Revolutionizing Football: Using Machine Learning to Predict Future Performances for Quarterbacks

It is important to have a reliable application that can aid users of betting and fantasy football in which players they should put bet on, or choose for their fantasy teams. My motivation behind this project was to create something that could further betting and fantasy football, and even increase traction.

Football is a largely popular American sport, and within fans of the sport, betting and fantasy football are also very popular. Football is also very unpredictable. On a certain day, a player may perform better because of the weather. On another day, that player may perform poorly because of an injury. Similarly, yearly stats also fluctuate. A quarterback may throw for 5,000 yards one year, and the next year they may throw for 3,000 yards. Due to this, it is important to have a reliable application that can aid users of betting and fantasy football in which players they should put bet on, or choose for their fantasy teams. My motivation behind this project was to create something that could further betting and fantasy football, and even increase traction. My approach to doing this was putting together dataframes of yearly statistics from 75 quarterbacks, and seeing if my linear regression model could predict statistics for a future season. Reliable sources such as the NFL website, or ESPN, were used in order to put these statistics together. The main objective was to produce as accurate as possible stats, which came over time as more seasons were added, and the code was tweaked.

Explore More!

Project Webpage

workspace_premium

Optimizing Prediction Accuracy Using Advanced Ensemble And Voting Classifier Methods

This project observes how various machine learning models, once tuned, can further be combined to create a complex model that uses NFL data from the past 18 years to predict the outcomes of matchups between any two competing teams.

Ashray P.

Mentored by Christopher Mauck

Application of AI to Tennis Match Footage Transcription

One of the best ways for tennis players to improve their game is to record and watch their own match footage, find patterns in the points they win and lose, and practice based on these realizations. However, watching match footage and documenting each point shot by shot is a very time-consuming process. This paper investigates an AI approach to transcribing tennis match footage, combining a deep convolution neural network (YOLOv4), a pose estimation model (Movenet), and a long short- term memory (LSTM) deep neural network. Looking at a transcript of each point will be far more efficient than watching entire match footage for a player to understand how they are losing and winning and analyze patterns in their game. The LSTM model in this project achieved accura- cies of 73.33% and 79.31% when classifying shot type (forehand, forehand volley, forehand slice, backhand, backhand volley, backhand slice, over- head/smash, and serve) for players on the close side and opposite side of the net, respectively, and 55.17% and 60.00% when classifying the di- rection of a shot (cross-court, down the line, down the middle, inside in, inside out, out wide, down the t, and body) for players on the close side and opposite side of the net, respectively.

Marco Y.

Mentored by Eric Bradford

Quantifying the True Trade Value of NBA Players: A Data Driven Approach

This study investigates the drivers of NBA player valuation by analyzing performance metrics, advanced statistics, and salaries from the 2022–2023 season, using regression models to assess how performance relates to trade value. The findings show that while team payroll strongly correlates with team performance, individual player salaries often diverge from statistical output, highlighting the influence of factors beyond on-court performance.

Ali Q.

Mentored by Rami Abi-Akl

About Inspirit AI

Home Team Impact

Programs

Live Online Learn More Apply Now

Join Our Mailing List

Stay up to date with all things Inspirit AI!

Terms and conditions | Refund policy | Privacy policy

Revolutionizing Football: Using Machine Learning to Predict Future Performances for Quarterbacks

Explore More!

Research Paper