Building a ZIO Application Backend

Topic

Building a REST API using the "Major League Baseball Dataset" from Kaggle.

Dataset Description

The "Major League Baseball Dataset" from Kaggle is a comprehensive collection of data related to Major League Baseball ( MLB) games, players, teams, and statistics. The dataset contains information about game-by-game Elo ratings and forecasts back to 1871. You can visit the Kaggle page for a more detailed description of the dataset.

The dataset is available in CSV format: mlb_elo.csv contains all data: mlb_elo_latest.csv contains data for only the latest season.

Ratings Systems: ELO and MLB Predictions

The dataset includes two ratings systems, ELO and MLB Predictions, which are used to evaluate teams' performance and predict game outcomes:

1. ELO: The ELO rating system is a method for calculating the relative skill levels of teams in two-player games, such as chess. In the context of MLB, the ELO rating system assigns a numerical rating to each team, which reflects their relative strength. The rating is updated based on game outcomes, with teams gaining or losing points depending on the result of the match.

2. MLB Predictions: The MLB Predictions rating system utilizes various statistical models and algorithms to predict game outcomes. It takes into account factors such as team performance, player statistics, historical data, and other relevant factors to generate predictions for upcoming games.

USAGE

ZIO Http: Building HTTP Applications

Resources

Using ZIO and related libraries: Build application backend using Scala 3 and leverage the power of ZIO. Utilizing libraries such as zio-jdbc, zio-streams, zio-json, or zio-http to handle database operations, stream processing, JSON parsing, and HTTP application, respectively.

Database initialization at startup: Implementing a mechanism to initialize the H2 database engine at application startup. Using ZIO for managing the initialization process and setting up the required database schema. To process CSV, you can use the tototoshi/scala-csv library.

See documentation links:

• ZIO Http
• ZIO JDBC
• ZIO Worksheet
• tototoshi/scala-csv
• Using sbt-revolver in workflow. Adding following dependency to project/plugins.sbt:

addSbtPlugin("io.spray" % "sbt-revolver" % "0.10.0")

Adding following dependency to build.sbt :

 enablePlugins(RevolverPlugin)

Usage

Compile, then run project with auto reload thanks to sbt-revolver:

$ sbt run

Method.POST :

$ curl  http://localhost:8080/gameHistory -d {'"team": "SDP", "startYear": "2019", "endYear": "2021"'}

Method.GET for static :

Test a query with: ( Response text "Hello World!" )

$ curl http://localhost:8080/text 

Test a query with: ( Response json {"greetings": "Hello World!"} )

$ curl http://localhost:8080/json

Method.GET for endpoints :

Prediction of the last game to be played . ATL(HomeTeam) / NYM (AwayTeam) :

$ curl http://localhost:8080/predict/game/ATL/NYM

Last played game of ATL(HomeTeam) and NYM (AwayTeam) :

$ curl http://localhost:8080/history/game/ATL/NYM