Sports Analytics as a Student in College

.title[
# Sports Analytics as a Student in College
]
.author[
### Tej Seth
]
.institute[
### SPT 313
]
.date[
### Feburary 24th, 2023
]

---

## Introduction

- My name is __Tej Seth__ 
  - Currently a Senior at the University of Michigan
  - Majoring in Information Analysis and minoring in Applied Statistics
  - Can be found at [@tejfbanalytics](https://twitter.com/tejfbanalytics)
  - Previous Experience Focus: PFF, Michigan Football
  - Now: Data Scientist at Sumer Sports

---

## My Sports Analytics Journey

- Whenever I got a baseball card as a kid I always looked at the stats on the back
- Was always a big sports fan, especially of Detroit sports
- High School 
  - Copy and pasted data from Pro Football Reference
  - Made scatter plots on google sheets (would not recommend)
- College
  - Joined a football analytics club
  - Took my first coding class in the winter (in C++)

---

## Teaching Myself How to Code

- Started coding as a freshman using R
- Wanted to recreate the graphs [@benbbaldwin](https://twitter.com/benbbaldwin) was posting on Twitter
- It took weeks but eventually everything clicks
- The lines of code that changed everything:

```r
# install.packages("nflfastR")
library(nflfastR)
library(tidyverse)
pbp <- load_pbp(2019)
pbp %>% select(posteam, defteam, down, ydstogo, yards_gained) %>% head()
```

```
## # A tibble: 6 × 5
##   posteam defteam  down ydstogo yards_gained
##   <chr>   <chr>   <dbl>   <dbl>        <dbl>
## 1 <NA>    <NA>       NA       0           NA
## 2 ATL     MIN        NA       0            0
## 3 ATL     MIN         1      10           -8
## 4 ATL     MIN         2      18            4
## 5 ATL     MIN         3      14           12
## 6 ATL     MIN         4       2            0
```

---

## The Next Steps

- Started posting graphs and analysis on Twitter
  - Received a lot of constructive criticism (and some criticism)
- Started stringing together multiple graphs for long-form articles
  - Enjoyed combining data and analysis in one central area
  
<img src="https://www.nflfastr.com/reference/figures/card.png" style="display: block; margin: auto;" />

---

## Data Viz Over Time (Bad Example)

---

## Data Viz Over Time (Good Example)

---

## Getting Your Name Out There

- Posting articles on a website and graphs on Twitter gets attention
  - Especially when you can reply with a graph you made to a bigger account
- Tweets are *fine* but making connections with people in the industry is one of the best things to do
  - Linkedin
  - Email
  - Twitter Direct Messages
- **Bottom Line: Doing good public work will get you noticed**

---

## My Journey to Pro Football Focus
  1. Got in touch with a current intern there through Twitter DM's
  2. He gave me Eric Eager's email (the Director of Research & Development)
  3. Eric had seen my tweet and articles and knew I liked football and data
  4. Applied to the link that he sent me and told him I applied
  5. Had an interview with him + a screening with HR
  
<img src="https://csva.com/wp-content/uploads/2017/03/PFF-logo.png" width="500" height="200" style="display: block; margin: auto;" />
  
---

## Balancing School and Sports Analytics

- School usually came first because there was a timeline
- Having "weekend" projects to look forward to made schoolwork go by faster
- Doing sports analytics doesn't feel like work because it's sports!

<img src="https://media.giphy.com/media/78yQCCyWAGiSpN5qVS/giphy.gif" width="600" height="350" style="display: block; margin: auto;" />
  
---

## A Process for Generating New Ideas

- Watching a lot of football helps conjure up ideas!
- Look into other sports and see how they are measuring performance
- Start with early data analysis (EDA) on the topic
- Make a simple model to see if the process is there
- Go to an advanced model if needed

```r
library(nflfastR)
library(tidyverse)
library(ranger)

pbp <- load_pbp(2022)
pbp_pass <- pbp %>%
  filter(pass == 1, !is.na(air_yards))

lm_air_yards <- lm(air_yards ~ as.factor(down) + ydstogo + yardline_100, 
                   data = pbp_pass)

rf_air_yards <- ranger(air_yards ~ down + ydstogo + yardline_100,
                       data = pbp_pass)
```
  
---

## Resources & Competitions for Sports Analytics Students

- Tutorials
  - Introduction to nflfastR: https://tinyurl.com/kutztown-nflfastr
  - Youtube: https://tinyurl.com/kutztown-youtube
  - Open Source Football: https://www.opensourcefootball.com/
- Competitions
  - The [Big Data Bowl](https://www.kaggle.com/c/nfl-big-data-bowl-2022)
  - Sports Info Solutions [Football Analytics Challenge](https://github.com/SportsInfoSolutions/AnalyticsChallenge2020)
  - The [Big Data Cup](https://www.stathletes.com/big-data-cup/)
  
<img src="https://operations.nfl.com/media/4213/2020-ploenzke-podium.jpg?mode=max&width=995" width="425" height="200" style="display: block; margin: auto;" />
  
  
---

## The Future of Sports Analytics

- It will be run by students like us!
- Even if you don't decide to go into sports analytics, being able to use tools
like R and/or Python to analyze, visualize and model data is an insanely useful skill
to have
- Analytics in the Pro's
  - the __MLB__ had their "Moneyball" takeover more than a decade ago
  - the __NBA__ just had their "Midrange" revolution
  - the __NFL__ is next as teams start to use tracking data to their advantage
  
<img src="https://waydev.co/wp-content/uploads/2020/01/moneyball-git-analytics.jpg" width="550" height="300" style="display: block; margin: auto;" />

---

## Conclusion

- Always feel free to reach out to me at:
  - Twitter: [@tejfbanalytics](https://twitter.com/tejfbanalytics)
  - Email: tejseth@umich.edu
  - Linkedin: [Tej Seth](https://www.linkedin.com/in/tejseth/)
- Thank you so much for listening to my presentation
- Feel free to ask any questions in the zoom chat or over the mic