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Int 2 PE Preparation of the Body Lecture 2 – Data Collection.

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Presentation on theme: "Int 2 PE Preparation of the Body Lecture 2 – Data Collection."— Presentation transcript:

1 Int 2 PE Preparation of the Body Lecture 2 – Data Collection

2 Data Collection It is vital that before we start a programme of improvement, that we measure our initial levels of fitness We can measure our fitness in a number of different ways Observation Schedules Standardised tests Training diaries Knowledge of results

3 Types of Data We are going to focus on Observation schedules to collect our INITIAL DATA Standardised Tests to collect our FOCUSED DATA

4 Data Collection Observation schedules are useful tools for assessing fitness levels as: They provide us with information about our performance related fitness (within a game environment – a match analysis) They are flexible – you build in to your observation schedule the variables you want to measure NOTE OF CAUTION!!! – These do not provide definitive answers : Be careful not to come to the wrong conclusions!

5 Data Collection Try to analyse this observation schedule What does it show about the players cardio-respiratory endurance? When in the game does the players performance drop- off? What was the second half performance like compared to the first? The key is x=unsuccessfull /= successfull

6 Data Collection Time(min s) Contro l PassingDribblingTacklingHeadingShooting 1 st half 0-5 ////////////////x/// 6-10//x///x///xx//x/x//// 11-15/xx//Xx/x/xx//x//Xxx/x/x 16-20/xxXxxXx/xXxx/Xx 2 nd half 0-5 //x/////x/x///////x/x////x 6-10Xx/Xx//x//x/xXx//Xx/x 11-15Xxx//xxXx//Xxx/Xx 16-20XxxXxx/Xx//xxXxx

7 Data Collection Note – this observation schedule was designed to show the drop off or deterioration of skills which result from the on-set of fatigue If the player lacks cardio-respiratory endurance I would expect his skills to deteriorate quicker. As he improves his cardio-respiratory endurance I would expect him to last longer and be able to perform the skills more consistently throughout the match

8 Data Collection Observation schedules are most accurately completed when you use a video camera. This allows you to: Watch the performance over and over again Stop/start the video when you want Slow/speed up the action MOST IMPORTANTLY – Ensures the data you have collected is accurate!

9 Data Collection After coming to the conclusion that there is a problem with one aspect of fitness, we need to get more FOCUSED DATA We can do this by using STANDARDISED TESTS

10 Data Collection Standardised tests allow us to measure our fitness levels in a controlled fashion We can also use them to compare our result to what is the norm for our age group/performance level Standardised test are also very useful as they allow us to complete the test over and over(under the same conditions) to monitor improvements/deterioration of performance

11 Data Collection Why are these methods appropriate? Allow us to focus on our own performance Allow us to look at various aspects of fitness It shows our performance level in a competitive game situation It is in a game against players of a similar ability Data is collected over a significant period of time Creates a permanent record for comparison

12 Data Collection If I did an 8 week training programme for cardio-respiratory endurance, how many tests would I do? Who would complete the tests? How do we ensure all the tests are the same/all the data is valid?

13 Data Collection Answer – 5!! 1 To collect my initial data (observation schedule) 1 to collect my focused data (Standardised test) 1 after 4 weeks of my programme to see if their has been any improvement (repeat of standardised test) 1 at the end of 8 weeks to measure improvements at the end of the programme (repeat of standardised test) 1 more observation schedule to see if improved cardio- respiratory endurance has impacted on my overall performance.

14 Data Collection The same person who monitored the tests in the first instance would monitor the rest – to ensure consistency Standardised tests help ensure validity of data – tests always done under the same conditions with the same variables.






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