Sports Data Mining: Predicting Results for the College Football Games
Carson K. Leung,Kyle W. Joseph +1 more
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TLDR
A sports data mining approach is presented, which helps discover interesting knowledge and predict outcomes of sports games such as college football, and makes predictions based on a combination of four different measures on the historical results of the games.About:
This article is published in Procedia Computer Science.The article was published on 2014-01-01 and is currently open access. It has received 75 citations till now.read more
Citations
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Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models
TL;DR: The design and implementation of an interactive visual analytics system, Prospector, that provides interactive partial dependence diagnostics and support for localized inspection allows data scientists to understand how and why specific datapoints are predicted as they are.
Journal ArticleDOI
Game Data Mining: Clustering and Visualization of Online Game Data in Cyber-Physical Worlds
Peter Braun,Alfredo Cuzzocrea,Timothy D. Keding,Carson K. Leung,Adam G.M. Padzor,Dell Sayson +5 more
TL;DR: A data mining algorithm is designed and developed for clustering and visualization of online game data at the cyber-physical world boundary and helps analyze the online game playing data, get insight about the grouping or clusters of players, and offer suggestions to new players of the game.
Journal ArticleDOI
Artificial intelligence for team sports: a survey
TL;DR: Assessing the work in how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making in team sports highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed.
Journal ArticleDOI
Predictive analytics on open big data for supporting smart transportation services.
Paul Patrick F. Balbin,Jackson C.R. Barker,Carson K. Leung,Marvin Tran,Riley P. Wall,Alfredo Cuzzocrea +5 more
TL;DR: This article examines open big data about bus performance (e.g., early, on-time, and late stops).
Journal ArticleDOI
Exploring and modelling team performances of the Kaggle European Soccer database
TL;DR: Role-based indicators of teams’ performance have been built and used to estimate the win probability of the home team with the binomial logistic regression (BLR) model that has been extended including the ELO rating predictor and two random effects due to the hierarchical structure of the dataset.
References
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Journal ArticleDOI
Learning to Track and Identify Players from Broadcast Sports Videos
TL;DR: A system that possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players, and proposes a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip.
Journal ArticleDOI
A compound framework for sports results prediction: A football case study
TL;DR: A framework for sports prediction using Bayesian inference and rule-based reasoning, together with an in-game time-series approach to predict sports matches is proposed, which enables the framework to reflect the tides/flows of a sports match, making predictions certainly more realistic, and somewhat more accurate.
Journal ArticleDOI
Mining constrained frequent itemsets from distributed uncertain data
TL;DR: A data-intensive computer system for tree-based mining of frequent itemsets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data is proposed.
Proceedings Article
Modeling Player Retention in Madden NFL 11
TL;DR: By building an accurate model of player retention, this work is able to identify which gameplay elements are most influential in maintaining active players and make recommendations which will be used to influence the design of future titles in the Madden NFL series.