To analyse the performances of two batsmen from the Indian cricket team and two batsmen from the Australian cricket team and to predict their probable scores in the forthcoming matches between them.

AIM

To analyse the performances of two batsmen from the Indian cricket team and two batsmen from the Australian cricket team and to predict their probable scores in the forthcoming matches between them. Also to figure out their consistency on the basis of the standard deviation formula.

HYPOTHESIS

The probable score of the batsmen will be somewhere close to their regular averages

For example  Sachin Tendulkar’s career average is around 45 runs per match. This would give an extremely rough estimate of what he would be able to score in Australia. The probability of scoring will be based entirely on the graph and will be similar to the careers of these players.

Procedure

1. The data is tabulated and the frequencies of scoring at every ten run periods

2. Histograms are drawn with respect to the data

3.)        The right hand corners of the histograms are joined to make a pdf( Probability density Function).

4. The probability of scoring the runs is then calculated by measuring the area corresponding to the score.

5. For calculating the consistency, the standard deviation is calculated.

6. This involves calculating the mean and frequency

 VARIABLES

Fitness of the player is a huge variable and can adversely affect the performance. Another variable could be that the playing conditions in Australia are not what have been assumed in this project/

DATA COLLECTION

The highlighted area is the score per match. For example in the data that shows the career of Sachin Tendulkar, on the first page the data would mean

Legend

DNB – Did Not Bat

• -  Not Out ( The batsman could have gone on to score more )

DATA PROCESSING AND PRESENTATION

I.)              Graphs showing the pdf curves of the batsmen

II.)       Calculation of the area under the graphs

3. The standard deviation calculations

I)

1. The first graph shows the statistics of Sachin Tendulkar. The graph how often Sachin has scored runs at 10 run intervals. The graph rises upto 89 initially as that is the number of times Sachin has scored between 0 – 10. In this manner histograms are drawn  for every 10 run interval upto 190. this is because sachins highest score is 186.

2. The second graph shows the statistics of the Australian vice-captain Ricky Ponting.This graph does not reach as high as Sachin’s because ricky ponting has played far less matches than Sachin Tendulkar.

3. The third graph shows the statistics of Matthew Hayden. This graph is even smaller than the other two because his career has been very short

II.)

These calculations show the probability density function of the three graphs. This is the area under the lines joining the top right hand corners of thehistograms.This area are converted in to centimeters.

1.) This is the probability density function of the graph of sachin tendulkar. After adding up all the rectangles and triangles the area comes out to   405.5 cm

2. The area under the graph of Ricky Pontinbg is 159.2 cm

3.) The        area under the graph of Matthew Hayden is 2072.5 cm

The Predicted scores

Sachin Tendulkar

The probability of Sachin scoring between 50-60 is 25.34 and is the highest, therefore the average probale score of sachin is between 50-60

Ricky Ponting

The highest probability is of scoring between 60-70

Matthew Hayden

The highest probability is of scoring between 40-50.

III.)

        These calculations show the standard deviation of each batsman from the mean

1. This calculates the deviation of Sachin Tendulkar  i.e. 35.18
2. This calculates the standard deviation of Ricky Ponting  i.e. 32.23
3. This calculates the standard deviation of Rahul Dravid i.e. 26.9
4. This calculates the standard deviation of Matthew Hayden i.e.  27.8

EVALUATION

This project is only theoretical and cannot be compared to a real life cricket situation. Players are known to play better than their average or less than their average at different situations. For example Sachin Tendulkar plays far better than his average in his Home country India. His average overall is around 45, whereas in India his average is about 70.

On the other hand his averages in the tough conditions of Australia are not worth speaking. Therefore the probabilities are very rough estimates.

The averages have been manipulated a little as not outs have not been taken into consideration. The measurements on the graphs are also not very accurate.

Uncertainty of the scale – 0.05

CONCLUSION

Sachin Tendulkar

AVERAGE – 41.71 runs per match

DEVIATION – 35.18

Rahul Dravid

AVERAGE   -  35.29

DEVIATION – 26.9

Ricky Ponting

AVERAGE   -  37.44

DEVIATION – 32.23

Matthew Hayden

AVERAGE   -  37.36

DEVIATION – 27.8

From the analysis above we can conclude that the most consistent batsman is Rahul Dravid and the least consistent is Sachin Tendulkar. The second most consistent player is Matthew Hayden and third is Ricky Ponting. The following is in the decreasing levels of consistency

Rahul Dravid

Matthew Hayden

Ricky Ponting

Sachin Tendulkar

More consistency does not necessarily mean that the batsman is good. If Dravid is very consistent it only means that he scores very close to his average i.e his scores wont be too less or too much more that his average whereas if Sachin Tendulkar deviates a lot from his average it only means that he will either score too much more or too less than his average scores.

WORD COUNT

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SOURCE OF DATA