American League Baseball Championship 2017 Prediction using AHP

Manoj, Misra; Prashant, R.; Parikh, V.; Chaudhary, Ankit

doi:10.1109/ic3iot.2018.8668120

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Manoj et al [ 3 ] collected American League (AL) game data that included four important factors, namely home/away, day/night, ranking, and division, using the Analytic Hierarchy Process (AHP), to predict the 2017 season champion. The result can show the winning probability for each team.…”

Section: Introductionmentioning

confidence: 99%

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Huang

2022

Entropy

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(1) Background and Objective: Major League Baseball (MLB) is one of the most popular international sport events worldwide. Many people are very interest in the related activities, and they are also curious about the outcome of the next game. There are many factors that affect the outcome of a baseball game, and it is very difficult to predict the outcome of the game precisely. At present, relevant research predicts the accuracy of the next game falls between 55% and 62%. (2) Methods: This research collected MLB game data from 2015 to 2019 and organized a total of 30 datasets for each team to predict the outcome of the next game. The prediction method used includes one-dimensional convolutional neural network (1DCNN) and three machine-learning methods, namely an artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). (3) Results: The prediction results show that, among the four prediction models, SVM obtains the highest prediction accuracies of 64.25% and 65.75% without feature selection and with feature selection, respectively; and the best AUCs are 0.6495 and 0.6501, respectively. (4) Conclusions: This study used feature selection and optimized parameter combination to increase the prediction performance to around 65%, which surpasses the prediction accuracies when compared to the state-of-the-art works in the literature.

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Section: Introductionmentioning

confidence: 99%

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Huang

2022

Entropy

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Football Player Analysis for Identifying Best Team using Machine Learning

Aditya Ramnath,

R. Priya

2024

IJARSCT

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In the game of football (soccer), the evaluation of players for transfer, scouting, squad formation and strategic planning is important. However, due to the vast pool of grassroots level player, short career span, differing performance throughout the individual’s career, differing play conditions, positions and varying club budgets, it becomes difficult to identify the individual player's performance value altogether. The Player Performance Prediction system aims at solving this complex problem analytically and involves learning from various attributes and skills of a football player. It considers the skill set values of the football player and predicts the performance value, which depicts the scope of improvement and the capability of the player. The objective of this project is to help the coaches and team management at the grassroots as well as higher levels to identify the future prospects in the game of football without being biased to subjective conditions like club budget, competitiveness in the league, and importance of the player in the team or region. The system is based on a data-driven approach and we train our models to generate an appropriate holistic relationship between the players’ attributes values, market value and performance value to be predicted. These values are dependent on the position that the football player plays in and the skills they possess. In This project best player is predicted by algorithms namely Naïve Bayes (NB) as proposed and K Nearest Neighbor (KNN) as existing system and compared in terms of Accuracy. From the results obtained its proved that proposed NB works better than existing KNN..

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Modelo para la selección de lanzadores de béisbol empleando el Proceso Analítico Jerárquico a través de la evaluación de su desempeño integral

Teppa-Garran,

Fernández-Da Costa

2023

Rev. Ing. Ind. Act. y N. Tend.

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El Proceso Analítico Jerárquico es una herramienta matemática, ampliamente usada en la toma de decisiones, destinada a la detección de una jerarquía global en un conjunto cuyos elementos se conocen relaciones de prioridad dos a dos. La evaluación de lanzadores de béisbol es una componente estratégica fundamental del desempeño de los equipos, la cual se puede plantear en términos de un problema de toma de decisiones multicriterio. En este trabajo se elaboran modelos para clasificar el desempeño de lanzadores de béisbol en la MLB en los roles de abridor y relevista haciendo uso del Proceso Analítico Jerárquico. Los modelos permiten evaluar el desempeño integral del lanzador. Los modelos fueron aplicados en la evaluación de abridores para el premio Cy Young de la temporada 2021 y en la evaluación de relevistas para los premios Trevor Hoffman y Mariano Rivero en la temporada 2017. Igualmente, los modelos permitieron evaluar la trayectoria de un conjunto de abridores emblemáticos venezolanos que realizaron su carrera en la MLB. Los resultados obtenidos fueron satisfactorios y confirman la eficiencia y factibilidad de los modelos propuestos.

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American League Baseball Championship 2017 Prediction using AHP

Cited by 3 publications

References 5 publications

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Football Player Analysis for Identifying Best Team using Machine Learning

Modelo para la selección de lanzadores de béisbol empleando el Proceso Analítico Jerárquico a través de la evaluación de su desempeño integral

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