The purpose of this report is to highlight how the inception of big data in baseball has changed the way baseball is played and how it affects the choices managers make before, during, and after a game. It was found that big data analytics can allow baseball teams to make more sound and intelligent decisions when making calls during games and signing contracts with free agent and rookie players. The significance of this project and what was found was that teams that adopt the moneyball mentality would be able to perform at much higher levels than before with a much lower budget than other teams. The main conclusion from the report was that the use of data analytics in baseball is a fairly new idea, but if implemented on a larger scale than only a couple of teams, it could greatly change the way baseball is played from a managerial standpoint.
The topic of this review is how big data analysis is used in a predictive model for classifying what pitches are going to be thrown next. Baseball is a pitcher’s game, as they can control the tempo. Pitchers have to decide what type of pitch they want to throw to the batter based on how their statistics compare to that of the batters. They need to know what the batter struggles to hit against, and where in the strike zone they struggle the most. With the introduction of technology into sports, data scientists are sliding headfirst into Major League Baseball. And with the introduction of Statcast in 2015, The MLB has been looking at different ways to use technology in the game. In 2020 alone, the MLB introduce several different types of technologies to keep the fans engaged with the games while not being able to attend them [^3]. In this paper, we will be exploring a predictive model to determine pitches thrown by each pitcher in the MLB. We will be reviewing several predictive models to understand how this can be done with the use of big data.
The National Basketball Association and the deciding factors in understanding how the game should be played in terms of coaching styles, positions of players, and understanding the efficiencies of shooting certain shots is something that is prevalent in why analytics is used. Analytics is a topic space within basketball that has been growing and emerging as something that can make a big difference in the outcomes of gameplay. With the small analytic departments that have been incorporated within teams, results have already started coming in with the teams that use the analytics showing more advantages and dominance over opponents who don’t. We will analyze positions on the court of players and how big data and analytics can further take those positions and their game statistics and transform them into useful strategies against opponents.
The IndyCar Series is the premier level of open-wheel racing in North America. Computing System and Data analytics is critical to the game, both in improving the performance of the team to make it faster and in helping the race control to make it safer. IndyCar ranking prediction is a practical application of time series problems. We will use the LSTM model to analyze the state of the car, and then predict the future ranking of the car. Rank forecasting in car racing is a challenging problem, which is featured with highly complex global dependency among the cars, with uncertainty resulted from existing exogenous factors, and as a sparse data problem. Existing methods, including statistical models, machine learning regression models, and several state-of-the-art deep forecasting models all perform not well on this problem. In this project, we apply deep learning methods to racing telemetry data. And compare deep learning with traditional statistical methods (SVM, XGBoost).
Sports Medicine will be a $7.2 billion dollar industry by 2025. The NBA has a vested interest in predicting performance of players as they return from injury. The authors evaluated datasets available to the public within the 2010 decade to build machine and deep learning models to expect results. The team utilized Gradient Based Regressor, Light GBM, and Keras Deep Learning models. The results showed that the coefficient of determination for the deep learning model was approximately 98.5%. The team recommends future work to predicting individual player performance utilizing the Keras model.
The present research investigates the value of in-game performance metrics for NFL skill position players (i.e., Quarterback, Wide Receiver, Tight End, Running Back and Full Back) in predicting post-season qualification. Utilizing nflscrapR-data that collects all regular season in-game performance metrics between 2009-2018, we are able to analyze the value of each of these in-game metrics by including them in a regression model that explores each variables strength in predicting post-season qualification. We also explore a comparative analysis between two time periods in the NFL (2009-2011 vs 2016-2018) to see if there is a shift in the critical metrics that predict post-season qualification for NFL teams. Theoretically, this could help inform the debate as to whether there has been a shift in the style of play in the NFL across the previous decade and where those changes may be taking place according to the data. Implications and future research are discussed.
Big data in sports is being used more and more as technology advances and this has a very big impact, especially when it comes to sports gambling. Sports gambling has been around for a while and it is gaining popularity with it being legalized in more places across the world. It is a very lucrative industry and the bookmakers use everything they can to make sure the overall odds are in their favor so they can reduce the risk of paying out to the betters and ensure a steady return. Sports statistics and data is more important than ever for bookmakers to come up with the odds they put out to the public. Odds are no longer just determined by expert analyzers for a specific sport. The compilation of odds uses a lot of historical data about team and player performance and looks at the most intricate details in order to ensure accuracy. Bookmakers spend a lot of money to employ the best statisticians and the best algorithms. There are also many companies that solely focus on sports data analysis, who often work with bookmakers around the world. On the other hand, big data for sports game analysis is also used by gamblers to gain a competitive edge. Many different algorithms have been created by researchers and gamblers to try to beat the bookmakers, some more successful than others. Oftentimes these not only involve examining sports data, but also analysing data from different bookmakers odds in order to determine the best bets to place. Overall, big data is very important in this field and this research paper aims to show the various techniques that are used by different stakeholders.