### Sports Analytics Models – Predictive Model Weights

This is a review of the research using predictive model weights conducted by Joel Brooks, Matthew Kerr, and John Guttag. Soccer is the world’s most popular sport, however, its statistics are not as sophisticated as those used in other sports. Players are evaluated using simple stats like number of goals, shots and assists. A new…

### Sports Analytics Methods – Frequency Distribution

Today’s blog takes a look at sports analytics frequency distribution and how this analytics method is applied in sports. Once data has been collected, it needs to be organized in a manner that makes it easy to analyze. The simplest way to do this is by summing up the information and entering the sums into…

### Sports Analytics – Point Distributions and Expected Points

This article focuses on sports analytics point distributions and expected points. One probability distribution that is especially useful in sports statistics is the distribution of the points scored. This distribution analyzes the probability of scoring points in any given situation. In a football game, a team could be on second down at their opponent’s ten-yard…

### Sports Analytics Best Practices

Sports analytics best practices are techniques and methodologies, proven by top teams around the world, to lead to a team’s desired results. In the Sports Analytics Maturity Model, Victor Holman, founder of Agile Sports Analytics, outlines 26 sports analytics best practices that every team with national or global reach should strive to implement. These best practices…

### Sports Analytics Methods – Conditional Probability

Probability is defined as a measure of how often a particular event will take place if the experiment occurs repeatedly. Probability ranges from zero to one. Zero indicates that the event is impossible, and one indicates that the event definitely will occur. The higher the probability the more likely it is that the event will…

### Sports Analytics Methods – Probability

Sports statistics regularly use the theory of probability. It is important to have a good grasp of the concept in order to use the information appropriately and understand the meaning behind it. The starting point in sports probability is an experiment. An experiment is any course of action where the outcome is random. An experiment…

### Sports Analytics – Decision Making Models

This is a review of the thesis written by Jonathan Mills regarding sports analytics decision making in the national basketball association. For years, coaches in the NBA have used basic statistics and experience to evaluate teams and players.  More advanced analytics continue to become more readily available.  The trick is deciphering what these statistics mean…

### 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…