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Who Makes the NBA Leap?: Predicting the Rookie Year Performance of NBA First Round Draft Picks

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The NBA Draft has become one of the most exciting and unique events in sports. Draft decisions are so monumental; so crucial to be right, so disastrous to be wrong.

The NBA Draft has become one of the most exciting and unique events in sports. Draft decisions are so monumental; so crucial to be right, so disastrous to be wrong. The purpose of this project is to build a model that would help teams to predict which types of players perform at a high level upon entering the league. By using regression analysis to predict the rookie year PER (performance efficiency rating) as a dependent variable, teams would have some idea of whether their rookies were underperforming, excelling, or performing at a level they could expect. The independent variables and their statistical significance could help answer a host of questions that front offices have about players: If a player came from a worse conference, can we expect them to have a harder time adjusting? Will their shorter wingspan have a negative effect on their play in the NBA? Do guards or forwards tend to have higher PERs upon entering the league? To answer these questions, I've gathered data on every first round NBA draft pick from 2001-2014 who played at least one season of Division 1 NCAA basketball. The data consist of anthropometric measurements (height, wingspan, standing reach, etc.), NBA draft combine results (agility drills, sprint times, etc.) and their college statistics per 40 minutes in their final season of college basketball (points, rebounds, assist-to-turnover ratio, etc.). I then separated the data into seven different sets: aggregate, backcourt, frontcourt, guard, wing, forward, and big. For each of these data sets, I built a predictive model for rookie PER. In doing so, I aimed to gain both a broad understanding of what factors lead to translation of college basketball play to professional play, and also a precise understanding of how those factors change for each distinct position.

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Date Created
  • 2016-05

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Capping the Competition: An Analysis of the NBA's Player Salary Cap

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The NBA operates under a unique system with both forms of the salary cap. The league has a team salary cap that sets a limit that teams can spend on

The NBA operates under a unique system with both forms of the salary cap. The league has a team salary cap that sets a limit that teams can spend on their entire roster. The NBA has a soft cap and a luxury tax system, meaning if teams spend over a determined amount, they are taxed for the salaries in excess. The league also has a player salary cap. The 1999 NBA collective bargaining agreement first introduced the individual player salary cap in the league. This cap sets a limit on what the best players can earn, otherwise known as the maximum contract. In an economic system with a soft team cap, the introduction of the player salary cap has important implications. The stated outcome of such a salary cap is to improve competitive balance and better distribute star players throughout the league. This study evaluated the 1990-2015 regular seasons to measure the impact of the player salary cap on competitive balance, the distribution of team payrolls, and the dispersion of star players. In accordance with the Rottenberg's invariance hypothesis, the player salary cap has hurt the players and benefited the owners by redistributing income from one party to the other, without impacting the distribution of talent in the league. The rule change has not affected competitive balance, while team payrolls have converged and star players have become more dispersed throughout the league. These changes hurt the league overall, preventing the maximization of revenues. Despite this inefficiency, the chance of the league moving to eliminate the player salary cap is low.

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Date Created
  • 2016-12