Matching Items (19)

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Attendance Elasticity of Win Percentage in the NBA: An Exploration of the Effects of Team Performance on Home Game Attendance

Description

In the wide world of sports, not all fan bases are created equally—especially in the NBA. Differences in factors like tradition, history, team performance amongst teams make each fan base distinctly unique. This paper will analyze how team performance effects

In the wide world of sports, not all fan bases are created equally—especially in the NBA. Differences in factors like tradition, history, team performance amongst teams make each fan base distinctly unique. This paper will analyze how team performance effects one component of fan behavior: home game attendance. Using win-loss data and home game attendance data for each NBA team from 2001 to 2017, I will construct statistical models to estimate how great of an impact team performance has on each team’s home game attendance. I expect each team’s fan base to respond differently to changes in their team’s win-loss record. This paper will also attempt to quantify other facts that impact attendance at NBA games, including year-to-year changes in team salary expenditures, regional income, and the number of star players playing for the team. Finally, this paper will explore the factors that affect home game attendance for specific games within a given season—things like weather, strength of opponent, and win streaks. Ultimately, the goal of this paper will be to provide NBA business analysts with resources to more precisely anticipate their team’s home game attendance. The ability to understand what motivates the behavior of a fan base is invaluable in creating a marketing strategy that drives fans to the arena. This paper will help to identify teams that are most susceptible to significant fluctuations in attendance and outline alternative strategies to positioning their product offering effectively to fans.

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Created

Date Created
2018-05

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Using Machine Learning to Predict the NBA

Description

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.

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Created

Date Created
2019-05

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The Winning Losers: Combatting the Economic and Competitive Balance Effects of Tanking in the National Basketball Association

Description

In order to establish sustainable parity in competition year over year, all four major professional sports organizations in the United States have established a first-year draft with an order decided or influenced by their Win-Loss record the previous year. The

In order to establish sustainable parity in competition year over year, all four major professional sports organizations in the United States have established a first-year draft with an order decided or influenced by their Win-Loss record the previous year. The assumption is that this draft structure should keep all teams competitive. Rather, there is an overwhelming shift to analytical problem-solving that suggests building a winning team requires a period of losing and collecting young talent. The separation has become so apparent that it has been referred to as, “Twelve teams a-tanking.” (Boras, 2018) The trend was so pronounced this last season that the seven worst teams that held their own pick all lost by more than 15 points in the span of two days.(Sheinin, 2018) This leaves the ratio of games with a 15 point or more point differential to that of less than 15 points was 8:9 on the date described by Sheinin, as opposed to the usual ratio of 2:5 for the rest of the season. This stretch of games occurred during a pivotal time in the season and should have garnered high interest for entertainment as teams grapple for playoff position heading into the post season. Instead, viewers were treated to seven blow-out games. In this thesis, the effects of tanking will be studied as it pertains to the NBA, as a whole, losing attendance in multiple aspects. This applies directly to the value of sponsorships in the NBA. In short, this thesis will answer three of questions; (1) How does expected point spread, which is highly affected by tanking, affect NBA attendance, of all teams, down the stretch of games? (2) How can the NBA protect its sponsors from the effects of tanking? (3) How can NBA sponsors protect themselves from the effects of tanking?

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Date Created
2020-05

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NBA PlayerTrack: A Mobile Application Providing NBA Fans with Statistics, News, and Information about their Favorite Players

Description

Current popular NBA mobile applications do little to provide information about the NBA's players, usually providing limited statistical information or news and completely ignoring players' presence on social media. For fans, especially fans who are unfamiliar with the NBA, finding

Current popular NBA mobile applications do little to provide information about the NBA's players, usually providing limited statistical information or news and completely ignoring players' presence on social media. For fans, especially fans who are unfamiliar with the NBA, finding this information by themselves can be a daunting task, one which requires extensive knowledge about how the NBA provides media related to its players. NBA PlayerTrack has been designed to centralize player information from a variety of media streams, making it easier for fans to learn about and stay up-to-date with players and enabling fan discussion about those players and the NBA in general. By providing a variety of references to the locations of player information, NBA PlayerTrack also serves as a tool for learning about how and where the NBA presents player-related media, allowing fans to more easily locate information they desire as they become more invested in the NBA.

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Date Created
2015-12

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

Description

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

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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Created

Date Created
2016-05

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The Obstacles of Building a Successful NBA Franchise

Description

When I was unsure of what my thesis project would be, the professor of my thesis prep class, Jill Johnson, recommended that I choose a topic that I am passionate about. Immediately, my mind went to basketball and the NBA,

When I was unsure of what my thesis project would be, the professor of my thesis prep class, Jill Johnson, recommended that I choose a topic that I am passionate about. Immediately, my mind went to basketball and the NBA, the business and operations side of things to be specific. Initially, this research paper was going to look into market size and how those teams in a smaller market made their money and ran their teams. It was to focus on some of the more successful franchises that come from smaller markets, as well as those franchises that have been historically unsuccessful. However, the kind of data that I was looking for on market sizes was not very available. So I ended up focusing almost exclusively on the operations side of things. I wanted to see if there was one strategy for building a team that had proven to be more successful than others. I was not sure what sort of answers I would find, but I knew that there had to be some useful data that had yet to be discovered. I settled on researching the success of teams that build primarily using players they drafted versus teams that were built primarily through trades and free agent signings. I also wanted to illuminate the difficulties that front offices, particularly those in smaller markets, face when building a franchise. I chose to focus on things such as the luxury tax and betting on the wrong players. This paper went a lot of different directions before it became what it did. I want to thank all of those who helped me, particularly my director Tim McGuire, my second reader Peter Bhatia and Jill Johnson for helping me get started on the most intimidating, yet rewarding, project that I have ever been a part of.

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Created

Date Created
2015-05

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Are NBA Video Games Representing the Real Game? A Statistical Comparison of Phoenix Suns' Shooting Patterns and their Video Game Counterpart

Description

This paper intends to analyze the Phoenix Suns' shooting patterns in real NBA games, and compare them to the "NBA 2k16" Suns' shooting patterns. Data was collected from the first five Suns' games of the 2015-2016 season and the same

This paper intends to analyze the Phoenix Suns' shooting patterns in real NBA games, and compare them to the "NBA 2k16" Suns' shooting patterns. Data was collected from the first five Suns' games of the 2015-2016 season and the same games played in "NBA 2k16". The findings of this paper indicate that "NBA 2k16" utilizes statistical findings to model their gameplay. It was also determined that "NBA 2k16" modeled the shooting patterns of the Suns in the first five games of the 2015-2016 season very closely. Both, the real Suns' games and the "NBA 2k16" Suns' games, showed a higher probability of success for shots taken in the first eight seconds of the shot clock than the last eight seconds of the shot clock. Similarly, both game types illustrated a trend that the probability of success for a shot increases as a player holds onto a ball longer. This result was not expected for either game type, however, "NBA 2k16" modeled the findings consistent with real Suns' games. The video game modeled the Suns with significantly more passes per possession than the real Suns' games, while they also showed a trend that more passes per possession has a significant effect on the outcome of the shot. This trend was not present in the real Suns' games, however literature supports this finding. Also, "NBA 2k16" did not correctly model the allocation of team shots for each player, however, the differences were found only in bench players. Lastly, "NBA 2k16" did not correctly allocate shots across the seven regions for Eric Bledsoe, however, there was no evidence indicating that the game did not correctly model the allocation of shots for the other starters, as well as the probability of success across the regions.

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Date Created
2016-05

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Critically Analyzing NBA Operations with a Focus on Revenue Sharing Restructuring

Description

The NBA has experienced success because of its ability to adapt and reform business operations to reflect dynamic economic conditions. This critical analysis uses the Collective Bargaining Agreement to explore the NBA operational structure, examine the current state of affairs,

The NBA has experienced success because of its ability to adapt and reform business operations to reflect dynamic economic conditions. This critical analysis uses the Collective Bargaining Agreement to explore the NBA operational structure, examine the current state of affairs, and propose solutions to fundamental issues. Included is an in-depth investigation into correcting team financial reporting and fixing market inequality across the league. Most notably, a proposal to restructure the current revenue sharing system is presented. By progressing the system to correlate winning with team financial performance, there is potential to improve competition and alleviate existing conflict. This will produce a better overall product for the NBA that drives more consumer interest, yields more revenue, and supports stronger international growth opportunity.

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Created

Date Created
2018-05

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Do Fame, Money and Performance follow Altruistic or Narcissistic Playing Styles in the NBA?

Description

The media often portrays professional basketball players as narcissistic, entitled and selfish, but are these portrayals accurate? After all, basketball is a team sport and team sport research indicates that players are more altruistic and selfless. This study proposes a

The media often portrays professional basketball players as narcissistic, entitled and selfish, but are these portrayals accurate? After all, basketball is a team sport and team sport research indicates that players are more altruistic and selfless. This study proposes a way to assess narcissism and altruism through observable behaviors from all the active players in the NBA.

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Created

Date Created
2013-12

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Home Advantage in Sports: The Value it Holds with COVID Restrictions

Description

Home advantage affects the game in almost all team sports across the world. Due to<br/>COVID and all of the precautions being taken to keep games played, more extensive research is able to be conducted about what factors truly go into

Home advantage affects the game in almost all team sports across the world. Due to<br/>COVID and all of the precautions being taken to keep games played, more extensive research is able to be conducted about what factors truly go into creating a home advantage. Some common factors of home advantage include the crowd, facility familiarity, and travel. In the English Premier League, there are no fans allowed at any of the games; furthermore, in the NBA, a bubble was created at one neutral venue with no fans in attendance. Even with the NBA being at a neutral site, there was still a “home team” at every game. The sports betting industry struggled due to failing to shift betting lines in accordance with this decreased home advantage. With these leagues removing some of the factors that are frequently associated with home advantage, analysts are able to better see what the results would be of removing these variables. The purpose of this research is to determine if these adjustments made due to COVID had an impact on the home advantage in different leagues around the world, and if they did, to what extent. Individual game data from the past 10 seasons were used for analysis of both the NBA and the Premier League. The results show that there is a significant difference in win percentage between prior seasons and seasons behind closed doors. In addition to win percentage, many other game statistics see a significant shift as well. Overall, the significance of being the home team disappears in games following the COVID-19 break.

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Date Created
2021-05