Assignment #6 Ethical Web Scraping

Table of contents

• Questions
• Cleaning and Wrangling
• Visualizations

Hello! For this assignment, I will be exploring the NFL’s Passing Stats starting in 2017 using data scraped from Pro Football Reference.

Questions

My questions using this data will be finding out who the best passers are and do pass attempts lead to higher numbers of stats. I think the results will be interesting because even in the last 6 years, the game of football has changed and the passing stats should illustrate that.

Cleaning and Wrangling

There is no data cleaning required for this data set. But some wrangling I performed was to only grab Player Name, Age, Team, Games Started, Completions, Attempts, Touchdowns, Interceptions, Passer Rating, and Quarterback Rating.

The next thing I want to do is filter for quarterbacks with over 200 passing attempts. This is to prevent stats that are unusually low or high. Having 200 passing attempts means that the quarterback has played around 8 games or so which is a sufficient sample size for what I’m trying to find.

passing_stats <- passing_stats %>% 
  filter(Att >= 200)

Visualizations

1) Distribution of Touchdowns

ggplot(passing_stats, aes(x = TD)) +
  geom_histogram(bins = 20, fill = "forestgreen", color = "white") +
  labs(title = "Distribution of Touchdowns", 
       x = "Touchdowns", 
       y = "Frequency")

2) Touchdowns vs. Passing Attempts

ggplot(passing_stats, aes(x = Att, y = TD)) +
  geom_point(color = "brown") +
  labs(title = "Touchdowns vs. Passing Attempts", 
       x = "Passing Attempts", 
       y = "Touchdowns")

3) Interceptions vs. Passing Attempts

ggplot(passing_stats, aes(x = Att, y = Int)) +
  geom_point(color = "forestgreen") +
  labs(title = "Interceptions vs. Passing Attempts", 
       x = "Passing Attempts", 
       y = "Interceptions")

4) Top 10 QBs by Touchdown / Interception Ratio

passing_stats <- passing_stats %>%
  mutate(TD_by_INT = ifelse(Int > 0, TD / Int, NA)) %>% 
  arrange(desc(TD_by_INT))

top_10_qbs <- passing_stats %>% 
  head(10)

ggplot(top_10_qbs, aes(x = reorder(Player, TD_by_INT), y = TD_by_INT)) +
  geom_bar(stat = "identity", fill = "brown") +
  coord_flip() +
  labs(title = "Top 10 Quarterbacks by TD/INT Ratio", x = "Player", y = "TD/INT Ratio")

5) Passer Rating by Year

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title: "Assignment #6 Ethical Web Scraping"
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Hello! For this assignment, I will be exploring the NFL's Passing Stats starting in 2017 using data scraped from [Pro Football Reference](https://www.pro-football-reference.com/years/2024/passing.htm).

## Questions

My questions using this data will be finding out who the best passers are and do pass attempts lead to higher numbers of stats. I think the results will be interesting because even in the last 6 years, the game of football has changed and the passing stats should illustrate that.

quarto-executable-code-5450563D

```r
#| include: FALSE
library(readr)
library(tidyverse)
```

quarto-executable-code-5450563D

```r
#| echo: FALSE
#| warning: false
passing_stats <- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/stewartm27_xavier_edu/EYc1MgP2FANPifdTOTQrAuMBVwogP0Jj2SPeAcGC04888A?download=1")
```

## Cleaning and Wrangling

There is no data cleaning required for this data set. But some wrangling I performed was to only grab Player Name, Age, Team, Games Started, Completions, Attempts, Touchdowns, Interceptions, Passer Rating, and Quarterback Rating.

The next thing I want to do is filter for quarterbacks with over 200 passing attempts. This is to prevent stats that are unusually low or high. Having 200 passing attempts means that the quarterback has played around 8 games or so which is a sufficient sample size for what I'm trying to find.

quarto-executable-code-5450563D

```r
passing_stats <- passing_stats %>% 
  filter(Att >= 200)
```

## Visualizations

1\) Distribution of Touchdowns

quarto-executable-code-5450563D

```r
ggplot(passing_stats, aes(x = TD)) +
  geom_histogram(bins = 20, fill = "forestgreen", color = "white") +
  labs(title = "Distribution of Touchdowns", 
       x = "Touchdowns", 
       y = "Frequency")
```

2\) Touchdowns vs. Passing Attempts

quarto-executable-code-5450563D

```r
ggplot(passing_stats, aes(x = Att, y = TD)) +
  geom_point(color = "brown") +
  labs(title = "Touchdowns vs. Passing Attempts", 
       x = "Passing Attempts", 
       y = "Touchdowns")
```

3\) Interceptions vs. Passing Attempts

quarto-executable-code-5450563D

```r
ggplot(passing_stats, aes(x = Att, y = Int)) +
  geom_point(color = "forestgreen") +
  labs(title = "Interceptions vs. Passing Attempts", 
       x = "Passing Attempts", 
       y = "Interceptions")
```

4\) Top 10 QBs by Touchdown / Interception Ratio

quarto-executable-code-5450563D

```r
passing_stats <- passing_stats %>%
  mutate(TD_by_INT = ifelse(Int > 0, TD / Int, NA)) %>% 
  arrange(desc(TD_by_INT))

top_10_qbs <- passing_stats %>% 
  head(10)

ggplot(top_10_qbs, aes(x = reorder(Player, TD_by_INT), y = TD_by_INT)) +
  geom_bar(stat = "identity", fill = "brown") +
  coord_flip() +
  labs(title = "Top 10 Quarterbacks by TD/INT Ratio", x = "Player", y = "TD/INT Ratio")
```

5\) Passer Rating by Year