This study examined the effect of RAT (a measure of player skills) on wages for professional basketball players in the NBA. The researchers collected panel data for 208 players over the 2012-2013 and 2013-2014 seasons, including variables for wages, RAT, age, height, education, race, and other factors. They estimated a fixed effects model with wages as the dependent variable and RAT and age squared as independent variables. Hypothesis tests found that RAT did not have a statistically significant effect on wages, while age squared did have a statistically significant effect. The researchers hypothesized this may be due to the short time period studied and multi-year contracts limiting wage changes based on short-term RAT changes.

1. THE EFFECT OF RAT ON WAGES FOR PROFESSIONAL BASKETBALL PLAYERS
BY:
NAME EMAIL STUDENT ID
FUNMI M. AJAO fma9@student.le.ac.uk 139020025
XIN CHANG xc53@student.le.ac.uk 129045012
TIMI GABRIEL wtg3@student.le.ac.uk 129048213
ADESEYE LAWAL-SOLARIN als57@student.le.ac.uk 139042627
ANDRE M.WILLIAMS amw41@student.le.ac.uk 119037004
Contents
I. Introduction 2
II. Related Work 2
III. Data 2
IV. Model, Assumptions, and Methods 3
V. Results 5
VI. Validity 6
VII. Conclusion 8
Bibliography 9

2. I. Introduction:
This empirical projectis designed to establish any causal effectof RAT on the wages of professional
basketball playersin the NBA (national basketball association). Our interest in this topic stems from a
passion of sports and specifically basketball,therefore we areinterested in the lifestyles of these
players and consequently how they are ableto financetheir lifestylewith the amount of money they
make. This led us to thinkingabout what impacts the wages of basketball players becausethis has an
effect on their lifestyle.We assumethat there must be a logical explanation for the wage differentials
in Basketball.After extensive research on factors that could be accountfor the wage differential,we
concluded that the differences in wage must arise as a resultof differingskillssetamongst the
players,therefore there must be a set of specific skills thataffecthow good a basketball player is,
such as their defence, offense, assists,block etc.We found a variable,RAT that consists of a players’
field goal percentage, free throw percentage, 3-pointers,totals rebounds, assists,steals and blocks.
All of which we believe impacts greatly on how well a basketballer player performs on the court and
consequently how well he gets paid. We assumethat this works because the higher the RAT (and
variablesthatitconsists of),the higher pay the player is rewarded with. For example, we know that
the higher the amount of blocks a team has in a game increases the chances of that team winningthe
game, therefore if one player consistently has a high number of blocks,this would mean that said
player would be of a higher valueto the team. This leads us to believe that the a player with a higher
valueto the team would be rewarded with higher wages in order to ensure they continueto perform
well as he increasein their wages would actas an incentive for a player to work hard. There’s a
handful of research investigatingwagedeterminants in the labour market and the discussion of
factors that affect wages. There are numerous studies examiningthe wage determination in sports,
and specifically in Basketball,consequently,we aimto establish RAT as a significantdeterminantof
wage in the professional basketball industry. Webelieve variables thatmake RAT solidifies howwell a
basketball player would perform on the court.
II. Related work:
Guis and Johnson’s ‘An Empirical Investigation of Wage in Professional Basketball’examinethe issue
of wage discrimination in the National Basketball Association (NBA) players’s alary.Their approach
was to estimate a log-linear equation and perform a Chow test using salary data fromthe 1996-97
season which they obtained from USA Today. More of the data used was obtained from the Official
NBA Register, Rick Barry’s Pro Basketball Bible (1995) and Pete Setter’s (1996) NBA Draft Report. They
conducted an F statistic testwith a null hypothesis statingthatthere aren’t significantdi fferences
between players of different racial groups.The null hypothesis could notbe rejected; their results
showed that African-American players do not earn less than their White teammates. The difference in
our approach in investigatingtheissueof wage determination in basketball is in terms of method. We
ran an OLS regression and further conducted a panel data, performing a T statistic testto determine a
statistical significanceof our independent variable,RAT on our dependent variable,wage.
Furthermore, the approach we’ve taken in collectingdata for our research is different. We opted to
collectdata ourselves,this is dueto the lack of unique dataset availableto us which led us to take
initiativeand createour own dataset with relevant information for our study. In addition,we sought
to avoid any administrativeproblems thatmight causebias and invalid results if weused data that
has been collected for a different purpose.
III. Data:
We collected data for two seasons 2012/2013 and 2013/2014 asour data is a Panel data,comparing
results from different seasons.We observed our data on 208 players from30 teams in the NBA,
choosingplayers atrandomfrom each team enablingus to get information on players with a valid
representation of the NBA population of 439 basketball players.Thevariables in our data includes
wage; our dependent variable,RAT; our independent variablewhich we believe would have a
significanteffect on wages, race (dummy variabledeterminingwhether or not a player is black),

3. college(dummy variabledetermining whether or not a player attended college); tryingto find any
relationship between a collegeeducation and an increased skillssetwhich would affect RAT and
therefore wage, height; accountingfor an increased chanceof shootingbetter as a taller player is
more likely to find iteasier to reach the basket, age; a players age. age squared; assumingthatwith
age comes experience and therefore better valueto a team, however after a peak age, players might
begin to losesome of their ability dueto injury and other health issues, this is also included because
there would be no significantdifferencein age in both of the seasons,which would reduce the validity
of our panel data and blocks;the more blocks,the less lossesa team face. We believe our variables
are factors that could affect how much a player gets paid,and consequently an appropriatesetof
data to test our research question. We collected all thesevariables with the intent to investigateand
then later decide upon which ones would allowus to test our research question most accurately and
efficiently.
Table above shows a description of our data in stata format
Table above shows a summary statistic of our data in stata format
IV. Model, assumptions and methods:
Our data is of panel data form with two time periods.From a population panel data model
log 𝑊𝑎𝑔𝑒 𝑖𝑡 = 𝛽0 + 𝛽1 𝑅𝐴𝑇𝑖𝑡 + 𝐴𝐺𝐸2
+ 𝛽3 𝑍𝑖 + 𝑢 𝑖𝑡
We derive our fixed effect model which is:
log 𝑊𝑎𝑔𝑒𝑖𝑡 = 𝛽1 𝑅𝐴𝑇 + 𝛽2 𝐴𝐺𝐸2
+ 𝛼𝑖 + 𝑢 𝑖𝑡
Where:
1. log𝑊𝑎𝑔𝑒𝑖𝑡 - Is a measure of wage over two years
BLOCKS double %10.0g BLOCKS
AGE byte %10.0g AGE
HEIGHT int %10.0g HEIGHT
COLLEGE byte %10.0g COLLEGE
RACE byte %10.0g RACE
RAT double %10.0g RAT
WAGE long %10.0g WAGE
YEAR byte %10.0g YEAR
ID int %10.0g I.D
NAME str24 %24s NAME
variable name type format label variable label
storage display value
size: 21,632
vars: 10 24 Mar 2015 13:06
obs: 416
BLOCKS 416 .538101 .518614 0 3
AGE 416 27.3149 4.137326 18 41
HEIGHT 416 201.5337 8.561546 180 216
COLLEGE 416 .7836538 .4122487 0 1
RACE 416 .7115385 .4535924 0 1
RAT 416 19.61034 9.905898 2.22 51.46
WAGE 416 5916204 5275645 44835 3.05e+07
YEAR 416 1.5 .500602 1 2
ID 416 104.5 60.11603 1 208
NAME 0
Variable Obs Mean Std. Dev. Min Max
. sum

4. 2. 𝛽0 - Is the intercept.
3. 𝛽1 𝑅𝐴𝑇𝑖𝑡 - Is our Independent variablewhich measures an approximatelevel of skill for each
player over two years
4. 𝛽2 𝐴𝐺𝐸2
- Is our independent variablewhich measures in quadratic form, the squareof a
player’s age over two years
5. 𝛽3 𝑍𝑖-Are invariableinputs thatinfluences a players wage, such as:
o A player’s work ethic
o The Previous teams a players played for
o A players race
o Whether a player went to collegeor not
6. 𝑢 𝑖𝑡- Reflects inputs that vary over time.
7. 𝛼 = 𝛽0 + 𝛽3 𝑍𝑖-This term is called a statefixed effect.
 We firstassumethere is no exogenity. This means that our error term has a conditional mean zero:
E (Uit|Xit,α)=0.If assumption holds,model can be correctly specified and our within estimator will yield
consistentestimates of B1. The consequence of this assumption is thatour regressor,RAT, and fixed effect
are not correlated with the error term. In our fixed effect model, for our assumption to hold, α needs to
not be correlated with the error term. Therefore αi must somehow be removed. Instead of justomitting
the variablewhich can causeomitted variablebias(OVB),the effect of α can be eliminated by usingour
fixed effect estimator.
 We further assume(RATi1…..RATiT,Ageit
2…..Ageit
2,ui1…...uit),i=1…n,are independent and identically
distributed randomvariables.This simply suggests thateach random variable(each basketball player) has
the same probability distribution as theothers and they are all mutually dependent of each other. We can
be positively sureof this assumption sincewe collected the data ourselves,through a random selection of
players.
 Another assumption we make is thatthere is no perfect multicollinearity.Multicollinearity exists when
two or more independent variables arehighly correlated with each other. i.e. Xi2=Z0+Z1X1i.. Fixed effect
models such as ours often generate largeVIF scores so therefore VIF’s aren’t usually strongindicators.We
ran a simplecorrelation test between RAT and AGE for each year, both showingweak correlation,
especially in year 2,consequently satisfyingour assumptions.
 Finally weassumed that there are no outliers present in our data. In our model, wages ranged from
$44,835-$30,000,000.Age ranged from 18yrs-41yrs,RAT ranged from 2.2-51.46. Although there is
significantvariation between these values,we can safely assumethat wages, age and RAT increased
gradually and no one singlevariablevaried massively fromanother. We also took logs of wage to solve
any outlier problems we may have.
 Overall we assumethat the model is correctly specified,meaningthat it is linear,has no omitted variable
bias and we included all theright variables.
We decided in our fixed effect model to use a within/fixed effect estimator. For the estimator to hold, the
effect of α needs to be removed to yield consistentresults.We therefore construct‘entity demeaned data’
from our variables,which isour data in time period one, minus the average of our data for all thetime periods :
log 𝑊𝑎𝑔𝑒 𝑖𝑡 −
1
𝑇
∑ log 𝑊𝑎𝑔𝑒 𝑖𝑡
𝑇
𝑡=1
= 𝛽1 𝑅𝐴𝑇𝑖𝑡 −
1
𝑇
∑ 𝑅𝐴𝑇𝑖𝑡
𝑇
𝑡=1
+ 𝛽2 𝐴𝐺𝐸2
−
1
𝑇
∑ 𝐴𝐺𝐸𝑖𝑡
2
𝑇
𝑡=1
+ 𝑢 𝑖𝑡 −
1
𝑇
∑ 𝑢 𝑖𝑡
𝑇
𝑡=1
Where

5. 1
𝑇
∑ log 𝑊𝑎𝑔𝑒 𝑖𝑡 = 𝛼𝑖
𝑇
𝑡=1 + 𝛽1
1
𝑇
∑ 𝑅𝐴𝑇𝑖𝑡 +
1
𝑇
𝑇
𝑡=1
∑ 𝐴𝐺𝐸𝑇
𝑡=1 𝑖𝑡
2
+
1
𝑇
∑ 𝑢 𝑖𝑡
𝑇
𝑡=1
The effects of 𝛼𝑖𝑡 has now been removed and these demeaned variables can nowbe estimated usingOLS and
we can concur that any change in wage cannot be caused by X.
log 𝑊𝑎𝑔𝑒̂ = 𝛽1 𝑅𝐴𝑇𝑖𝑡
̂ + 𝛽2 𝐴𝐺𝐸̂𝑖𝑡
2
+ 𝑢 𝑖𝑡
We usethe fixed effect (within estimator) estimator; this is becauseitmeasures the association between
individual-specific deviationsof regression fromtheir time averaged values and individual specific deviationsof
the dependent variablefromits time averaged value.This is done by usingthe variation in our data over the
two year period.
V. Results:
Hypothesis test
H0: 𝛽1̂ = 0 VS 𝛽1̂ H1: ≠ 0
H0: 𝛽2̂ = 0 vs 𝛽2̂ H1: ≠ 0
In order to gauge the statistical significance of each of our beta values we ran a number of statistical
inference tests. We ran a hypothesis test initially. Hypothesis testing is an inferential procedure that uses
sample data to evaluate the credibility of a hypothesis about a population. A null Hypothesis states that
the treatment has no effect. An alternative hypothesis states that the treatment does have an effect.
(Forrest.psych.unc.edu, 2015)
HYPOTHESIS TESTING
In regards to our research we ran tests against the null hypothesis that player’s ratings within the NBA
did not affect their wage. We ran this test at the 5% significance level and we found that our β1 variable
was not statistically significant as its t stat value 1.55 which is less than 1.96 which is the critical 5%
value. Therefore, our findings showed that we could not reject the null hypothesis with regards to this
variable. We believe one major reason as to the statistically insignificance of RAT was due to our limited
time scale, in which we measured the RAT effect, we believe that with a longer time scale, we would have
been able to see a greater impact of RAT on wages, in addition, one important factor could be that a lot of
players are on fixed wage contracts for a number of years, so this would delay the effect of any increase in
RAT mid-season. For example, a dramatic increase in RAT in the first two years for a player who’s signed
a 5 year contract might lead to the board paying close attention to his performance in the third year
before re-negotiating his contract. Hence, it would only be in the fourth year that his salary would raise
and he would only begin to reap rewards for his hard work. Salary re-negotiation would be even harder if
players RAT kept fluctuating. (En.community.dell.com, 2015)
Our second null hypothesis was that slope value for our generated variable age squared was statistically
significant and there for greater than zero. Again we tested the null hypothesis for this variable at the 5%
level. Results showed that the t stat value was 3.0 we were there for able to reject the null hypothesis as
the t stat value was greater than 1.96. We believe that the null hypothesis was rejected for a number of
reasons; one possible reason (En.community.dell.com, 2015) the null hypothesis was rejected could be
because the age squared variable explains a significant amount of the variability in their wage, young
players “ rookies” tend to get plaid less that older players in the NBA. The result matched our
expectations on common sense grounds as a player gets older they become more experienced and as a
result they are paid a higher wage.

6. P-VALUE
We also evaluated the p value for both our β values. With regards to β1 the p value was 0.052 i.e. 5.2%
this value is slightly greater than the critical value of 5%. This finding reaffirmed our earlier finding from
the t stat hypothesis, which has indicated that this variable was not statistically significant. With regards
to our β2 p value we found that this variable had a p value of 0.003 i.e. 0.3% this is well below the 5%
critical value for the p value test. The p value results also correlated with the results of the t stat test for
slope of the age squared variable.
CONFICENCE INTERVALS
(Stats.gla.ac.uk, 2015) We found the confidence interval results quite interesting. Confidence interval
gives an estimated range of values which is likely to include an unknown population parameter, the
estimated range being calculated from a given set of sample data. With regards to our β1 estimated
variable denoted “RAT” we found our lower limitfor the 95% confidence interval was -0.0032548 and
our upper 95% limitwere 0.0271931. These values were quite surprising to use, in particular the lower
limit. The lower limit value was negative which was very surprising to use, because this implies that
potentially a one unit increase in a players rating score could lead to a decrease in their wages. This
finding contradicts logic to some extent, if a players NBA rating increases i.e. they have improved their
skill set in some way since the last season it would not make sense to reduce their pay packet. The upper
limit was positive which matches our initial expectation that an increase in a basketball players rating
would lead to a percentage increase in their wage. With regards to our second independent variable β2.
The upper limitfor the 95% confidence interval was 0.0007424 and the lower limit was 0.003591. We
found these results matched our initial expectations for the reasons stated above.
COEFFICIENTS
The coefficient for “RAT” variable was 0.0119691. This value suggests that a 1 unit increase in a players
rating would lead to a less than 1 .19% increase their wage, again this result was shocking but it was in
line with our other results regarding this variable. The coefficient for our β2 variable was 0.0021667.
This result somewhat shocked us as based on common sense and research done by other parties we
expected the coefficient value to be bigger.
VI. Validity:
The model we chosefor our projectis the fixed effect panel Data Model.
MAIN MODEL: log 𝑊𝑎𝑔𝑒𝑖𝑡 = 𝛽1 𝑅𝐴𝑇 + 𝛽2 𝐴𝐺𝐸2
+ 𝛼𝑖 + 𝑢 𝑖𝑡

7. We have chosen to use this model, as we are tryingto find the effects of RAT and Age on a basketballer
player’s wage, so we chose panel data because it enables us to control for some types of omitted variables
even without observingthem, by observingchanges in our dependent variable(wages) over time. This controls
for omitted variables thatdiffer between cases but are constantover time. We chose to use in the fixed effect
regression to remove any unobserved variablethatmay affect wage in our case,s uch as attitudes towards
work or other commitments. In our final model,we also decided to Log wage in order to simplify the
interaction between our dependent and independent variable,consequently makingit easier to interpret the
coefficientfrom our regression analysis. Wechanged the functional form of Age, squaringit in order to model
more accurately the effects of differingages on wage. This is because,age on its own doesn’t change
significantover the period of two seasons,for instancea players ageonly increases by 1 year maximum during
the two season time period.Therefore, squaringageprovides us with a better variablein testingthe
significanceof age on a basketball players’wage.
Comparingour main model to an OLS Model
OLS Model : log 𝑊𝑎𝑔𝑒 𝑖𝑡 = 𝛽0 + 𝛽1 𝑅𝐴𝑇𝑖𝑡 + 𝐴𝐺𝐸2
+ 𝛽3 𝑍𝑖 + 𝑢 𝑖𝑡
We did a regression on an OLS Model and found that an increasein RAT by 1 unitwould causea 6.5% increase
in Wage, compared to the 1.19% increasein wage by a 1 unit increasein RAT in our main model. Although, this
suggests a more accurateand valid resultfroman OLS model, itis importantto consider that the pooled result
of the OLS coefficientfails to accountfor the time variation specified in our panel data method, where data
from 2 seasons were accounted for. We also found that a 1year increasein age would causea 0.1% increasein
wage, as opposed to the 0.14% increasein wage caused by a 1 year increasein wage in our panel data model.
>
rho .8461837 (fraction of variance due to u_i)
sigma_e .37190042
sigma_u .87228381
>
> 14.30494
_cons 13.25967 .5301931 25.01 0.000 12.2144
> .003591
AGE2 .0021667 .0007224 3.00 0.003 .0007424
> .0271931
RAT .0119691 .0077221 1.55 0.123 -.0032548
>
> Interval]
LnWage Coef. Std. Err. t P>|t| [95% Conf.
Robust
>
> ers in ID)
(Std. Err. adjusted for 208 clust
> 0.0007
corr(u_i, Xb) = -0.0101 Prob > F =
> 7.54
F(2,207) =
>
> 13.03504
_cons 12.75126 .1443633 88.33 0.000 12.46748
> .0017733
AGE2 .0014657 .0001565 9.37 0.000 .0011581
> .0722618
RAT .0651762 .0036046 18.08 0.000 .0580906
>
> Interval]
LnWage Coef. Std. Err. t P>|t| [95% Conf.
Robust
>
> = .73299
Root MSE
> = 0.5149
R-squared
> = 0.0000
Prob > F
> = 230.26
F( 2, 413)
> = 416
Linear regression Number of obs
. reg LnWage RAT AGE2,r
. edit
. gen LnAGE2 =log( AGE2)
. gen LnRAT =log(RAT)

8. Comparingour main model with different Specifications
We tried quite a few specifications before concluding with our final one.
Different Specification 1: log 𝑊𝑎𝑔𝑒 𝑖𝑡 = 𝛽0 + 𝛽1 𝑅𝐴𝑇𝑖𝑡 + 𝛽2 𝐴𝐺𝐸2
𝑖𝑡
+ 𝛽3 𝐵𝐿𝑂𝐶𝐾𝑖𝑡 + 𝛽4 𝑍𝑖 + 𝑢 𝑖𝑡
In this specification model,we added blocks to our main model as an additional variablein order to check for
any improvement to our results. With this specification,wefound that a 1 unitincreasein RAT resulted in a
1.3% increasein wage. Also,an increasein ageby 1 unit would resultin a 0.2% increasein wage. This shows
that the effect of RAT on wages is higher with the inclusion of blocks by 0.11%, itis not a significantdifference
between the two specifications.As a result,we concluded that sincethe inclusion of blocks didn’tcausea
drastic changein our results,we were probably better off disregardingitas an additional variable,avoiding
any influence of omitted variablebiason our results.
Comparingour main model to another different specification
Different Specification log 𝑊𝑎𝑔𝑒𝑖𝑡 = 𝛽0 + 𝛽1 𝑙𝑜𝑔𝑅𝐴𝑇𝑖𝑡 + 𝛽2 𝑙𝑜𝑔𝐴𝐺𝐸2
𝑖𝑡
+ 𝛽3 𝑍𝑖 + 𝑢 𝑖𝑡
In this Model we decided to add logs to each of the variableto see whether itwould be easier for
interpretation. The results showed us that a 1% increasein RAT would lead to a 7% increasein wage, and a 1%
increasein Age would lead to a 139% increasein wage.
This log-logspecification doesn’tapply to our analysisasitis difficultand unnecessary to work with a
percentage change in age.
>
rho .84774041 (fraction of variance due to u_i)
sigma_e .37233641
sigma_u .87856648
>
> 14.35906
_cons 13.33891 .5174481 25.78 0.000 12.31877
> .1136435
BLOCKS -.1014356 .1090946 -0.93 0.354 -.3165146
> .0034646
AGE2 .0020847 .0007 2.98 0.003 .0007047
> .0279419
RAT .0139054 .0071197 1.95 0.052 -.000131
>
> Interval]
LnWage Coef. Std. Err. t P>|t| [95% Conf.
Robust
>
> ers in ID)
(Std. Err. adjusted for 208 clust
> 0.0017
corr(u_i, Xb) = -0.0073 Prob > F =
> 5.24
F(3,207) =
end of do-file
.
. edit
>
rho .85245131 (fraction of variance due to u_i)
sigma_e .37445073
sigma_u .90003996
>
> 13.96839
_cons 5.779893 4.153454 1.39 0.166 -2.408601
> 2.654004
LnAGE2 1.390668 .6408028 2.17 0.031 .1273313
> .2626427
LnRAT .0709295 .0972428 0.73 0.467 -.1207838
>
> Interval]
LnWage Coef. Std. Err. t P>|t| [95% Conf.
Robust
>
> ers in ID)
(Std. Err. adjusted for 208 clust
> 0.0366
corr(u_i, Xb) = 0.0622 Prob > F =
> 3.36
F(2,207) =
> 2
overall = 0.2065 max =
> 2.0
between = 0.2170 avg =
> 2
R-sq: within = 0.0633 Obs per group: min =
> 208
Group variable: ID Number of groups =
> 416
Fixed-effects (within) regression Number of obs =
. xtreg LnWage LnRAT LnAGE2 if YEAR==1| YEAR==2, fe r

9. VII. Conclusion:
In conclusion,our empirical experiment sought to find out how RAT, a basketball player’s ratings affects wages
in the NBA with the assumption that the RAT would have a statistical significancein determiningbasketball’s
players’wages in the NBA. In order to test this assumption,we ran statistical inferencetests with data
collected from the Basketball sources,such as theNBA website regardingplayer’s statistics(wage, RAT, age,
race, collegeinformation,blocks) fromtwo seasons becausewe decided to use a panel data evaluation,paying
attention to how our variables changeover time.
We chosea fixed effect estimator to estimate our fixed effect model. We assumed the four key fixed effect
assumptions would hold four our model. We believed these assumptions would hold becauseof our sampling
method, choiceof variables and our model specification. Based on these reasons we expected our model to
yield consistentand accurate results .Overall,we assumed our model was correctly specified,had no omitted
variablebiasand we included all theright variables,makingthe fixed effect estimator the best fitfor our
project.
However, we believe that there were some limitations to our project. This may explain why our results didn’t
match our expectations. Firstly,we didn’t take into accountthe lagbetween wages and RAT, meaning, there is
a time lagbetween the period in which a players ratincreases and when a new contractis signed to reflect a
wage increaseor decrease. We believe that some way this could haveinfluenced our results and findings by
distortingthe effect of RAT on wages.
Another limitation wehad was that we only used T=2 years.To solvethis limitation,we could have
implemented a panel data set on a much larger scale,e.g. 10, instead of 2 years.This larger data set would
have considered our contractproblem we mentioned earlier,however, collectingdata for multipleyears
would have consumed a lot of time. In addition,itis difficultto find data on players goingas far back 10
seasons,as someplayers haven’t been playingfor that long. A largedata set such as T=10 years would have, in
itself created limitations/problems for us such as missingvariables,amountof data available,outliers,not
correctingwages properly for inflation etc.
To summarizeour data findings,our results from conductingour statistical inferencetests were shocking, to
say the least.To begin with we had expectations for our RAT variableto be statistically significant based on
common sense grounds. However at the conventional 5% t stat significancelevel and at the p value
significancelevel we found that the variablewas notstatistically significant.Aplayer with a lowRAT score
tends to be paid lower than a player with a much higher RAT, real world examples help to illustratethis Amar'e
Stoudemire’s 2014-2015 RAT scoreis a considerably lowat20.18 and his salary isaccordingly quitelowat
$306,876,in comparison Kevin Durant’s 2014-2015 RAT score is 41.80
(BBCricketRugbyXGamesChalkEnduranceCFL, FB and BB, 2015 )and his wage is accordingly high ata whopping
$18,995,624.(BBCricketRugbyXGamesChalkEnduranceCFL, FB and BB, 2015) This example goes to highlight
the implicationsRATcan have for wage, so contrary to our data findings we would say that the significanceof
a Players RAT with regards to wage cannot be disregarded.
On balance,itis safeto concludethat although our findings didn’tquite confirmour expectations, itdid imply
a positiverelationship between RAT and wage. In our opinion,given a wider range of data and time period, we
believe that RAT would be shown to be statistically significantand havea positiveeffect on wage.

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