Choose a case study either from page 2 of the attached 'CASE STUDY' document or another case study found on the web regarding Sports Analytics (i.e how Analytics is used in Physical The

The Endgame: Presentation

Understanding the Strength of a Relationship

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.

Final Project-Case Study

Zakai Meghoo-Peddie

Professor Phillips

HKSP 464: Sports Analytics

Morehouse College

12/05/2021

1

Final Project-Case Study

Abstract:

We see numerous representations of what it means to be the favorite team and club to win

it all in the sports world. One of the most common representations in sports is the home field

advantage which is the perceived built-in edge that the team playing at home will have for the

contest. This advantage can be described as non-specific and further broken down into various

sectors such as the outcome of a match, points or goals scored, defensive performance, and

individual player performance. The home-field advantage raises questions and assumptions about

whether the team in this position has some competitive advantage or if this theory is merely a

psychological notion that holds little to no value. In this study, there will be empirical evidence and

research to explain the theory of a home-field advantage in soccer, if any. Methods to be used in

this study will include qualitative and quantitative indicators and descriptors that provide an

analysis of home-field advantage statistics from various leagues at different competitive levels

across the globe.

Figure 1

2

Final Project-Case Study

The conversation of home-field advantage has been a topic of discussion nearly since the

inception of organized soccer in 1863 in England. Nearly every game since sports media began has

certain buzzwords or mentions of the term “home team” that reflects some sort of impact that being

a home team might have, whether negative or positive. In most cases, fan engagement is expected

to be more positive, impactful, and desirable in the case of a match where a home team is present

as opposed to a meeting at a neutral location for match day. Factors that play a role in fan

engagement include geographic and economic attributes. These factors indicate a somewhat of an

advantage as matches at home tend to have more local spectators, support in ticket sales from team

fans tend to be higher, and merchandise is often purchased at a higher rate when teams have home

matches. All these factors indicate a huge boost in support for the home team through fan

engagement, but they don’t point out a clear advantage in home teams’ overall match success. If

anything, they might seem more beneficial to a team from a sales and reputation perspective.

For a long time, it was a commonly held belief about the home-field advantage that through

their support, fans elevated athletic performance, thus contributing to the overall home advantage.

However, this theory is only partially correct. It is the referees’ job to maintain order and act as a

disciplinarian on the field, and just like the players, she or he has the potential to really change the

outcome of the match. In soccer, the referee can impartially dictate the match by calling fouls and

penalties, adding more time to the match, and issuing red and yellow cards. It is rare that the

decisions of the referees are impartial in nature because human bias will always be present.

According to data compiled from 128 Italian Serie A soccer games, referees were found to

statistically favor home teams. In each game that was analyzed, referees awarded, on average, 2.69

yellow cards, .27 red cards, and .17 penalties for each team.

3

Final Project-Case Study

However, the home team’s average was 2.65 yellow cards, .23 red cards, and .2 penalties,

while the away team averaged 2.73 yellow cards, .31 red cards, and .15 penalties (Scoppa and

Schwartz, 2014). These statistics are intriguing because any of those decisions by the referee has

the potential to alter the outcome of multiple matches. For example, when a player receives a red

card, their team will have to resume the remainder of the match with one less player. Also, when a

team is awarded a penalty kick, nearly 76.8% of the time, a goal is a result (Hawerchuk, 2010).

Furthermore, another study discovered that when adding stoppage or injury time, the

referee’s decision tends to go in the way of the home team. In the Bundesliga (German Soccer

League), and the Major League Soccer (MLS), when the home team loses by a goal, on average,

extra time, is 12% versus when they’re winning by a goal. More notably, an example of this home-

field advantage bias could be represented by the team, Stuttgart. The average stoppage time added

when winning is 2 minutes and 20 seconds, and when they are losing a match, it is 3 minutes and

41 seconds. The differential in stoppage time is 1 minute and 21 seconds, or 57% (Bialik, 2014).

While these numbers may appear insignificant, it is vital to remember that a goal can be scored in a

matter of seconds. So, if a team is winning, it is beneficial to have less stoppage time, but if they

are losing, the more time, the greater chance they have to score. Additionally, the implications of

these results are worrisome.

4

Final Project-Case Study

Figure 2

5

Final Project-Case Study

Although it may not directly affect players and their performances, crowd pressure can

very much so influence the referees. A study in 1993 by Professor Simo Salimen analyzed 56

matches where there was a home team present, but the crowd was neutral, so the fan support was

equal. In this research, Salimen discovered that the home team did statistically perform better than

the visiting team when their fans supported them; even when the opposing crowd supported the

other team, they still managed to score more goals (Salimen, 1993).

In addition, Alan Nevill conducted research in which he divided 40 referees into two

groups of 20. Then he had them watch a replay of a match between Leicester City and Liverpool

that took place in Anfield, otherwise known as Liverpool’s home stadium. The first group watched

the replay with audio while the second group watched the replay in silence. Although both groups

of referees watched the same game, Nevill had them decide whether 47 different incidents were

fouls or not. The first group of referees who watched with audio, where the crowd supported

Liverpool, called about 15.5% fewer fouls against Liverpool as opposed to the silent group of

referees (Ingle, 2013). The results of Salimen’s research contradict the idea that the home crowd

affects players and provides a direct home-field advantage. Instead, Nevill’s experiment is a clear

indicator that crowd behavior can directly influence the decisions of referees, which can shift the

outcome of a match.

6

Final Project-Case Study

The aforementioned findings demonstrate that home teams have an advantage, but they do

not clearly define how much impact those factors such as referee and fan engagement can have on

the outcome of a match. Essentially, how effective is home advantage? Findings from statistics

from the English Premier League seasons 2009 to 2013 found that a home team’s win percentage

was 47% and their away win percentage was nearly 27%. Also, 15 out of 20 of these English

7

USEFUL NOTES FOR:

Analytic Plan to Guide

Introduction

A/B testing is a great way to test your website’s user experience, but it can be difficult to do. There are many variables that come into play when you compare two A/B tests, and determining how you’ll use them after the fact can be tricky. In this post we’ll go over a plan for how to analyze your A/B tests so that your results will be as useful as possible.

An A/B Test

An A/B test is a common method for comparing two versions of a website in order to identify which version performs better. A/B testing can be used to compare different designs, copy or calls-to-action.

An example of how you might use an A/B test is by creating two different landing pages for your product: one with more information about the product and one with less information about the product. You would then measure the click-through rate (CTR) by determining which variation had the highest CTR among all visitors who saw that variation during their visit to your website. The higher number represents more people clicking through while they were browsing through those pages compared with other variations during their visits – in this case, there was no difference between them!

Problem to be Solved

In order to clearly define the problem and set measurable benchmarks, you need to know what metrics are important. The most common metrics are:

Revenue growth

Conversion rate (the percentage of people who sign up or make a purchase)

Customer retention rate (the number of people who have been customers for a certain amount of time)

Metric Choice

The metric you choose to measure your test’s effectiveness will depend on the goals of your test. For example, if you are trying to improve conversion rates and increase revenue, then it makes sense that a metric like “conversion rate” or “revenue per user” would be a good indicator of success. On the other hand, if your goal is simply to improve user experience (UX), then metrics like “time on page” or “click-through rates” may be more appropriate because they directly relate with how well users interact with your product.

Once you have chosen what kind of metric will best represent what you want from this particular experiment—and whether or not it should include any other factors such as cost per acquisition—you can begin setting up your plan!

Measuring Standard Deviation

Standard deviation is a measure of spread, or variability, in a set of data. The standard deviation takes into account both the mean and the expected value of each value in your sample.

The formula for calculating standard deviation is:

where n represents the number of observations in your sample and s2 represents their squared deviations from their mean. You can use this formula to calculate your own sample’s SD, which will be somewhere between 0 and 1—the larger it is closer to 1 (i.e., more negative), while smaller values indicate less variability within the population and greater variability among individuals within that population.

Although it may seem like an easy way out at first glance when trying to understand why certain events happen over time; however, there are some caveats worth mentioning here:

Measuring Variability

The variability of your data is the difference between the lowest and highest values. You can calculate this value using standard deviation, which is a measure of how much data points deviate from their mean or average. The larger the standard deviation, the more variable your data set is (and therefore less reliable).

When you’re measuring variability in any kind of metric, keep in mind that it will be lower when there are fewer observations than when there are more ones—but don’t just take this at face value! If there are only two points on a graph but one point has an extremely high value compared to another one behind it with similar measurements then perhaps those measurements aren’t as accurate as we think they might be; maybe both sets should be moved closer together instead because it’s clear now that something isn’t right about either set alone

Which Test to Use

The first step in the process of choosing which test to use is to determine what information you want to collect and how it will help you make decisions. For example, if your goal is to gather data on how well your product is selling, then you might decide that a quantitative survey would be more appropriate than an interview or focus group.

If there are several different types of tests that could help answer your question(s), then it’s always best practice to consider all possible options before making a decision about which one(s) would work best for your situation.

Test Analysis and Results

In this section, you will post your test results. A breakdown of the results can be found in your Analytic Plan to Guide.

Here is an example of how to format these:

[Test Analysis and Results](https://www.example.com/my-analytics-report/)

Conclusion

After all, you’re probably hoping to get the most out of your analytics plan. And we can help! Our team of data analysts will work with you to create tests that will give you the results you need, and we also offer our services as consultants for clients who want more hands-on guidance on how to implement their own data-driven approaches. Contact us today to learn more about what we can do for your organization—we look forward to working with you!

</