Tennis Ace

Overview

This project contains a series of open-ended requirements which describe the project we’ll be building. There are many possible ways to correctly fulfill all of these requirements.

Project Goals

We will create a linear regression model that predicts the outcome for a tennis player based on their playing habits. By analyzing and modeling the Association of Tennis Professionals (ATP) data, we will determine what it takes to be one of the best tennis players in the world.

Data Description

The ATP men’s tennis dataset includes a wide array of tennis statistics, which are described below:

Identifying Data

• Player: name of the tennis player
• Year: year data was recorded

Service Game Columns (Offensive)

• Aces: number of serves by the player where the receiver does not touch the ball
• DoubleFaults: number of times player missed both first and second serve attempts
• FirstServe: % of first-serve attempts made
• FirstServePointsWon: % of first-serve attempt points won by the player
• SecondServePointsWon: % of second-serve attempt points won by the player
• BreakPointsFaced: number of times where the receiver could have won service game of the player
• BreakPointsSaved: % of the time the player was able to stop the receiver from winning service game when they had the chance
• ServiceGamesPlayed: total number of games where the player served
• ServiceGamesWon: total number of games where the player served and won
• TotalServicePointsWon: % of points in games where the player served that they won

Return Game Columns (Defensive)

• FirstServeReturnPointsWon: % of opponents first-serve points the player was able to win
• SecondServeReturnPointsWon: % of opponents second-serve points the player was able to win
• BreakPointsOpportunities: number of times where the player could have won the service game of the opponent
• BreakPointsConverted: % of the time the player was able to win their opponent’s service game when they had the chance
• ReturnGamesPlayed: total number of games where the player’s opponent served
• ReturnGamesWon: total number of games where the player’s opponent served and the player won
• ReturnPointsWon: total number of points where the player’s opponent served and the player won
• TotalPointsWon: % of points won by the player

Outcomes

• Wins: number of matches won in a year
• Losses: number of matches lost in a year
• Winnings: total winnings in USD($) in a year
• Ranking: ranking at the end of year

Action

• Perform exploratory analysis on the data by plotting different features against the different outcomes.
• Use one feature from the dataset to build a single feature linear regression model on the data.
• Create a few more linear regression models that use one feature to predict one of the outcomes.
• Create a few linear regression models that use two features to predict yearly earnings.
• Create a few linear regression models that use multiple features to predict yearly earnings.

Data Source

The dataset is provided in tennis_stats.csv is data from the men’s professional tennis league, which is called the ATP (Association of Tennis Professionals). Data from the top 1500 ranked players in the ATP over the span of 2009 to 2017 are provided in file. The statistics recorded for each player in each year include service game (offensive) statistics, return game (defensive) statistics and outcomes.