A comprehensive SQL database adhering to Boyce Codd Normal Form, utilizing publicly available data from Europe's top 5 leagues, which served as a centralized repository for efficient and analytical statistical research on players, and developed an interactive program tailored for scouts, streamlining the scouting process and enabling informed decision-making.

I utilized publicly available data from Europe's top 5 leagues and designed a comprehensive database using SQL that adhered to Boyce Codd Normal Form. This database served as a centralized repository, incorporating a wide range of information, to facilitate efficient and analytical statistical research on players.

Building upon the structured database, I developed an interactive program specifically tailored for scouts. This program provides them with a user-friendly interface to navigate through the database and access relevant player information efficiently. By utilizing the program, scouts are able to conduct in-depth statistical analysis on players, enabling them to make more informed decisions when scouting for talent.

The integration of the comprehensive database and the interactive program significantly streamlines the scouting process, allowing scouts to access all necessary information in one place. This approach eliminates the need for manual data gathering and enables scouts to focus more on the analytical aspects of player evaluation.

Overall, my project aims to leverage the power of SQL and an interactive program to enhance the scouting process by providing scouts with a robust tool to efficiently and analytically research players using the extensive data available from Europe's top 5 leagues.

**ER Diagram**

****![](/images/download-d33b3e78.png)

**Query Examples**

**1.**

****![](/images/query1.png)![](/images/quer1res.png)

**2.**

****![](/images/query2.png)

![](/images/query2res.png)

3\.

![](/images/query3.png)

![](/images/query3res.png)

**Interactive Program Example**

****![](/images/Intercative%20app%20ex.png)

***

***

For more information, check out the projects GitHub repository: https://github.com/hauckshane/shauc19_CS345Fall22/tree/main/FinalProject

Comprehensive analysis of how coaches and analysts can utilize soccer tracking data analysis to improve a team's defensive performance. The paper provides practical insights into using hierarchical clustering to define defensive formations and make informed decisions on tactical strategies.

**Abstract**

"Organizing For Success: A Tactical Analysis of Defensive Structure in Soccer" provides a
comprehensive analysis of the importance of defensive tactical analysis in soccer and how it can be
applied to improve team performance. The paper provides practical insights into how coaches and
analysts can gain a more comprehensive and objective view of a team's defensive performance by
utilizing data on soccer passing events and player locations.

One of the key methods discussed in the paper is the use of hierarchical clustering to define
soccer defensive formations. This process involves the use of player location data from passing events to
group similar data points into clusters based on their proximity to each other. The process starts by
grouping the data into three clusters representing the formation's three lines (defensive line, midfield
line, forward line), and then further refining the cluster to assign specific positions to players in a
defensive, defensive midfield, attacking midfield and forward line.

This information can be used by coaches and analysts to better understand their team's
strengths and weaknesses and make informed decisions on how to optimize their tactical decisions. For
example, data can be used to identify areas for improvement in their team's defensive structure and
develop strategies to better protect their goal. Additionally, coaches and analysts can use the
information to analyze the effectiveness of their defensive strategies and make changes as needed to
improve their team's performance on the field.

Overall, the paper provides valuable insights into the importance of defensive tactical analysis in
soccer and how coaches and analysts can utilize data analysis to improve their team's performance. By
incorporating these techniques, a better understanding of a team's defensive performance can be found
and informed decisions can be made to help the team succeed on the field.

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_01.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_02.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_03.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_04.png)![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_05.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_06.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_07.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_08.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_09.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_10.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_11.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_12.png)

![](/images/Organizing%20for%20Success%20%20%20A%20Tactical%20Analysis%20of%20Defensive%20Structure%20in%20Soccer_Page_13.png)


Organizing For Success:        A Tactical analysis of defensive structure in soccer

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A Shiny application allowing users to visualize and compare advanced player statistics from the English Premier League. Various different variables are used to most accurately evaluate players quality and their style of play.

**Abstract:**

The purpose of this app is to provide factual and comparable statistical data to support pre-existing information collected to analyze individuals competing in the English Premier League. This app provides value in that it delivers a more precise statistical understanding of players overall abilities based on data in accordance with preexisting knowledge of players skill. It compares players in various ways while analyzing statistical and visual data that can help give a more comprehensive understanding of the player. Tool can be used as an additional benefit for coaches, players, scouts, general managers, agents,  etc. by providing accurate depictions of players for various areas of decision making. Offers additional statistical information about players in accordance with the “eye test”. Displays what type of player each player is in regard to the system that each team plays. App can be used for selecting players in accordance with both team management and recruitment (finding players of good value). The outcomes of the app include an inhibiting of an understanding that certain statistics are results of players style of play. By being able to clearly identify player styles, best fits can be determined that provide the most value for team and player.  Data was compiled from FBref.com where data was split up into 5 main categories (shooting, passing, dribbling, defending, and possession) to display radar charts and tables based on a scaled value. The shape of the radar chart is meant to show an accurate visualization based on the type of player that is being featured as a result of various different factors.    

***

**How The App Works**

The app is meant to give users an efficient way to visualize player data in a variety of combinable ways that creates an accurate depiction of a player's ability and value.

Radar charts and tables containing the value, a scaled value and a ranking for each statistic are created after selecting a player. There are 6 different sections:

**Overall**

![](/images/Web%20capture\_23-10-2022\_184432\_127.0.0.1.jpeg)

**Shooting**

![](/images/Web%20capture\_23-10-2022\_192214\_127.0.0.1.jpeg)

**Passing**

![](/images/Web%20capture\_23-10-2022\_192347\_127.0.0.1.jpeg)

**Dribbling**

![](/images/Web%20capture\_23-10-2022\_192417\_127.0.0.1.jpeg)

**Defending**

![](/images/Web%20capture\_23-10-2022\_192618\_127.0.0.1.jpeg)

**Possession**

![](/images/Web%20capture\_23-10-2022\_192627\_127.0.0.1.jpeg)

***

**Data adjustments that allow players to be equally analyzed:**

**Per 90 minutes**

Creates all players who play varying minutes onto an equivalent scale.

![](/images/Web%20capture\_23-10-2022\_201013\_127.0.0.1.jpeg)

**Position**

Only compare the selected player with other players that play his position.

![](/images/Web%20capture\_23-10-2022\_201958\_127.0.0.1-26b7723d.jpeg)

**Per 100 Touches**

Compares players based on their stat value for every 100 touches of the ball they take. Shows a player's effectiveness and quality when they are in possession of the ball.

![](/images/Web%20capture\_23-10-2022\_203826\_127.0.0.1-c3599516.jpeg)

Note: Receiving a pass, then dribbling, then sending a pass counts as one touch.

**Scaled and Ranked Values**

Scaled Value: The default for all ratings across the app is the scaled value.

Formula: Scaled Value = (value - min(value))/(max(value)-min(value))

Ranked Value: Takes the ranking from all the players that the player is being compared to and takes the inverse of that fraction

Formula: Ranked Value = 1 - (rank/# of players)

When ranked value selected:

![](/images/Web%20capture\_23-10-2022\_20593\_127.0.0.1.jpeg)

**Adding 2nd Player**

All of the above can be done with the addition of a second player for a side-by-side comparison.

![](/images/Web%20capture\_23-10-2022\_2165\_127.0.0.1-7a50c303.jpeg)

***

**Purpose**

*   To provide factual and comparable statistical data to support pre-existing information collected to analyze individuals competing in the EPL.

**Value**

*   To deliver a more precise statistical understanding of players overall abilities based on data in accordance with preexisting knowledge of players skill.

**Benefits**

*   Offers additional statistical information about players in accordance with the “eye test”.
    *   Displaying what type of player each player is in regard to the system that each team plays.
*   An additional benefit for coaches, scouts, general managers, etc by providing accurate depictions of players for various areas of decision making.

    *   Can be used for selecting players in accordance with both team management and recruitment (finding players of good value)

**Outcomes**

*   Inhibits an understanding that certain statistics are results of players style of play.

*   By being able to clearly identify player styles, best fits can be determined that provide the most value for team and player.

Access to the app can be found at [ https://shanehauck.shinyapps.io/EnglishPremierLeaguePlayerAdvancedStatisticsVisualizer/](https://shanehauck.shinyapps.io/EnglishPremierLeaguePlayerAdvancedStatisticsVisualizer/)



Data compiled for this project was from[ https://fbref.com/en/comps/9/Premier-League-Stats](https://fbref.com/en/comps/9/Premier-League-Stats)
as well as a dataset from [https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset .](https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset)
