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ChatGPT- example of Analysis

Updated: Jul 29, 2023

We uploaded an simple excel file ( below ) of the data regarding the number of registered players from a large fictitious club - Club XXX and asked Chat GPT : " Can you analyse the trends from enclosed Excel File of registered football players from Club XXX and highlight the strengths and weaknesses? "





The line plot represents the trends in the number of registered players at Club XXX across different years, split by age group and gender. The table provides the change in the number of registered players from 2022 to 2023 for each age group and gender. Here are the significant trends:

  1. For both genders and all age groups, the number of registered players shows some fluctuations between 2022 and 2023.

  2. The 'U9' and 'U10' age groups for both genders, and the 'U11' age group for males, show an increase in the number of registered players from 2022 to 2023.

  3. The 'U11' age group for females, the 'U12' and 'U13' age groups for both genders, and the 'U14' age group for males show a decrease in the number of registered players from 2022 to 2023.

  4. The 'U14' age group for females shows a slight decrease in the number of registered players from 2022 to 2023.

  5. The 'U13' age group shows new registrations for female players in 2023 that were not present in 2022.

These trends indicate that Club XXX has a dynamic player base with changes in registration numbers across different age groups and genders from year to year. The club is successful in attracting new registrations in the younger age groups ('U9' and 'U10'), but there are drops in player numbers in some of the older age groups ('U12' to 'U14'). This could suggest areas for focus in terms of player retention and recruitment. What would you analyse to be the TWO major Strengths and TWO Major Weaknesses in the registration of players at Club XXX? Based on the provided data and the analysis, here are the two major strengths and two major weaknesses for Club XXX: Strengths:

  1. Young Talent Recruitment: The club is effective in attracting young talent, as seen in the increase in registered players in the 'U9' and 'U10' age groups for both genders, and the 'U11' age group for males from 2022 to 2023. This is a major strength as it indicates a solid foundation for future development.

  2. New Female Registrations: The club has managed to attract new registrations in the 'U13' age group for female players in 2023 that were not present in 2022. This shows progress in promoting gender diversity in the club's ranks.

Weaknesses:

  1. Retention in Older Age Groups: The club shows a decrease in the number of registered players in the 'U12' and 'U13' age groups for both genders, and the 'U14' age group for males from 2022 to 2023. This could suggest issues with player retention as they progress to older age groups.

  2. Gender Gap: Despite new registrations, there remains a significant gender gap, with fewer registered female players across all age groups. This indicates a potential area for improvement to promote gender diversity in the club's ranks.

It's important to note that these observations are based on the available data. Further insights could be obtained with additional data, such as player performance metrics, player retention rates, and comparisons with other clubs or national averages.








 
 
 

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FA SOCCER STAR
In1988 after 3 years research the FA Soccer Star tests were  validated by Professor Tim Holt of Southampton University, England following extensive research with over 10,000 children. They had been proven to display over 90% reliability and over 90% validity in distinguishing between different levels of players.The outline of the tests are available and details in PDF form  here Download FA_Soccerstar_challenge

The book of the Programme is still available on Amazon  here . Over 1million children in over 50 countries went on to take the Soccer Star Challenge which involved 6 tests - dribbling, turning,speed slalom,heading, shooting and  running with the ball. It was intended as a ' fun' activity to motivate players to practise in their own time but there was an Talent ID aspect:- 

The Programme of tests produced a final score for the player on a 6 Level rating.Players achieving the highest ( 6 Star ) grading were recommended to their nearest licensed Academy ( previously known as ‘ Centre of Excellence ) Some of the England Full Internationals and England Under 21 players who have undertaken the Soccer Star Challenge and obtained the top six star grading are shown below ( including their age when they achievedthe Soccer Star 6 star rate and the team they went on to play for )

Michael Carrick (Manchester United) 10 years ,Michael Bridges (Leeds United) 11 years ,Matthew Upson (West HamUtd ) 10 years ,10 Jonathan Greening (West Bromwich Albion ) 10 years ,John Curtis Blackburn Rovers 11 years,Matthew Etherington ( West Ham Utd ) 10 years

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