It’s a great day in sports analytics! Today, we will be looking at three critical phases of sports analytics: Descriptive, Predictive, and Prescriptive Analytics.
Before we go on, let’s briefly discuss what each of the analytics mean. Descriptive analytics is a method used by analysts to understand what has happened within the field in the past using data aggregation and mining to collect information.
Predictive analytics, on the other hand, uses statistical tools and models to provide insights into future events with the aim of making predictions.
The third, prescriptive analytics, employ the use of algorithms to optimize and simulate data and/or events with the goal of giving possible outcomes of an event.
Coaches and sport analysts employ these tools and integrate the three phases in order to understand past game events, performance of athletes or players, and the performance of a team.
Based on this understanding, they make predictions of future sporting events using predictive analysis, and then by employing the third phase, they utilize the algorithm to give possible outcomes, resulting in better decisions making.
All of these are brilliant ways of improving team performance, as they provide teams with insights, which would not be there by ordinary means of analyzing games. So let’s take a look at the three phases of this method, and how coaches and sports analysts apply them.
Coaches and analysts gather information about their sport and then sort out the performances of each team in their league, as well as the high ranking players. For a football analysis, for instance, it could be the top four teams, players with the most goals, coaching tactics over a period of the season, and so on.
All of these can be grouped into a column season by season to see the variation in these factors. The aggregation of this data will reveal patterns which will highlight the factors that are responsible for wins and for losses.
Predictive analytics require the use of statistical tools and techniques. For this reason, coaches and sports analysts often consult experts who provide them with information on which certain statistical analysis can be carried out in order to make predictions about future events.
Having carried out the descriptive analytics, the factors that are deemed significant for the team or players’ performances are then used as data. This data serves as inputs in various statistical techniques and then enable the analysts to build models, which help them to determine the likelihood of one team performing better than another in future games.
Predictive analytics, as a method of sports analytics, is not only used by coaches and analysts, but also by professionals in the betting world in other to create virtual games.
Prescriptive analytics is similar to the supervised learning method used in sports analytics. This is because it employs the use of algorithms, which are supplied to the machine by the analysts from certain factors that have been deduced from the players or teams.
The machine then optimizes and simulates the data and/or events, and provide the analysts with the possible outcomes. For instance, as we have explained earlier about football analysis and data that are used to reveal past trends in a football season, based on those factors considered, the machine can process the data and provide certain possible outcomes. This helps coaches understand the areas of deficiency in his or her team as well as strengths, and then work accordingly to improve the performance of the team.
In conclusion, these three phases of sport analytics—descriptive, predictive, and prescriptive—are often employed together as a tool in the performance analysis of a player or a team.
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