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My submission to Stathletes 2022 Big Data Cup where tracking and play-by-play data from the women's hockey games during the 2022 Beijing Olympics was examined to develop a strategical analysis of defensive shape and offensive possession tactics.

![](/images/forma-a0a7f6e5.png)![](/images/forma-de7359a3.png)![](/images/forma-1bbdbcf4.jpg)![](/images/forma-f2b97707.jpg)![](/images/forma-1dbf7cc1.jpg)![](/images/forma-33292d0a.jpg)

















**References**

Boyle, C. (2019). The
Most Important Line on the Ice you’ve Never Heard of: The “Royal Road” and why
it leads to more goals. *Omha*. <https://www.omha.net/news_article/show/486107-the-most-important-line-on-the-ice-you-ve-never-heard-of> 

Krzywicki, K. (2005).
Shot Quality Model: A logistic regression approach to assessing NHL shots on
goal. *Hockey Analytics. *<http://www.hockeyanalytics.com/Research_files/Shot_Quality_Krzywicki.pdf>

Krzywicki, K. (2010). NHL
Shot Quality 2009-10: A look at shot angles and rebounds. *Hockey Analytics. *[*http://hockeyanalytics.com/Research_files/SQ-RS0910-Krzywicki.pdf*](http://hockeyanalytics.com/Research_files/SQ-RS0910-Krzywicki.pdf)

Ryder, A. (2004). Shot
Quality: A Methodology for the Study of the Quality of a Hockey Team’s Shots
Allowed. *Hockey Analytics. *<http://hockeyanalytics.com/Research_files/Shot_Quality.pdf>


If you have questions, want to know more, or want access to the app, feel free to reach out!


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)


ENGLISH PREMIER LEAGUE ADVANCED PLAYER STATISTIC VISULAIZER

If you have any questions or would like to know more please feel free to reach out!!!



A research paper using FIFA data to explore the physiological benefits that shorter soccer players gain in regard to their taller counterparts.

![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_01.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_02.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_03.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_04.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_05.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_06.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_07-b219bc38.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_08-a71293dc.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_09.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_10.jpg)![](/images/Intro%20Kinesiology%20Final%20Paper_Page\_11.jpg)
