESPN and team statistics.

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Exploring Correlations in Player Performance Indicators: Games Played, Age, and Points Scored

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  1. Linear Regression and the By: Nancy Thach & Alexis Ammons July 11, 2011

  2. HYPOTHESES • There is a correlation between the number of games a player played & total points scored. • There is a correlation between the age of the player & total points scored. • There is a correlation between the age of the player & salary. • There is a correlation between field goal percentage of a player & 3-point percentage. • There is a correlation between field goal attempts made by a player & field goal percentage.

  3. Definitions • A Field Goal occurs when the ball enters the basket from above during play; worth 2 points, or 3 points depending on if the shooterwas standing behind the 3-point line. • A3-Point Shot is a fieldgoalworth 3 points because the shooter had both feet on the floorbehind the3-point line when he released the ball; also counts if one foot is behind the line while the other foot is in the air.  

  4. Source of Data: • ESPN Website: http://espn.go.com/nba/team/stats • Chicago Bulls Website: http://espn.go.com/nba/team/roster

  5. r = .457 • Weak Positive Linear Correlation • Prediction: If a team member played 90 games, he would score a possible 11.2 points. • y = 0.102(90) + 2.041 • y = 11.2

  6. r = .426 • Weak Negative Linear Correlation • Prediction: If a team member is 24 years old, it is estimated that he will score 10.8 points. • y = -0.724(24) + 28.21 • y = 10.8

  7. r = .369 • Weak Negative Linear Correlation • Prediction: If a player is 24 years old, it is estimated that he will make about $18,865,592 per year. • y = -47267(24) + 2E+7 = 18,865,592

  8. r = .008 • Extremely weak positive correlation; line is almost horizontal. • Prediction: If a player has a field goal percentage of 0.35 = 35%, then his 3 - Point Percentage is estimated to be 0.263 = 26.3%. • y = 0.031(0.35) + 0.252 = .263

  9. r = .006 • Extremely weak positive correlation; line is almost horizontal. • Prediction: If a player makes 20 Field Goal attempts, then his field goal percentage would be .465 = 46.5%. • y = 0.00007(20) + 0.464 = .465

  10. Conclusions • As the number of games played increases, thetotal number of points a player scores increases. The correlation is weak. • As age increases, the total number of points that he scores decreases. The correlation is weak. • As age increases, salary decreases.The correlation is weak. • As field goal percentage increases, 3-point percentage is almost constant. The correlation is extremely weak. • As field goal attempts increases, field goal percentage is almost constant. The correlation is extremely weak.

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