Web Dashboard

Project Abstract

We take the preliminary results of Project 2, and we redo some of them so it's quantitative instead of qualitative analysis- and then we slap that into a OpenAPI compliant interface using FastAPI, and produce a half-baked NextJS based dashboard to showcase some of the the graphs as dashboards. Unfortunately due to time constraints, a significant portion of the functionality is still locked behind the API routes only.

Team - Gatorade

Jacob Barkovitch, jbarkov1@binghamton.edu
Guy Ben-Yishai, gbenyis1@binghamton.edu
Alan Bixby, abixby1@binghamton.edu
Jacob Coddington, jcoddin1@binghamton.edu
Ryan Geary, rgeary1@binghamton.edu
Joseph Lieberman, jliebe12@binghamton.edu

Tech-stack

Python 3.8 - The project is developed and tested using python v3.8; packages are managed using poetry, but the pyproject.toml should be fairly package manager agnostic
MongoDB v6.0.2 - An industry staple, web scale, NoSQL database- handles schemaless input and theres less of a concern for accidental SQL injections. MongoDB
python-dotenv - Python-dotenv reads key-value pairs from a .env file and can set them as environment variables. python-dotenv repo
numpy - a fundamental package for scientific computing with Python
matplotlib - a comprehensive library for creating static, animated, and interactive visualizations in Python
seaborn - a Python data visualization library based on matplotlib
fastapi - a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints
uvicorn - an ASGI web server implementation for Python, used to run fastapi
thefuzz - used for fuzzy search of API inputs to prevent the need to memorize exact team names or subreddits
scipy - used for integrating the time series data to find overall sentiment scores across time

Dashboard

NextJS - a flexible React framework that gives you building blocks to create fast web applications
ChartJS - a simple yet flexible JavaScript charting library for the modern web
ChartJSNextJSDashboard Github Template: a Python centric, full stack dashboard template found on YouTube

Minimal webscraping for extra data used, these projects will appear as branches:

NodeJS - a JavaScript runtime
TypeScript - the superior form of JavaScript
FuseJS - a powerful, lightweight fuzzy-search library, with zero dependencies.
Cheerio - fast, flexible & lean implementation of core jQuery designed specifically for the server.
got-scraping - an extended version of Got that automatically changes user-agents, etc, to avoid rate limiting while webscraping

Three data-sources

Our three data sources consist of the same dataset defined in Project 1; however for computational complexity, the focus of our time was put on NFL data. There is heavy overlap between what was implemented in Project 2 and Project3. The following cleaning has been done as outlines:

Extra NFL Data

Team rosters were web-scraped from footballdb.com using Cheerio and got-scraping, implemented in project 2 roster-to-entitlementids
Game scores and durations (start/end times in EST) were fetched from the pro-football-reference.com data set, implemented in nfl-endtimes

Twitter

Teams were designated for tweets by using the Twitter Entitlement IDs that matches the team name or player roster names; matches were done using a fuzzy search with FuseJS, to reduce the amount of missed data from nicknames/name formatting differences; in instances where more than one match was found, the strongest match was used
See teamToEntitlementIds.json for entity ID to team mappings

Reddit

Due to time constraints, only subreddit was used to map comments to a team. Unfortunately this means places like /r/nfl are entirely excluded from our analysis- but it was too complexity to design a ML model, etc that could accurately classify arbitrary text to a given team
See teamToSubreddits.json for subreddit to team mappings

Odds API

Only NFL data is considered; NCAAF while very well covered by the betting odds, had insufficient amounts of filtered player data in the Twitter and Reddit data pipelines to practically use.

How to view the project?

As outlined in Project 1, a Google VM was created for slightly better specs/redundancy during an outage (using free credit!)- the demo was conducted using the Google VM, but by the time this is graded, this should be runnable on the school's provided VM. The only hold up for the demo was the fact that there was insufficient space to migrate the segregated data to the VM; so the analysis was needed to be computed on the full data set (in-place), which there was insufficient space to do.

Binghamton VM

Navigate to the following links:

Dashboard: https://dashboard.ds.bxb.gg
OpenAPI Docs: https://api.ds.bxb.gg

localhost/code level access is the same as School VM listed below

Local Code Access:

ssh prod@***.***.***.*** # (Google VM)
ssh prod@***.***.***.*** # (Binghamton VM)

Code should match on both machines; if not, git pull, with the associated SSH-key, same password as SSH.

School VM

Connect to the Ivanti VPN (formerly Pulse VPN)
In two separate terminals, construct SSH reverse tunnels to the VM on port 3000 (dashboard) and port 5000 (API):
- ssh -L 3000:localhost:3000 -L 5000:localhost:5000 <username>@***.***.***.***
- I personally use SSH Remote on VSCode which automatically handles the remote port forwarding, so YMMV- consult the bxb.gg links otherwise.
From a local browser, access localhost:3000 and localhost:5000/docs
- This is equvialent to https://dashboard.ds.bxb.gg and https://api.ds.bxb.gg, respectively
In the API Docs, expanding a route, clicking Try it out in the top right corner, then filling in the appropriate fields, and hitting Execute will process a request.

From Fresh Install

Clone the repository and navigate into the package.
Initialize a new virtual environment with poetry shell
Install the Python dependencies with poetry install
Run python src/api.py to start the FastAPI webserver
Navigate to dashboard, run npm install to install NodeJS dependencies
Run npm start to start the NextJS webserver for the dashboard

On both the Google VM and Binghamton VM, these are managed via PM2 and will already be running. Running second processes with fail/launch on a different port.

Web Dashboard

Accessible on https://dashboard.ds.bxb.gg (localhost:3000)

A minimal web dashboard using some of the endpoints of the API to generate data using ChartJS; much more functionality is available (exclusively) on the API docs page.

This dashboard was slapped together last minute from this YT Tutorial GitHub template repo.

Clicking elements on the legend can hide/show data sources

OpenAPI Documentation

Accessible on https://api.ds.bxb.gg/docs (localhost:5000/docs)

An API for generating graphs and dataframes of sentiment analysis data for NFL teams on Reddit and Twitter 🏈

General note: Los Angeles Rams are not supported for any Reddit data retrieval; the fact that there are two Los Angeles teams likely caused us to think one was a mistake/duplicate and it was purged from our original subreddit list for data collection.

Path Table

Method	Path	Description
GET	/df/{team_name}/{collection}/{mode}	Generate Dataframe Data
GET	/games	List of NFL Games
GET	/most_positive/{data_source}	Get Most Positive Team
GET	/least_postive/{data_source}	Get Least Positive Team
GET	/positivity_sort/{data_source}	Get Positivity Sort
GET	/odds/odds_ts	Get Bookmaker Odds
GET	/odds/odds_avg	Get Average Bookmaker Odds
GET	/graph/odds	Graph Betting Odds
GET	/graph/team/{team_name}	Graph Team Polarity
GET	/graph/game/{game_id}	Graph Team Vs Team Polarities
GET	/diff/{game_id}	Calculate Game Difference

Path Details

[GET] /df/{team_name}/{collection}/{mode}

Summary

Generate Dataframe Data

Description

Generate a dataframe of data for a given team, data source, and metric; allows for custom time windows
team_name: name of the team; fuzzy search enabled ("buffalo" -> "Buffalo Bills")
collection: data source to use; reddit or twitter
mode: metric to use; sentiment or frequency
focus_datetime: a datetime to center the graph on; generally the game start time
window_before: a timedelta of the amount of time to fetch prior to the focus_datetime
window_after: a timedelta of the amount of time to fetch following the focus_datetime
sample_window: a timedelta template string to be used for the moving average calculation
resample_window: a timedelta template string to be used for the graphing interval (otherwise every point is shown which takes forver to render)
all_data: bool; override the window parameters and just graph everything

Parameters(Path)

team_name: string
collection: 'reddit' | 'twitter'
mode: 'sentiment'

Parameters(Query)

focus_datetime?: string // 2022-11-14T12:00:00+00:00
window_before?: number // 172800 (seconds) 
window_after?: number // 172800 (seconds)
sample_window?: string // 2D
resample_window?: string // 90T
all_data?: boolean // false

Responses

200 Successful Response

application/json

{
	polarity: number[] // [0.08553888081356927, ..., 0.07893374467957985]
	sentiment: number[] // [0.4102488992935106, ..., 0.4113862354514848]
}

[GET] /games

Summary

List of NFL Games

Description

Generate an array of all recorded games

Responses

200 Successful Response

application/json

[
	{
		_id: string // 075dbff0219a3a1100c513400c4796ef
		timestamp: string // 2022-11-04T00:15:00
		endTimestamp: string // "2022-11-04T03:05:00"
		home_team: string // Houston Texans
		home_score: number // 17
		away_team: string // 2022
		winner: string // "Philadelphia Eagles"
		duration: string // "2:50"
	}
]

[GET] /most_positive/{data_source}

Summary

Get Most Positive Team

Description

Return the NFL team with highest positive sentiment
data_source: data source to use; reddit or twitter

Parameters(Path)

data_source: "reddit" | "twitter"

Responses

200 Successful Response

application/json

string // "baltimore-ravens"

[GET] /least_postive/{data_source}

Summary

Get Least Positive Team

Description

Return the NFL team with lowest positive sentiment
data_source: Name of social media site

Parameters(Path)

data_source: "reddit" | "twitter"

Responses

200 Successful Response

application/json

string // "las-vegas-raiders"

[GET] /positivity_sort/{data_source}

Summary

Get Positivity Sort

Description

Generate a dataframe of NFL teams sorted by positivity
data_source: data source to use; reddit or twitter

Parameters(Path)

data_source: "reddit" | "twitter"

Responses

200 Successful Response

application/json

[string, number][] // [["baltimore-ravens", 8.006139505827427], ..., ["las-vegas-raiders", 3.5768940857376483]]

[GET] /odds/odds_ts

Summary

Get Bookmaker Odds

Description

Generate a dataframe of all bookmaker odds for a specific game in timeseries formatting; use /games to fetch an applicable game_id
game_id: game id of desired game; can be provided by /games

Parameters(Query)

game_id: string // 38bf123ec9df3af4efff83e45f472c61

Responses

200 Successful Response

application/json

{
	string:	[{
	  time: string // "2022-11-18 16:59:09.263000"
	  team1_name: string // "Chicago Bears"
	  team1_odds: number // 152
	  team2_name: string // "New York Jets"
	  team2_odds: number // -180
	}]
}

[GET] /odds/odds_avg

Summary

Get Average Bookmaker Odds

Description

Generate a dataframe of average bookmaker odds for a specific game
game_id: game id of desired game; can be provided by /games

Parameters(Query)

game_id: string // 38bf123ec9df3af4efff83e45f472c61

Responses

200 Successful Response

application/json

{
	[key: str]:	{
	  team1_name: string // "Chicago Bears"
	  team1_odds: number // 194.12056737588654
	  team2_name: string // "New York Jets"
	  team2_odds: number // -234.2127659574468
	}
}

[GET] /graph/odds

Summary

Graph Betting Odds

Description

Generate graph of specified bookmaker odds for a desired game
game_id: game id of desired game; can be provided by /games
bookmaker: bookmaker to use; fuzzy searchable (draft -> "DraftKings")

Dotted vertical line indicates winner

Parameters(Query)

game_id: string // 49b82cfa8d543643cf2b5d5109afa7be

bookmaker: string // fanduels

Responses

200 Successful Response

image/png

The vertical dotted line marks the start of the game, the color of the line matches the winner

Notice that odds fluctuate greatly after the dotted line due to mid-game betting

[GET] /graph/team/{team_name}

Summary

Graph Team Polarity

Description

Generate graph of polarity for specified team
team_name: name of the team; fuzzy search enabled ("buffalo" -> "Buffalo Bills")
data_source:data source to use; reddit or twitter
sample_window: a timedelta template string to be used for the moving average calculation
resample_window: a timedelta template string to be used for the graphing interval (otherwise every point is shown which takes forver to render)

Parameters(Path)

team_name: TeamNameLiteral // Philidelphia Eagles

Parameters(Query)

data_source: "reddit" | "twitter" // reddit

sample_window?: string // 3D

resample_window?: string // 3H

Responses

200 Successful Response

image/png

Vertical bars indicate the the start to end time of a game; the color of the bar indicates the outcome.

Green ✅= Win, Red ❌= Loss

[GET] /graph/game/{game_id}

Summary

Graph Team Vs Team Polarities

Description

Generate graph of team VS team polarities
game_id: game id of desired game; can be provided by /games
data_source: data source to use; reddit or twitter

Parameters(Path)

game_id: string // 38bf123ec9df3af4efff83e45f472c61

Parameters(Query)

data_source: "reddit" | "twitter" // reddit

Responses

200 Successful Response

image/png

The vertical bar indicates the start and end of the game; the color of the bar matches the winning team.

Notice how the losing team has a bigger sentiment drop; we attribute the drop in the winning team to general less activity

[GET] /diff/{game_id}

Summary

Calculate Game Difference

Description

Calculate overall difference in sentiment between two teams
game_id: game id of desired game; can be provided by /games
data_source: data source to use; reddit or twitter

Parameters(Path)

game_id: string // 38bf123ec9df3af4efff83e45f472c61

Parameters(Query)

data_source: "reddit" | "twitter" // reddit

Responses

200 Successful Response

application/json

{
	<team_name_1>: number
	<team_name_2>: number
	delta: number
	predicted_winner: <team_name_1> | <team_name_2>
	winner: <team_name_1> | <team_name_2>
	theory_supporting: bool
}

References

#/components/schemas/TeamNameLiteral

[
	"Philadelphia Eagles",
	"Buffalo Bills",
	"Green Bay Packers",
	"Minnesota Vikings",
	"Los Angeles Chargers",
	"Carolina Panthers",
	"Miami Dolphins",
	"Indianapolis Colts",
	"Las Vegas Raiders",
	"Seattle Seahawks",
	"Los Angeles Rams",
	"Tennessee Titans",
	"Baltimore Ravens",
	"Atlanta Falcons",
	"Cleveland Browns",
	"Denver Broncos",
	"Houston Texans",
	"Detroit Lions",
	"Jacksonville Jaguars",
	"New Orleans Saints",
	"Dallas Cowboys",
	"Arizona Cardinals",
	"Washington Commanders",
	"New York Jets",
	"Chicago Bears",
	"Cincinnati Bengals",
	"Kansas City Chiefs",
	"San Francisco 49ers",
	"New York Giants",
	"New England Patriots",
	"Tampa Bay Buccaneers",
	"Pittsburgh Steelers",
]

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
.vscode		.vscode
dashboard		dashboard
src		src
.flake8		.flake8
.gitignore		.gitignore
README.md		README.md
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml
sentiment_bet_results.json		sentiment_bet_results.json

alanbixby/cs415-project3-implementation

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Project Abstract

Team - Gatorade

Tech-stack

Three data-sources

Extra NFL Data

Twitter

Reddit

Odds API

How to view the project?

Binghamton VM

Local Code Access:

School VM

From Fresh Install

Web Dashboard

Accessible on https://dashboard.ds.bxb.gg (localhost:3000)

OpenAPI Documentation

Accessible on https://api.ds.bxb.gg/docs (localhost:5000/docs)

Path Table

Path Details

[GET] /df/{team_name}/{collection}/{mode}

Parameters(Path)

Parameters(Query)

Responses

[GET] /games

Responses

[GET] /most_positive/{data_source}

Parameters(Path)

Responses

[GET] /least_postive/{data_source}

Parameters(Path)

Responses

[GET] /positivity_sort/{data_source}

Parameters(Path)

Responses

[GET] /odds/odds_ts

Parameters(Query)

Responses

[GET] /odds/odds_avg

Parameters(Query)

Responses

[GET] /graph/odds

Parameters(Query)

Responses

[GET] /graph/team/{team_name}

Parameters(Path)

Parameters(Query)

Responses

[GET] /graph/game/{game_id}

Parameters(Path)

Parameters(Query)

Responses

[GET] /diff/{game_id}

Parameters(Path)

Parameters(Query)

Responses

References

#/components/schemas/TeamNameLiteral

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