Improving Job Scheduling in GRID Environments with Use of Simple Machine Learning Methods

2009 ◽  
Author(s):  
Daniel Vladušic ◽  
Aleš Cernivec ◽  
Boštjan Slivnik
Keyword(s):  
Machine Learning ◽  
Job Scheduling ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Simple Machine ◽  
Grid Environments

scholarly journals Random forest model identifies serve strength as a key predictor of tennis match outcome

2021 ◽  
pp. 1-8
Author(s):  
Zijian Gao ◽  
Amanda Kowalczyk
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Sports Analytics ◽  
Record Keeping ◽  
Machine Learning Methods ◽  
Simple Machine ◽  
Similar Information ◽  
Betting Odds ◽  
Potential Use ◽  
Tennis Match

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

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