### Sports Analytics Models – The Boruta Algorithm

This is a review of a thesis applying the Boruta Algorithm to analyze three point shooting by Bradley A. Sliz. Player tracking data in basketball allows analysts to refine more advanced statistics than ever before.  These advanced statistics help teams determine how different strategies affect the outcome of the game.  One strategy that can be…

### Sports Analytics Maturity Model

How Mature is Your Sports Analytics Program? “If you aren’t ahead of the sports analytics game… you’re falling behind.” With sports data increasing at a staggering pace, teams looking for a competitive advantage are realizing the benefits that business intelligence (BI), data discovery, and advanced analytics provide. Whether your team has evolved its analytics strategies…

### Sports Analytics Methods – Subjects and Variables

Sports analytic methods organize and analyze data in order to look for patterns that help in the decision making process. Statistical models are capable of sifting through vast quantities of information in a very short period of time. This is extremely useful in the world of sports as the amount of data collected by the…

### Sports Analytics Methods – Measuring Variation

Variation is the name of the game in sports. No two players are the same and no two teams are the same. How a team plays in one game is different from they perform in another. Understanding these differences and the cause behind them is a main reason for sports statistics. Analysts and coaches are…

### Sports Analytics Methods – Measuring Mean and Median

Mean and median are two of the most useful methods used to summarize numerical data. The mean of a set of data is simply the average, which is found by adding up the total of all the observations and dividing by the number of observations.  The mean is used to describe the average number of…

### Sports Analytics Methods – Prediction Models

This is a review of the sports analytics prediction model research conducted by Stephanie Kovalchik and Machar Reid. Tennis is often considered to be a mental game as it is an individual sport and the majority of the time is spent preparing for the next play. Consequently, coaching is often focused on the players’ mentality.…

### Sports Analytics Methods – eSports Analytics

This is a review of the e-sports analytics research conducted by Matthias Schubert, Anders Drachen, and Tobias Mahlmann. The field of e-sports, computer games played competitively, is growing around the world, with millions of competitors and millions of viewers. As a result, e-sports analytics have emerged, which relate to both sports analytics and digital game…

### Sports Analytics Models – Convolutional Neural Networks

This is a review of the convolutional neural networks research conducted by Paul Power, Jennifer Hobbs, Hector Ruiz, Xinyu Wei, and Patrick Lucey. In the English Premier League, the discrepancy between the larger market and smaller market teams grows consistently wider.  One strategy that the small market team could use to help decrease this disparity…

### Sports Analytics Methods – Win Probability

This is a review of the win probability research conducted by Sujoy Ganguly and Nathan Frank. In the NBA the win probability statistic is based on game time, possession, and point differential in order to predict who the winner of the game will be. There are two main issues with this statistic. One, it does…

### Sports Analytics Methods – Egocentric Ghosting Models

This is a review of the egocentric ghosting model research conducted by Gedas Bertasius, Aaron Chan, and Jianbo Shi. The use of data-driven ghosting models is increasing as player tracking data becomes more widely available.  The issue with the models that have been previously constructed is that their analysis of the players’ decisions is based…