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f1-data-analytics.rmd
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---
title: "Analysis of Formula 1 Data"
author: Abhiram Gunti
date: "04/12/2024"
output: ioslides_presentation
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Table of Contents
1. Data Analytics and Visualization
- What is it?
- Why is it important?
2. Intro to Formula 1
- Important concepts about the sport
3. Data Analysis Process
- Data Collection
- Data Cleaning
- Data Analysis
- Data Visualization
## Data Analytics and Visualization
### What is it?
- Raw data --> useful information
- Clean up messy data
- Discover patterns and insights by representing the data in different ways
### Why is it important?
- Proper data analytics can help improve decision making, show hidden trends, and help in forecasting
- In sports, improves team/individual performance, helps in scouting, etc.
## Data Analytics and Visualization (cont)
### Common Tools
- Common tools: R and Python
- Common libraries: Pandas and Matplotlib
<center>
<img src="images/r_logo.png" alt="F1 Logo" width="100" style="padding: 30px; margin-top: 40px;">
<img src="images/python_logo.png" alt="F1 Logo" width="100" style="padding: 30px;">
<img src="images/pandas_logo.png" alt="F1 Logo" width="200" style="padding: 30px; margin-top: 40px;">
<img src="images/matplotlib_logo.png" alt="F1 Logo" width="100" style="padding: 30px;">
</center>
## Intro to Formula 1
<center>
<img src="images/f1_logo.png" alt="F1 Logo" width="200">
</center>
<br>
- Formula 1 is the premiere open-wheel motorsport racing series
- Top 20 drivers in the world compete in ~23 races a year to win the World Drivers Championship title
- 10 teams (2 drivers per team) compete to win the World Constructors Championship title
## Basics and Terminology
- Each race is 300km (~50-70 laps depending on circuit)
- Qualifiying on Saturday sets grid order for Sunday race
- Each team has to adjust their setup and determine a race strategy every race
- Grid, pole position, pit stop, strategy
<center>
<img src="images/mclaren-pit-wall.jpeg" alt="McLaren pit wall analyizng data" width="400">
</center>
## Data in F1
- Macro scale: analyse evolution of sport, performance of teams over their history
- Team win history, top speed of cars over time, etc.
- Per team scale: analyze car performance at each race to tune setup and race strategy
- Downforce, tire temperature, fuel consumption, etc.
<center>
<img src="images/leclerc_data_ex.png" alt="Charles Leclerc's Monza lap data" width="300">
</center>
- Data helps improve car performance from practice to race, and from year to year
## Data Analysis Process
<center>
<img src="images/ds_pipeline.jpg" alt="Data science pipeline" width="400">
</center>
<br>
- Data collection, Data cleaning, Data analysis, Data visualization
## Data Collection
- First Party Data: data collected by you or the organization itself
- Social media followers, feedback forms, sensor data, etc.
- Second Party Data: data collected not directly by you
- Trusted partners, web scraping, etc.
- Third Party Data: data collected from external sources
- Kaggle, Unaffiliated companies, etc.
- Dataset: Formula 1 World Championship (1950-2023)
## Data Cleaning
- Once the data is collected, it needs to be cleaned
- Involves removing any errors, inconsistencies, or duplicates in the data
- It might also make sense to restructure the data as you see fit
<br>
<center>
<img src="images/data_cleaning.png" alt="Data cleaning graphic" width="380">
</center>
## Data Analysis
<center>
<img src="images/data_analytics.jpg" alt="Data analysis graphic" width="380">
</center>
- Explore the data, play around with it
- Using your intuition, place different data together and graph them
- Use statistical methods to discover patterns and trends in the data
## Data Visualization
- Final step of the data analytics process is data visualization
- Involves presenting the data in a visual format to make it easier to understand
- Tell a story with your graphs and visualizations
<br>
<center>
<img src="images/visualization.jpeg" alt="Data visualization graphic" width="400">
</center>
## Resources
- https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020
- https://towardsdatascience.com/a-beginners-guide-to-the-data-science-pipeline-a4904b2d8ad3
- https://www.simplilearn.com/tutorials/data-analytics-tutorial/what-is-data-analytics
- https://blog.hubspot.com/service/first-party-data