nickkeller21 / mlb_stats_viz

MLB Stats Visualization

MLB WAR Analysis and Visualization

Overview

The goal of our project was create a web dashboard that displays career stats for the top 50 WAR leaders in the MLB over the past 5 years.

The inspiration for this project came from FiveThirtyEight's NBA Player Projections dashboard.

What is WAR?

The main stat that we were interested in and are displaying is WAR (Wins Above Replacement). The WAR stat has become increasingly popular and attempts to summarize a player's overall contribution to their team. The calculations that go into the final number are complex but a detailed explaination can be found on the FanGraphs website.

In it's simplest form the WAR number represents how many wins or losses a player will contribute to a team if they were to replace a player with league average stats. So, for example, if a player has a WAR of 4, it can be said that they have helped their team win an additional 4 games than the team would have won if they had replaced that player with an average player.

Data Sources

We ended up needing to use multiple sources for our data.

• Selecting Players: To get the list of players that we would be using we used ESPN's Annual WAR Leaders
• Getting Player ID's: This ended up being one of the bigger challenges we faced. Each organization has their own ID's for players, which weren't neccesarily easy to come by. We were able to find a csv from CrunchTimeBasebll which had player ID's from MLB, ESPN, Fangraphs, Yahoo and BaseballReference.
• Stats: For the majority of our statistics we were able to use the ESPN player ID's to build query urls and pull individual player stats off of each player's ESPN profile page. (Example of Alex Bregman's page)

Cleaning Data

Luckily the data we were able to pull was pretty clean and didn't have many NULL or NA values. Since we used pandas to webscraped we needed to pull the correct DataFrame and we had to drop a few extra columns from each table before working with them to make the data more readable.

One unexpected issue that we ran into was that batters and pitchers had different supplemental stats when we scraped them from the ESPN website (i.e. pitchers do not have stats for batting average or OPS and batters do not have stats for ERA or WHIP) so we needed to separate them before we can fully use the data.

One of the biggest obstacle for us trying to get the data from a DataFrame into a dictionary or json format that would allow us to both upload it into MongoDB and be able to query and manupulate it to render the charts we needed on our webpage.

Database Connection

Because we didn't have a need for a relational database and we would prefer a speedier option, we decided to use MongoDB as our database option. Using MongoDB necessitated the use of mLab to connect the database to our Heroku app.

Website and Javascript

Our website layout is Bootstrap based. The additional JavaScript library that we decided to use was JQuery. It was integrated into the search/dropdown selector that allows you to choose which player you would like to visualize.