Each of the projects can be a hyperlink to the final project report submitted by the teams.
Predictive modeling in sports involves using data analysis techniques to identify patterns and predict future outcomes. In the context of the 2024 ipl score predictor, a predictive model could be developed to forecast the performance of different teams, individual players, or specific matches
To build a predictive model, data on various factors such as player statistics, team performance, and environmental conditions could be collected and analyzed. Machine learning algorithms could then be used to identify correlations and patterns within the data and to generate predictions about future outcomes.
Cricket Player Performance Analysis Data-driven insights to manage the player's Performance. There are 2 phases in the project: 1. Data based player performance analysis 2. Video based player performance analysis
xG is a new emerging stat in football analytics which helps us find the potential of a player. We use this stat to form a scouting system which fill help teams save valuable transfer budget
A data dashboard that provides player matchup analysis to help teams strategise by running queries and presenting relevant Data.
Fast bowlers being prone to injuries because of fundamental defects in bowling actions.
Match Outcome Prediction: Predicting win probability of a team using historical data analysis leveraging ML Models "Mapping Shooting Patterns in Ice Hockey: A Heatmap Analysis of a Player's Goal-Scoring Areas" : To help the goalie defend better
Analysing the playing style and techniques of badminton or tennis players, with the goal of providing personalized training recommendations and improving their overall performance using Pose estimation and even Image processing for better Results.
Dynamically create and shift the guideline for wide deliveries based on the batsman's position at the time of ball release to remove ambiguity in the wide-ball Decisions.
Using the Monte Carlo method to generate a large number of random samples of the variables in your model, and simulate the performance of different lineups and formations under each sample.
Applying Monte Carlo simulation and machine learning to football data analysis, focusing on predicting match outcomes, optimizing team strategies, and forecasting player growth.
