Visión: Desarrollo de un modelo de inteligencia artificial para identificar patrones de visión en equipos esports a nivel profesional, pertenecientes a la League Of Legends European Championship (LEC) de la temporada número 10
| dc.contributor.advisor | Espinosa Carreño, María Alexandra | |
| dc.contributor.advisor | Jurado García, Miguel Eugenio | |
| dc.contributor.apolounab | Espinosa Carreño, María Alexandra [maria-alexandra-espinosa-carreño] | spa |
| dc.contributor.author | Mantilla Molano, Cristian Augusto | |
| dc.contributor.cvlac | Espinosa Carreño, María Alexandra [0001495409] | spa |
| dc.contributor.cvlac | Jurado García, Miguel Eugenio [0001691975] | spa |
| dc.contributor.googlescholar | Jurado García, Miguel Eugenio [es&oi=ao] | spa |
| dc.contributor.googlescholar | Espinosa Carreño, María Alexandra [Ve6S8ocAAAAJ&hl] | spa |
| dc.contributor.orcid | Espinosa Carreño, María Alexandra [0000-0003-1411-0828] | spa |
| dc.contributor.orcid | Jurado García, Miguel Eugenio [0000-0002-2653-249X] | spa |
| dc.contributor.researchgate | Espinosa Carreño, María Alexandra [Maria-Espinosa-C] | spa |
| dc.coverage.campus | UNAB Campus Bucaramanga | spa |
| dc.coverage.spatial | Bucaramanga (Santander, Colombia) | spa |
| dc.coverage.temporal | 2021 | spa |
| dc.date.accessioned | 2022-02-28T13:25:49Z | |
| dc.date.available | 2022-02-28T13:25:49Z | |
| dc.date.issued | 2021 | |
| dc.degree.name | Ingeniero de Sistemas | spa |
| dc.description.abstract | Para inicios del 2015 la firma de estudios de mercado Newzoo estimó un ingreso por deportes electrónicos de 2.22 dólares anuales por cada entusiasta (alrededor de 0.28 billones de dólares), lo cual representa un crecimiento importante del producto interno bruto (PIB) en comparación con los deportes tradicionales indicando el tamaño potencial económico de esta escena competitiva. Para el 2020, a pesar de la pandemia SASR-Cov-2 el crecimiento exponencial se mantuvo llegando a una generación de 7.5 billones de dólares, demostrando una nueva oportunidad de negocio en cualquier región. En el caso puntual de League Of Legends, es un esports transmitido en 19 idiomas, cuyo evento mundial en el 2019 llegó a un pico de espectadores de 200 millones (logrando el doble de audiencia del super bowl edición 2018).Después de todos estos datos, no es raro evaluar las franquicias de organizaciones como Cloud9, Team Solo Mid y Team Liquid en 200 a 300 millones de dólares, superando a clubes de fútbol tan reconocidos como River plate(219 millones de dólares) o Boca juniors (213 millones de dólares). Pese al auge económico en esta escena competitiva, Latinoamérica ha estado imposibilitada de alcanzar un desempeño reconocible en comparación a otros continentes, considerando que parte del problema son las pocas herramientas tecnológicas que cuentan para desarrollar un esquema que le permita competir con otros equipos, en parte por la desarticulación que tiene con el mundo académico. Es por eso que el propósito de este proyecto es generar una herramienta basada en inteligencia artificial, para reconocer los patrones de visión de equipos en continentes con un mejor desempeño a nivel mundial con el fin de ayudar a los equipos latinoamericanos a la toma de decisiones más rápidas y precisas en base a esta información. | spa |
| dc.description.abstractenglish | For the beginning of 2015, the market research firm Newzoo estimated an income from esports of 2.22 dollars per year per enthusiast (around 0.28 billion dollars), which represents a significant growth in gross domestic product (GDP) compared to the traditional sports indicating the potential economic size of this competitive scene. By 2020, despite the SASR-Cov-2 pandemic, exponential growth continued, reaching a generation of 7.5 billion dollars, demonstrating a new business opportunity in any region. In the specific case of League Of Legends, it is an esports broadcast in 19 languages, whose world event in 2019 reached a peak of viewers of 200 million (achieving twice the audience of the 2018 super bowl edition). After all these data , it is not uncommon to value the franchises of organizations such as Cloud9, Team Solo Mid and Team Liquid at 200 to 300 million dollars, surpassing such well-known soccer clubs as River Plate ($219 million) or Boca Juniors ($213 million). ). Despite the economic boom in this competitive scene, Latin America has been unable to achieve a recognizable performance compared to other continents, considering that part of the problem is the few technological tools they have to develop a scheme that allows them to compete with other teams, partly because of the disarticulation it has with the academic world. That is why the purpose of this project is to generate a tool based on artificial intelligence, to recognize the vision patterns of teams in continents with a better performance worldwide in order to help Latin American teams to make better decisions. quickly and accurately based on this information. | spa |
| dc.description.degreelevel | Pregrado | spa |
| dc.description.learningmodality | Modalidad Presencial | spa |
| dc.description.tableofcontents | 1. PLANTEAMIENTO DEL PROBLEMA Y JUSTIFICACIÓN 8 1.1. Planteamiento del problema 8 1.2. Árbol del problema 9 1.3. Justificación 13 2. OBJETIVOS 14 2.1. Objetivo general 14 2.2. Objetivo especifico 14 2.3. Resultados Esperados 15 3. MARCO CONCEPTUAL 16 3.1. E-sports 16 3.1.1. MOBA 16 3.1.2. League of legends 16 3.1.3. League of Legends World Championship 17 3.1.4. Play-in 17 3.1.5. Meta 18 3.2. Inteligencia artificial 18 3.2.1. Machine Learning 18 3.2.2. Aprendizaje supervisado 19 3.2.3. Aprendizaje no supervisado 19 3.2.4. Modelos usados en el estado del arte 19 4. Marco Teórico 23 4.1. Limpieza de datos 23 4.2. Análisis de datos 23 4.2.1. Weight of Evidence (WoE) 23 4.2.2. Information Value (IV) 24 4.2.3. Outlier 25 4.2.4. AutoML 25 4.2.4.1. H2O AI 26 4.3. Métricas de evaluación 26 4.3.1. F1Score 26 4.3.2. Área bajo la curva de precisión- recuperación (AUCPR) 27 5. ESTADO DEL ARTE Y ANTECEDENTES 28 Resumen del estado del arte y antecedentes 32 6. METODOLOGÍA 35 6.1. CRISP-DM 35 6.1.1. Business Understanding: Fase de comprensión del negocio o problema 35 6.1.1.1. RSL DANDELION 35 6.1.1.1.1. Objetivo de investigación 36 6.1.1.1.2. Diseño de protocolos 38 6.1.1.1.3. Criterios de calidad 39 6.1.1.1.4. Extracción de datos y analítica de resultados 40 6.1.2. Data Understanding: Fase de comprensión de los datos 40 6.1.2.1. Obtención de datos 40 6.1.3. Data Preparation: Fase de preparación de los datos 41 6.1.3.1. Limpieza de datos 41 6.1.3.2. Descripción de variables 42 6.1.3.3. Análisis de datos 44 6.1.3.3.1. Análisis de Variables 44 6.1.3.3.2. Análisis de Outliers 51 6.1.3.3.3. Análisis del WoE e IV 53 6.1.4. Modeling: Fase de modelado 54 6.1.4.3. Diseño de modelos 54 6.1.4.3.1. Diseño de modelos con H2O AutoML 55 6.1.4.3.2. Análisis de modelos 55 6.1.5. Evaluation: Fase de evaluación 58 6.1.5.3. Evaluación de modelos 58 6.1.5.3.1. Evaluación de métricas de entrenamiento 59 6.1.5.3.2. Evaluación de métricas con datos reales 59 6.1.6. Deployment: Fase de implementación 61 6.1.6.3.1. Interpretación de la imagen 62 7. CONCLUSIONES 64 | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.instname | instname:Universidad Autónoma de Bucaramanga - UNAB | spa |
| dc.identifier.reponame | reponame:Repositorio Institucional UNAB | spa |
| dc.identifier.repourl | repourl:https://repository.unab.edu.co | spa |
| dc.identifier.uri | http://hdl.handle.net/20.500.12749/15733 | |
| dc.language.iso | spa | spa |
| dc.publisher.faculty | Facultad Ingeniería | spa |
| dc.publisher.grantor | Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.publisher.program | Pregrado Ingeniería de Sistemas | spa |
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| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.creativecommons | Atribución-NoComercial-SinDerivadas 2.5 Colombia | * |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | * |
| dc.subject.keywords | Systems engineer | spa |
| dc.subject.keywords | Technological innovations | spa |
| dc.subject.keywords | League of legends | spa |
| dc.subject.keywords | Developing | spa |
| dc.subject.keywords | Research | spa |
| dc.subject.keywords | Artificial intelligence | spa |
| dc.subject.keywords | Vision patterns | spa |
| dc.subject.keywords | Tournament | spa |
| dc.subject.keywords | Online games | spa |
| dc.subject.keywords | Simulation methods | spa |
| dc.subject.keywords | Machine theory | spa |
| dc.subject.lemb | Ingeniería de sistemas | spa |
| dc.subject.lemb | Innovaciones tecnológicas | spa |
| dc.subject.lemb | Desarrollo | spa |
| dc.subject.lemb | Investigación | spa |
| dc.subject.lemb | Métodos de simulación | spa |
| dc.subject.lemb | Teoría de las máquinas | spa |
| dc.subject.proposal | Inteligencia artificial | spa |
| dc.subject.proposal | Patrones de visión | spa |
| dc.subject.proposal | Torneo | spa |
| dc.subject.proposal | Juegos online | spa |
| dc.title | Visión: Desarrollo de un modelo de inteligencia artificial para identificar patrones de visión en equipos esports a nivel profesional, pertenecientes a la League Of Legends European Championship (LEC) de la temporada número 10 | spa |
| dc.title.translated | Vision: Development of an artificial intelligence model to identify vision patterns in esports teams at a professional level, belonging to the League Of Legends European Championship (LEC) of season number 10 | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | |
| dc.type.driver | info:eu-repo/semantics/bachelorThesis | |
| dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
| dc.type.local | Trabajo de Grado | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/TP |
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