FREE ESSAY ON BASKETBALL STATS

BASKETBALL STATS

Executive Summary
We tried to find out how certain basketball statistics affect winning percentage for a
NCAA Division I basketball team. We used the entire NCAA division I 1999-2000 season
statistics. We considered the following statistics: Field Goals, Free Throws, Personal
Fouls, Turnovers, 3 Pointers, Blocks, & Steals.
Our conclusion is that while a rise in each stat had some affect in the rise or fall of
winning percentage, we could not determine a single stat that had a direct affect on the
dependent variable (Winning Percentage). Our results were more effective when we ran the
test on how the combination of all stats affected winning percentage, however, this would
be obvious given the nature of our study.
While this study did not produce the result we wanted, we believe that we could use the
information learned from this study and develop a study that would be more effective. 
Introduction
Coaches are always looking for a better understanding of what makes up a winning team.
This knowledge would help them in recruiting athletes that could improve the team's
statistics in the areas we observed. We took the entire statistical breakdown from the
1999-2000 season and were hoping to find any key statistical areas that could be directly
related to winning percentage.
Methodology
We will run regression analysis on how each independent variable affects the dependent
variable individually and in total. The dependent variable is winning percentage, and the
stats we measured were selected as the independent variables. The independent variables
that we chose were: Field Goals per Game, Free Throws per Game, Assists per Game, Fouls
per Game, Turnovers per Game, 3 Pointers per Game, Blocks per Game, Steals per Game &
Rebounds per Game. 
Our statistics were gathered from www.cbssportsline.com. We will use these statistics to
run a regression analysis to see if any one statistic could be used to predict winning
percentage.
Findings
This section includes the actual statistical calculations. It shows the calculations of
how each statistical variable affected winning percentage individually and how in
combinations the same statistical variables affected winning percentage. 
The data we gathered from our analysis are presented in a formal way on the following
page.
Regression Steals Turnovers Committed Made Field Goals Made Free Throws
Statistics Per Game Per Game Per Game Per Game
Multiple R 0.228547 0.429836 0.510207 0.384329
R Square 0.052234 0.184759 0.260311 0.147709
Adjusted R Square 0.049234 0.182179 0.257970 0.145011
Standard Error 0.177928 0.165020 0.157188 0.168728
Observations 318 318 318 318
Regression Made 3 Pointers Blocked Shots Personal Fouls All Variables 
Statistics Per Game Per Game Per Game Per Game
Multiple R 0.147094 0.366930 0.276749 0.800440
R Square 0.021637 0.134637 0.076590 0.640704
Adjusted R Square 0.018541 0.131899 0.073668 0.632591
Standard Error 0.180778 0.170017 0.175627 0.110607
Observations 318 318 318 318
The findings of listed in these tables show us how a jump in each independent variable
considered individually would affect the dependent variable. The last statistical
category listed show the relationship observed when all independent variable are
considered together. 
Each independent variable considered separately had very low multiple R values which
means that they could not significantly be used to predict the dependent variable of
winning percentage.
As you can see this last study gives us a better Multiple R value and a better R Squared
value as well. We ran the multiple regression analysis eliminating variables that had
lower R values but found no improvement from our original findings.
Conclusions and Recommendations
We were trying to determine what statistic or group of statistics could be used the help
predict a teams winning percentage. Our study does not accomplish what we started out
wanting to find, however, it does provide us with some direction with which to formulate
future studies.
The study does suggest that the independent variables considered as a whole could be used
to predict winning percentage but this is an obvious result. You would expect this result
given the nature of the variables we studied.
It is our opinion that further studies would have to be conducted to achieve the desired
results we were looking for.