Sports Performance Overview
The purpose of this document is to provide a snapshot of all the Sports Performance analyses and capture the key objectives.
Overall Objective
The Sports Performance Analysis project aims to deliver comprehensive analysis in various sports, leveraging both real-time and historical data. This project encompasses two main areas: predictive analytics and data visualisation.
In predictive analytics, the project aims to employ data analysis and machine learning techniques to forecast outcomes in diverse sports disciplines. This approach is designed to offer deep insights, aiding in the enhancement of both individual athletes and team performances. By analysing patterns and trends, the project aims to provide strategic guidance to optimise training and competitive strategies.
For data visualisations, the project aims to create dynamic and interactive experiences through Power BI. These dashboards will be tailored to present insights in various sports domains, ranging from individual pursuits like cycling to team sports like football and cricket. The projects aim is not to just track performance and progress but also to provide a clear, understandable visual and representation of complex data.
Cycling Analysis
Cycling Performance Analysis
Objective
The objective of the analysis is to provide a comprehensive overview of key performance metrics such as distance covered, power output, cadence, calories expenditure, and heart rate. The output aims to help cyclists, coaches, and fitness enthusiasts to track progress, identify areas for improvement, and optimise training strategies.
Data Sources
The data used for this analysis was internally generated by Redback Operation’s SunCycle Smart Bike Project. Please refer to the projects repo for the data used in analysis.
20km Time Trial Analysis
The objective of the analysis is to understand the relationships between cadence, heart rate and power output under different conditions, and to determine the consistency of cyclist responses during a 20km distance. The analysis seeks to identify patterns and correlations that could help in optimising training strategies and improve cycling performance. Externally sourced data was used to create a benchmark for athlete comparison across similar distances.
Data Sources
The data used for this analysis was internally generated by Redback Operation’s SunCycle Smart Bike Project. Please refer to the projects GitHub repository for the data used in analysis:
Additionally, externally sourced data was for benchmarking which has been loaded into the project’s GitHub repository.
The data was originally sourced from Mendeley Data.
Borg, David; Osborne, John (2019), “20 km time trial cycling performance with and without task-specific feedback”, Mendeley Data, V3, doi: 10.17632/zxrdvwp6yr.3
Football Analysis
English Premier League (EPL) Team Fixture Prediction
Objective
The objective of the model is to forecast the outcomes of EPL games based on historical data. The goal is to analyse the data to identify patterns and factors that most significantly influence game outcomes, enabling accurate predictions for future matches.
The model aims to provide invaluable information for team strategy development and fan engagement.
Data Sources
Please refer to the Project's repo for the data used in the analysis:
The data was originally sourced from http://Football-data.co.uk :
Football-data.co.uk (n.d.). England Football Results Betting Odds | Premiership Results & Betting Odds. Available at England Football Results Betting Odds | Premiership Results & Betting Odds (football-data.co.uk). Accessed on 20 November 2023.
Cricket Analysis
Bowling Statistic Analysis
Objective
The primary goal of this analysis is to provide a comprehensive understanding of cricket bowling statistics, focusing on identifying top wicket-takers, bowlers with the best averages, and the most economical bowlers.
T20 World Cup Decision Impact Analysis
Objective
The analysis explores and quantifies the impact of decisions made after winning the toss in T20 World Cup matches.
T20 World Cup Venue Performance Analysis
Objective
The aim is to analyse win ratios associated with each venue, offering insights into how location impacts team performance. A time series analyse aims to trace historical performance patterns at these venues over time. The objective is to provide strategic perspective on decision-making effectiveness at different grounds, crucial for teams in planning and strategising for matches at various venues.
Key Player Performance Metric Analysis
Objective
This analysis concentrates on evaluating performance metrics to identify the top eleven players from the 2023 Cricket World Cup. By analysing key metrics like strike rate and balls faced, it aims to reveal underlying performance patterns. The objective is to assist team management in selecting the most effective players for future tournaments and identify promising young talents for development.
Team of the Tournament Analysis
Objective
The objective is to select the best 11 players for the IPL 2023 team of the tournament, categorising them into specific roles. This analysis aims to serve as a critical tool for understanding player performance across different roles, aiding in recognising the most impactful players of the tournament.
Player Performance Modelling
Objective
This analysis focuses on developing tailored models to forecast key player performances in batting and bowling. The goal is provide an accurate and detailed forecast for player performance, assisting in strategic planning and player selection.