Wow! We are back again on sport analytics!

Today, we will be looking at another critical approach that is used in sport analytics, and that is SIMULATION.

Coaches, managers, or sport analysts employ simulation method to predict the outcome of events or performances of particular sportspeopleor teams. This allows the managers to predict the chances of his team against opponent in order to enhance the performance of his team through training or formation lineup.

More often for sports analysts who enjoy predicting the chances of a particular team winning a match perhaps for the purpose of giving hints for a bet, simulation allows them to recreate the sport events through statistical models and to foretell the likelihood of the success of a team over a period of time.

This method starts with observation of the team performance over a particular period of time. This observation allows analysts to develop a model for making win-loss predictions, the progression of matches (such as changes in lineups of teams during a season, ball movements as the match progresses pass by pass, defensive and attacking strategy of the teams), and the margin of error during simulation.



Let’s take a look at basketball match and see how coaches or analysts make use of simulation to predict various possibilities during the match, using the professional basketball player Kevin Durant of Oklahoma City Thunder as a case study.

First of all, we consider a game or season as an “experiment”. Then the actual results observed of a player or team over the course of a season will reflect the natural randomness of that player or team which forms the “data set” of the experiment.

For our case study, observing the performance of Oklahoma City Thunder over the period of six seasons, it is possible to build a statistics model which shows the position of the team in each of the six seasons, number of titles won in each of the six seasons, and the strength of the players across the six seasons. By building the model, sport analysts can recreate basketball events through the models built. This model is then simulated for unreal matches which help them predict what will happen in real life matches over the next one or two seasons subsequently after the six seasons which the team’s success is modeled upon.

Let’s a deeper look at how simulation helps make predictions based on the match progression of a team. It is very common to see changes during a match—like formation lineup, change of players during the match, or attack-defense strategy. This kind of changes is a very sensitive thing to do because it plays a key role in the success of a team. So coaches are caught in the habit of making models and simulation to help them determine the performance of their teams against opponent during a match by changing the pattern of the team.

For instance, the manager of Oklahoma City Thunder might discover that taking out Kevin Durant during a match would put the team out of possession, or switching the ball movements when the team is under-performing in a match can change the dynamism of the team and spur the team performance which will eventually increase the team’s chances of winning.

With the progression of a basketball match using a probabilistic graphical model, a coach can predict the best strategy that will produce winnings.


Simulation as a method of sports analytics is widely used by various professionals. This is because it helpscoaches and sport managers adopt the best tactics which will yield the most positive results. Also, in the area of sport betting, simulation helps developers build a virtual sport game based on the real-world performance of the team and players.