The objective of this project was to investigate potential correlations between specific player attributes of 2023 NFL teams—such as average age and weight across different positions—and key performance metrics from the 2023 season, including points scored (PF), points allowed (PA), and yards per return (Y/Rt). Specifically, the analysis aimed to explore the following relationships:
Offensive Attributes: Correlation between the average age and weight of offensive players and the total points scored by the team (PF). Defensive Attributes: Correlation between the average age and weight of defensive players and the total points allowed by the team (PA). Special Teams Attributes: Correlation between the average age and weight of special teams players and the average yards per return (Y/Rt).
By evaluating these relationships, I aim determine if there were any statistically significant correlations that might warrant further investigation. Such insights can be valuable for betting strategies and sports analysis, especially since these relationships might exist only for short periods, such as within a single season or two. Searching for these trends most often yields no new correlations but there are points in time when some statistical relationships such as these become evident upon inspection. The project begins with the assumption that it is highly unlikely that these meaningful correlations will be evident and thus a small but significant sample size is first tested.
The analysis focused on four AFC East NFL teams: the Buffalo Bills, Miami Dolphins, New York Jets, and New England Patriots. Data was obtained from Pro Football Reference website, converted into CSV files, and imported into a Python environment for analysis. Python's pandas and numpy libraries were used extensively for data manipulation, calculation of averages, normalization of data, and application of statistical tests.
CSV files containing player data for each team were loaded into Python using pandas. The datasets were processed to use the correct headers and remove any extraneous rows. The player data was grouped by position, and calculations were made for the average age and weight for each position group (e.g., offensive, defensive, special teams).
Categorization of Positions: Positions were categorized into offense, defense, and special teams to facilitate focused analysis. Each position was mapped to a consolidated category (e.g., quarterbacks, running backs, defensive backs) to ensure a consistent and meaningful comparison across teams.
Normalization: The average age and weight for each position within each team were normalized to account for potential differences in scale and distribution. This allowed for a fair comparison of metrics across teams and positions.
Correlation Analysis: The Pearson correlation coefficient was applied to evaluate the linear relationship between player attributes (average age and weight) and team performance metrics (PF (Points For), PA (Points Against), Y/Rt (Yards per Return).
The correlation coefficient (r) indicates the strength and direction of the relationship, while the p-value assesses the statistical significance of the observed correlation.
Understanding Correlation and Statistical Significance Correlation Coefficient (r):
Measures the strength and direction of a linear relationship between two variables. A value close to 1 or -1 indicates a strong positive or negative correlation, respectively, while a value close to 0 suggests little or no linear relationship.
P-Value: The p-value helps determine the significance of the observed correlation. It represents the probability that the observed correlation could have occurred by random chance, assuming no actual relationship exists in the population. A low p-value (typically less than 0.05) suggests that the observed correlation is unlikely to have occurred by chance, indicating a statistically significant relationship.
Why the P-Value Matters: Even when the correlation coefficient (r) appears moderately strong (e.g., 0.59), a high p-value (e.g., 0.41) suggests there is a substantial probability (41%) that the observed correlation could have occurred by random chance. This means we cannot confidently assert that the observed correlation reflects a true relationship in the population; it could simply result from random variation in the sample data.
To draw meaningful conclusions, both the correlation coefficient and the p-value should be considered together:
A high correlation with a low p-value indicates a statistically significant and potentially meaningful relationship. A high correlation with a high p-value suggests that, although there seems to be a relationship, the evidence is insufficient to rule out random chance. Conversely, a low correlation with a low p-value still indicates a weak but statistically significant relationship, which might not be practically meaningful.
The results of the Pearson correlation analysis were as follows: Offense - Age vs Points For (PF): Correlation: -0.67 P-Value: 0.33 Interpretation: There is a moderate negative correlation between the average age of offensive players and points scored (PF). However, the p-value of 0.33 suggests that this correlation is not statistically significant, as there is a 33% chance that this result could occur by random chance.
Offense - Weight vs Points For (PF): Correlation: -0.59 P-Value: 0.41 Interpretation: A moderate negative correlation was observed between the average weight of offensive players and points scored (PF). The p-value of 0.41 is relatively high, indicating that there is a 41% likelihood that this correlation occurred by random chance, thus rendering it statistically insignificant.
Defense - Age vs Points Against (PA): Correlation: -0.19 P-Value: 0.81 Interpretation: The correlation between the average age of defensive players and points allowed (PA) is weakly negative, with a correlation coefficient of -0.19. The p-value of 0.81 is very high, meaning there is an 81% probability that this correlation could have occurred by random chance, further confirming its insignificance.
Defense - Weight vs Points Against (PA): Correlation: 0.63 P-Value: 0.37 Interpretation: There is a moderate positive correlation between the average weight of defensive players and points allowed (PA). However, the p-value of 0.37 suggests that there is a 37% chance that this observed correlation could occur by random chance, indicating that it is not statistically significant.
Special Teams - Age vs Yards per Return (Y/Rt): Correlation: -0.54 P-Value: 0.46 Interpretation: The correlation between the average age of special teams players and yards per return (Y/Rt) is moderately negative. The p-value of 0.46 indicates a 46% chance that this correlation could have occurred by random chance, making it statistically insignificant.
Special Teams - Weight vs Yards per Return (Y/Rt): Correlation: 0.39 P-Value: 0.61 Interpretation: A weak positive correlation was found between the average weight of special teams players and yards per return (Y/Rt). With a p-value of 0.61, there is a 61% probability that this result could have occurred by random chance, confirming that this relationship is not statistically significant.
Conclusion: Overall, the findings did not reveal any statistically significant correlations between the analyzed player attributes and team performance metrics. The moderately strong correlation coefficients observed in some cases (such as offense age vs. Points For and defense weight vs. Points Against) were negated by high p-values, indicating a substantial probability that these results could occur due to random chance. Therefore, there is insufficient evidence to suggest a meaningful relationship between these metrics within the examined teams.
Given the lack of significant findings, the project concludes without expanding to additional teams, as further analysis would not likely yield more meaningful results. The initial hypothesis that it is unlikely significant correlations would be present in the data appears to be validated by these results. This does not mean that at some point in the NFL season evolution that this or other correlations hold true for periods of time and can be used as forecast variables.