Greyhound Racing Using Support Vector Machines: A Case Study

2010 ◽  
pp. 101-108
Author(s):  
Robert P. Schumaker ◽  
Osama K. Solieman ◽  
Hsinchun Chen
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

Risk of invasion predicted with support vector machines: A case study on northern pike ( Esox Lucius , L.) and bleak ( Alburnus alburnus , L.)

2016 ◽  
Vol 342 ◽  
pp. 123-134 ◽  
Author(s):  
Rafael Muñoz-Mas ◽  
Paolo Vezza ◽  
Juan Diego Alcaraz-Hernández ◽  
Francisco Martínez-Capel
Keyword(s):  
Support Vector Machines ◽  
Northern Pike ◽  
Esox Lucius ◽  
Support Vector ◽  
Pike Esox Lucius ◽  
Vector Machines ◽  
Bleak Alburnus Alburnus

scholarly journals On modeling player fitness in training for team sports with application to professional rugby

2017 ◽  
Vol 12 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Matthew Revie ◽  
Kevin J Wilson ◽  
Rob Holdsworth ◽  
Stuart Yule
Keyword(s):  
Support Vector Machines ◽  
Team Sports ◽  
Professional Sports ◽  
Support Vector ◽  
Performance Models ◽  
Sports Teams ◽  
Vector Machines ◽  
Using Data ◽  
Explicit Measures

It is increasingly important for professional sports teams to monitor player fitness in order to optimize performance. Models have been put forward linking fitness in training to performance in competition but rely on regular measurements of player fitness. As formal tests for measuring player fitness are typically time-consuming and inconvenient, measurements are taken infrequently. As such, it may be challenging to accurately predict performance in competition as player fitness is unknown. Alternatively, other data, such as how the players are feeling, may be measured more regularly. This data, however, may be biased as players may answer the questions differently and these differences may dominate the data. Linear mixed methods and support vector machines were used to estimate player fitness from available covariates at times when explicit measures of fitness were unavailable. Using data provided by a professional rugby club, a case study was used to illustrate the application and value of these models. Both models performed well with R2 values ranging from 60% to 85%, demonstrating that the models largely captured the biases introduced by individual players.

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