The mathematical genii apply their Statistical Wizardry to Basketball
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Situation The manager of a professional basketball team is having a tough decision in choosing which of his two top scorers this season are better at free-throw shots. The final decision will go towards picking the team for Saturday's Cup Final match. On a training session one week before the match the coach decides to "go all out" and bring some mathematical genii in to model a situation where Lee Grimes and Dominic Aspbury, the goalscorers, will shoot at the basketball net. The mathematical genii are students from Cambridge and are benefiting from this opportunity in that they will be able to show evidence of coursework for their final exam. Their coursework will be using their abilities to collect data and "test the appropriateness of a probability model" on a real situation whilst the coach's aim will be to pick the better of the two players for the "big game." If the random variables X and Y count the number of independent trials before the event, having a probability p, occurs then X and Y have geometric distributions: P ( X = r ) = q r - 1 p where r = 1,2,3,...... X~G ( p ) and Y~G ( p ) I will define X as being the number of shots required before Lee shoots a basket. Therefore, Y is defined as the number of shots required before Dom shoots a basket. I will be attempting to see if X and Y have geometric distributions by taking samples of X and Y. The populations are the infinite range of shots capable from the two throwers taken in a discrete time period under varied conditions at the same level of skill. This is impossible to create so the coursework will have to involve sampling, therefore not producing results representative of the whole population. For this coursework I can not take random samples because it will not be possible to recreate due to the infinite choices of shot which could occur e.g.
* Alternatively, if the value lies outside the critical region, the result is valid and there is a larger possibility of the value being what it is. The model is assumed to be correct and the model is accepted. Conclusion would be to state that the statistical model is appropriate to the situation and the assumptions are correct. In the tables, the expected and observed frequencies were calculated but how close together are the values? The closer the observed value to the expected value the more accurate the geometric model will be. The goodness of fit statistic is: where O = Observed Frequency E = Expected Frequency To find the best measure of goodness of fit, add up all values for each statistic and compare with the 2 probability distribution tables. The chi squared test should only be used if the expected frequency of a cell is more than five which means some of the groups are going to have to be combined. This enables the chi-squared distribution to be better approximated. The total frequency of expected frequencies should also be over 50. This makes the chi squared test work at a more accurate level. Lee's chi squared test Using the equation : As we can see by the result = 7 To analyse the result with the chi squared test the number of degrees of freedom have to be established following this procedure: Degrees of Freedom = Number of Cells - Number of Constraints In Lee's table there are seven cells. The number of constraints is two because: o A sample size of eighty is one constraint: The sample has to be eighty. o The probability is another constraint: The mean of the model has to equal the mean of the data so we used the data to work this value out. * Therefore: Degrees of Freedom = 7 - 2 = 5 * at 10% critical level i.e. prob ( ) = 0.9 * but observed value of = 7.478504913 * 7.478......
free-throws made by the performers from the game * Calculate who is most accurate A problem with this is time, as it would take a year to go through just one season, therefore it is impractical and illogical. The physical form of the player should also alter throughout the season so a random sample of more than one season would have to be made. A much better way is to watch all training sessions and take a general overview of who supplies the most points in miniature matches from free throws. This gives more of a view of consistency than "on the day" performance but during game situations the performer will be thinking more logically. A sample of eighty straight baskets is tedious and will affect performance. Modifications * Use a longer time period. The performers were rushed to collect their sample size within two hours as a result of school timetabling and so one of them had to rush his last twenty shots. * Use the same time period i.e. one performer did it one day and the other completed it the next day. Conditions may have been different and morale, energy etc may be variated for both Dom and Lee * Use foot-mats on the floor so that it indicates an exact position for the feet to stand instead of just using the line. This may be an insignificant difference but to improve the coursework it is better than no difference at all. * Using the same basketball. Half way through the sample collection the basketball was lost leaving us the trouble of having to use another basketball - maybe of different weight, age etc and possibly affecting the results Improvements * I would like to calculate confidence intervals for both expected values (X and Y) to determine my degree of confidence in Lee being a better freethrower. * I would also like to be able to see if my result E[X] = E[Y] was statistically significant ?? ?? ?? ?? 11
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