Sports Informatics and Analytics

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Sports Informatics and Analytics by Mind Map: Sports Informatics and Analytics

1. Pattern Recognition

1.1. Machine Learning

2. Introductions

2.1. Informatik

2.1.1. This is the beginning, the first term used.

2.2. Informatics

2.2.1. Informatics is the computational systems that are seen in everything. Almost everything follows a pattern, some are too big to be seen with naked eye, and some too small. Using technological advances we can use the eyes of a computer to recognise the information available.

2.3. Analytics

2.3.1. Then what do we do with the pattern? In this case we analyse it and see where it leads. Information and statistics lead us on a journey. By revisiting where the journey has gone before what we then do is predict the next stage. This is how we model events that are going to happen in the future, and in my case attempt to predict sporting outcomes.

3. Audience and Messages

3.1. Augmented Information

3.2. Feed Forward

3.2.1. Possibly the coolest idea Keith Lyons ever taught me. Instead of telling someone they executed a sport skill incorrectly, we give them the tools to correct the error (without too much emphasis on the error itself). Next time when conducting the skill, correct technique is on the mind.

4. Performance Measurement

4.1. Player Tracking

4.1.1. Video Tracking

4.1.2. GPS Tracking

4.1.3. Local Positioning Systems

4.2. Data Mining

4.2.1. Websites

5. Pattern Detection

5.1. Machine Learning

5.1.1. Qualitative Prediction

5.1.1.1. Win/Loss

5.1.1.1.1. Logistic Regression

5.1.1.1.2. Random Forrests

5.1.1.2. Categorical Values

5.1.1.2.1. Multiple Logistic Regression

5.1.2. Quantitative Prediction

5.1.2.1. Number Values

5.1.2.1.1. Multiple Linear Regression

5.1.2.1.2. Linear Regression