Final Project - Analyzing a Use Case for Case Study

Case Study Analysis: Analyzing Player Behavior In

The NFL Runningback

Simmione Sauls 12-2-21 HKSP-464-01

Introduction

Since 1920, the National Football League (NFL) has been in existence. NFL management, teams, and players have sought a competitive advantage over their opponents since the league's start. With the help of different analytical tools and data sources, teams around the league have found ways to gain the upperhand on their competitors. As years go by, the behavior of players has been one of the harder areas to collect information. Due to the seemingly fast pace and rigorous contact involved with the game, it has never been easy to analyze the behaviors of a football player (such as the effect of fatigue and stress during the course of a game, speed/agility during plays, force taken/given during contact, etc.) and there has always been room for improvement in terms of analysis tools. In modern days AI technology can help make these analysis and calculations that are too tough for the human eye to see.

Phase of Analytics

The analysis of player behavior would be classified as predictive analytics. When investigating

player behavior, teams try to figure out what factors make a difference in a players

performance. Learning and understanding these factors that affect the athlete can help

predict when the player will potentially be at his best and worst, as well as help identify

factors/behaviors that can either help or hurt the performance of the player. The main

question that I am trying to answer by studying these types of analytics uis “what factor is

most associated with how many rushing 100 yards in a game?”

Data Acquisition

For this analysis, data will be taken from three sources. These sources consists of the three

pieces of equipment that every player must wear; helmet, shoulder pads, and cleats. Data will

be collected by speed and force detection devices that are placed in each source. These

devices will be placed in the equipment of running backs across the league, in order to see

what plays the biggest part in their ability to rush for 100 yards or more in a game.

Variable Selection

Aside from physical attributes, there are several variables that factor in when measuring

rushing success as a football player. Of these three factors, the most important are speed,

strength, and control. Speed is self explanatory, it is simply how fast the ball carrier is moving

once they have the ball and is measured by there 40 yard dash time. Strength is more

complex, instead of measuring weight room strength, it measures how much force a player

can deliver/receive while running the ball, and could be measured by a player’s weight.. Lastly,

control measures how many yards a player averages a carry, as well as a players ability to stay

moving after contact as well as their ability to control the ball during contact.

Exploratory Data Analysis

For this analysis, i figured the best way to present and analyze the dataset was with a scatter

plot.Scatter Plots not only give you a vast amount of data in one place, but they also can show

relationships between variables. In my opinion, scatterplots are best used when you have a lot

of numerical data to analyze, because they can show you the correlation between two

variables and whether or not that correlation is a positive or a negative one. See Dataset for

examples.

Model Selection

The ultimate goal of the study is to not only see what factors play in to running backs having successful rushing games, but also to see what attributes have a stronger correlation with rushing success. I choose to use a regression model for this problem because it is the most efficient and simplistic way of seeing the correlations between the y and x variables, and it gives you the values that you need with just a few clicks. My Y variable will be the average number of yards produced in a game, and my x variables will the speed, strength, and control numbers that I collected.

Evaluation Metrics

I would evaluate the predictions of my regression model by paying attention to my p-values. By examining these, I can get a feel for which one of my variables has the greatest correlation, as well as see which variables have little to no correlation at all. Fortunately for my study, all of my variables had some kind of correlation, which means that each variable plays a part in the amount of total yards a game.

Testing

In order to fully test the model, I would need to get a sample group of NFL running backs that are known to produce a lot of yards each game, as well as access to their equipment. I will also need the sensor devices to track the speed and force in the equipment. The players would have to do nothing out of their ordinary routine except make sure the sensors are in the pads and cleats at all times when there is practice or a game. This will help collect unbiased data, because some of the data recorded could be from only games where the player played well if the test is not well monitored.

Conclusion

In conclusion, I believe that the observation and analysis of player behaviors is very necessary in this new era of sports. The speed, strength, and control that a running back has during the course of a game can be forever changing due to reasons that we can’t physically see and therefore can’t measure accurately. However, by analyzing the tangible factors we are able to see what physical traits play the biggest role. Of the three variables that I measured, control while carrying the football had the most significance when it came to determining which variable correlated more with averaging 100 or more yards a game.