Sports, Inc. Volume 9, Issue 1

The ILR Cornell Sports Business Society magazine is a semester publication titled Sports, Inc. This publication serves as a space for our membership to publish and feature in-depth research and well-thought out ideas to advance the world of sport. The magazine can be found in the Office of Student Services and is distributed to alumni who come visit us on campus. Issues are reproduced here with permission of the ILR Cornell Sports Business Society.https://digitalcommons.ilr.cornell.edu/sportsinc/1011/thumbnail.jp

Sports, Inc., Volume 10, Issue 1

The ILR Cornell Sports Business Society magazine is a semester publication titled Sports, Inc. This publication serves as a space for our membership to publish and feature in-depth research and well-thought out ideas to advance the world of sport. The magazine can be found in the Office of Student Services and is distributed to alumni who come visit us on campus. Issues are reproduced here with permission of the ILR Cornell Sports Business Society.https://digitalcommons.ilr.cornell.edu/sportsinc/1013/thumbnail.jp

Commonwealth Times 2017-04-17

https://scholarscompass.vcu.edu/com/2955/thumbnail.jp

Development of value metrics for specific basketball contexts: evaluating player contribution by means of regression

Els clubs de l'NBA inverteixen centenars de milions de dòlars anuals en l'adquisició de jugadors per ajudar-los a guanyar al més alt nivell. Amb decisions tan crucials -i cares- per prendre, el seu apetit per l'avaluació i l'anàlisi dels jugadors ha crescut juntament amb la seva capacitat tècnica per extreure dades del joc, passant del tracking manual a l'òptic durant l'última dècada. En aquest projecte introduirem alguns d'aquests anàlisis pel que fa a determinades facetes del bàsquet, i ho farem utilitzant una combinació de dades de tracking manual (play-by-play) i dades òptiques (tracking de jugadors). En concret, pretenem avaluar la contribució real dels jugadors en accions concretes, en aquest cas, el rebot defensiu. Abordarem aquest problema mitjançant la regressió, i proposarem diferents tècniques per obtenir resultats més precisos, incloent mètodes híbrids que incorporen dades de seguiment del jugador.NBA clubs invest hundreds of millions of dollars yearly in acquiring players to help them win at the highest level. With such pivotal - and expensive - decisions to be made, their appetite for player evaluation and analysis has grown together with their technical ability to extract data from the game - going from manual to optical tracking over the last decade. In this project we will introduce some of these analysis as it respects to certain parts of the basketball game, and will do so using a combination of manually-tracked data (play-by-play) and optical data (player-tracking). In particular, we aim to evaluate the real contribution of players in specific actions, in this case, defensive rebounding. We will approach this problem by means of regression, and propose different techniques to obtain more accurate results, including hybrid methods incorporating player-tracking data

Spartan Daily, September 6, 2017

Volume 149, Issue 6https://scholarworks.sjsu.edu/spartan_daily_2017/1047/thumbnail.jp

Análisis de la similaridad para la toma de decisiones en el Draft de la NBA

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Sistemas Informáticos y Computación, Curso 2019/2020This work is based on the different statistical studies published by Mock Draft Websites and on webs that store the official statistics of the NBA players. The data associated with NBA players and teams are currently very precious since their correct exploitation can materialize in great economic benefits. The objective of this work is to show how data mining can be useful to help the scouts in this real problem. Scouts participating in the Draft could use the information provided by the models to make a better decision that complements their personal experience. This would save time and money since by simply analyzing the results of the models, teams would not have to travel around the world to find players who could be discarded for the choice. In this work, unsupervised grouping techniques are studied to analyze the similarity between players. Databases with statistics of both current and past players are used. Besides, three different clustering techniques are implemented that allow the results to be compared, adding value to the information and facilitating decision- making. The most relevant result is shown at the moment in which the shooting in the NCAA is analyzed using grouping techniques.Este trabajo se basa en los diferentes estudios estadísticos publicados en páginas web de predicción de Drafts de la NBA y en webs que almacenan las estadísticas oficiales de los jugadores de la NBA. Los datos asociados a los jugadores y equipos de la NBA son actualmente muy valiosos ya que su correcta explotación puede materializarse en grandes beneficios económicos. El objetivo de este trabajo es mostrar cómo la minería de datos puede ser útil para ayudar a los entrenadores y directivos de los equipos en la elección de nuevas incorporaciones. Los ojeadores de los equipos que participan en el Draft podrían utilizar la información proporcionada por los modelos para tomar una mejor decisión que la que tomarían valorando su experiencia personal. Esto ahorraría mucho tiempo y dinero ya que, simplemente analizando los resultados de los modelos, los equipos no tendrían que viajar por el mundo para encontrar jugadores que pudieran ser descartados para la elección. En este trabajo se estudian técnicas de agrupación para analizar la similitud entre jugadores. Se utilizan bases de datos con estadísticas de jugadores tanto actuales como del pasado. Además, se implementan tres técnicas de clustering diferentes que permiten comparar los resultados, agregando valor a la información y facilitando la toma de decisiones. El resultado más relevante se muestra en el momento en el que se analiza el tiro en liga universitaria mediante técnicas de agrupación.Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEunpu

A Game-Level Analysis: How Does Trade Affect Team Performance in NBA?

Using game-level panel data on the National Basketball Association for 2015-2016 season, I examine the relationship between trade and team performance. In my study, trade is measured by a game-minute-adjusted salary dispersion. I construct a fixed effect model to analyze the effect of salary dispersion on team performance. The results show that salary dispersion is negatively related to team performance. To verify whether different team characteristics will affect this relationship, I categorize the teams into two groups twice based on their playoff likelihood and number of trades made. The results provide additional evidence that salary dispersion influence team performance negatively. The findings suggest that a compressed salary structures lead to more productivity in the NBA

The Cowl - v.83 - n.15 - Jan 31, 2019

The Cowl - student newspaper of Providence College. Vol. 83 No. 15 January 31, 2019. 24 pages