Final Paper

SPM 490
Independent Study-
Advanced Football
Analytics
Final Report
Robbie Hamill
5/9/2016

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Robbie Hamill SPM490 5/9/16
Table of Contents
Introduction.......................................................................................................................................2
Football Outsiders...............................................................................................................................3
EViews- Salary/Performance ...............................................................................................................5
Age-Experience ..................................................................................................................................7
EViews- Age/Experience....................................................................................................................13
Weather/Performance......................................................................................................................16
EViews- Weather/Performance.........................................................................................................17
Conclusion .......................................................................................................................................24

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Introduction
I decidedinearlyJanuarythatI wantedtocomplete anindependentstudy,butIwas unsure as
to what.I was debatingdoing somethingrelatedtoanalytics.Aftermuchconsideration,Idecidedto
revolve myindependentstudyaroundresearchingadvancedfootballanalytics,specificallythe analytics
on FootballOutsiders.FootballOutsiders isafootball analyticswebsite posting articlesandinnovative
statistics.Theyhave createdmanydifferentstatisticsoverthe years,andtheirstatisticswere the basis
for myresearch.NotonlydidI furtherenhance myunderstandingof advancedfootball statisticsand
howtheyare calculated,butIwas able to take itone stepfurther.Iwantedtosee if there wasa
relationshipbetweenQBperformance andsalary,since itappearedthatnoanalyticswebsite reallydove
intothat relationship.There waslittle researchonsalary. CanQB performance explainhow andwhy
playersare paidthe way that theyare?Are QBs rewardedforthrowingmore yards,throwingfewer
interceptions,orhavingahighercompletionpercentage?Iwasable to see mayrelationshipsbetween
variables,Ithenmovedontoanothertopic.
Afterexploringthe relationshipbetweenQBperformance andsalary,Ithendove intoseeingif
there wasa relationshipbetweenQBperformance andweather.Canweathermake animpacton howa
QB plays?Do quarterbackstendtoperformpoorlyinwindy,rainy,snowyconditions?Doquarterbacks
enjoythe “mile high”Denveraltitude like baseballhittersdo?Iwasable to draw many conclusions
basedoff of takingsimple statisticsandcomparingthem.
I learnedalot duringmyweeklymeetingswith Dr.Paul,myindependentstudyadvisor,andI
lookforwardto hopefullybringingsomethinglike thisintomyfuture careerone day.Ihope to use the
knowledge thatIgainedoverthe pastfew monthsinthe real world,andhelpteamsevaluate players
basedoff of advancedresearchthat nobodywouldeverhave thoughtof.

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Football Outsiders
As mentionedabove,myfirsttopicthatIwantedto explore wasthrough FootballOutsiders and
weather. Ifirsttooka lookat the 2015 QB advancedstatisticsandpostedthemintoone of my excel
sheets.The statsthat I lookedatwere DVOA,DYAR,YAR,VOA,QBR,Passes,Yards,EfficientYards,TD,
INT,CompletionPercentage,andDPI. Ithenlookedat everyquarterback’ssalaryforthe 2015 season.
For the 2015 season, Iusedthe cap hit.Thisincludedbase salaryandbonusesthe playerwasgoingto
make.Thisiswhat the initial spreadsheetlookedlike:
It was greatto lookat playersalaryand see if QB performance fromthe pastseasoncould
explainthe player’ssalary.Insome cases,itdid.Lookingatverycostlyplayerslike TomBrady,Ben
Roethlisberger,Russell Wilson,andPhilipRivers,Iwasable to see thattheirindividual QBperformance
was stellar.These weresome of the bestquarterbacksinthe pastseason.There were alsosome less-
paidquarterbacksthat stoodout.KirkCousins,DerekCarr,and TyrodTaylor all hadveryimpressive
seasonslastyear.All three playersmade lessthan$1.5 million.Some quarterbacks,however,wereon
the wrong endof the spectrum.Last year,Tony Romo,future HOFerPeytonManning,andColin
Kaepernickall costtheirfranchise about$15 millioneachandproducedhorrendousseasons.Allthree
quarterbackswere benchedatsome pointduringthe season(Romo’swasdue toinjury). Tome,this

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spreadsheetwasveryinterestingbecausethisiswhatmostGeneral Managersuse to determine how
theyshouldpaytheirplayers.Theylookatpreviousstatisticsandpriorsalariesandputtogethera
general ideaasto whatthe playerisworth to the teamand if he is worthgettinga new contract. This
was the case of KirkCousinstothe Redskinsthispastyear.Cousinswasmakinglessthana million
dollarsin2015, andwas appliedthe franchise taginthe offseason,boostinghissalaryinthe 8-figure
amount.Cousinswasable toget higherpaybecause he finishedinthe top10 inmanyadvanced
analyticsandwas an instrumental partinthe Redskincampaign.Itwill be interestingtosee how Cousins
fairsthisyear- he put togethermediocre seasonspriortothisone.
Sometimes,quarterbackscanputtogether one goodseasonandgetone heckof a payday.
Afterthe 2013 SuperBowl,Joe Flaccogot a huge raise afterputtingtogetherone of the better
postseasonsinrecentmemory.Eversince then,Flaccohasputtogethermediocre stats,butnotworthy
of the giantcontract he receivedbackin2013. I lookedateveryquarterbackwhothrew a passinthe
past fourseasonsandput togethertheirreport.Itlookedsomethinglike this:
One can see that Cousinsproducedprettymediocre stats(evenverybelowmediocre in2013),
so teamsshouldbe careful whenrewardingaplayerforone great season. Drew Breesisone of the
more consistentlygoodplayersoverthe years,despite throwingdoubledigitinterceptionsthe last4

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seasons.Ialsoaddedanothercomponentaftersalary,winsandlosses.Iwantedtosee if there wasa
relationshipbetweensalaryandwins,orQB performance andwins.Doteamswinif theirquarterback
playswell?Doquarterbacksgetpaida little more if theirteamstartswinning?These were some of the
questionsthatIwantedto answer,andthusI beganusingEViewseconometricsoftwaretoexpressmy
equationandsee if there wasreallyacorrelationbetweenthe two.
EViews-Salary/Performance
WhenI firstexperimentedwithEViews,Iwantedtosee if there were relationshipsbetweenthe
variablesthatI foundfrom FootballOutsiders.Inmyequationtab,I firstinputtedsalaryasmy
dependentvariable andQBRas my independentvariable.Iwantedtosee if there wasabasic
relationshipbetweenoverallQBperformance andhissalary.The resultsIfoundwere basicallywhatI
expected.
There appearsto be a prettystrong correlationbetweensalaryandaquarterback’sQBR,
meaningthatthe higherQBR that the quarterbackproduces,the more moneyhe tendstomake.Thisis
prettymuch self-explanatory;the bestplayersinthe game getthe most money.If youpostbad
numbers,there’saprettystrongchance that you won’thave a veryhighsalary.

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Similartomy lastequation,Iwantedtosee furtherif QB performance playedanimpactin
playersalary.IaddedDYAR, or Defense-AdjustedYardsAbove Replacement.Thisstattellsme how
much bettera playeristhanhisbackup.The higherthe number,the more valuable the playeris.Similar
to my firstresults,Ifoundoutthat the higherDYAR,or higherperformance metric,increasessalary.
Playerslike DrewBrees,BenRoethlisberger,TomBrady,andAaronRodgerspostveryhighDYAR
numbersandinturn, theyare some of the highestpaidplayersinthe league.
One of the more shockingresultswasthroughDPI.DPIillustrateshow manydefensive pass
interference callsaQB forces.Itisn’tnecessarilyastatthat islike QBRor DYAR whichexplainshow
efficientorgooda quarterbackis.Some QBs, like Drew Brees,AndyDalton,andMVPCamNewton,
force verylittle passinterferencesoverthe course of aseason.Thisisa stat that can be easily
overlooked.Isetsalaryagain as my dependentvariable,andputDPIand the yards fromthe pass
interference callsasmyindependentvariables.
Interestingly,the higherDPIthata QB posts ina seasonthe more he isrewarded.While his
yards were notstatisticallysignificant,his DPIvalue was.Ifoundthisveryinterestingbecause evenI
overlookedaplayer’sDPI.It’snotnecessarytoposthighDPIvalues(again,Brees,Dalton,andNewton
had some of the lowestoverthe lastfourseasons) butitdoeshelpa playerwhentryingto determine
howmuch he shouldmake.ThisisdefinitelysomethingthatGMs andagentsneedto take a lookout
whenworkingoutplayercontracts.If a playerpostshighDPI values,he shouldinturnmake a decent

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salary,accordingto EViews. While the amountof yardsisprettystatisticallyinsignificant,the overall
numberof pass interference callsthataQB forcesoverthe course of a seasontendsto increase his
salary.
There were otherequationsthatItriedthat tendedtomake sense.Afterlookingatthe
DPI/salaryrelationship,Ilookedintomultiple QBperformance variables.These includedcompletion
percentage,yards,TDs,andINTs.Afteradjustingthose variablesfromtime totime,Isaw prettybasic
results.The highercompletionpercentage,yards,andtouchdownsthrownbyaquarterbacktendedto
increase overall salary.Interceptionsactuallyhadapositive coefficient,whichwassomewhatshocking
since the overall assumptionisthatthe more interceptionsthataplayerthrows,the lessmoneyhe
shouldmake.Ideterminedthatthiswasprobablydue tothe fact thatlots of highpaidquarterbacks
throwinterceptions(Brees,Newton,Eli Manning).
Salaryand performance wasa veryinterestingrelationshiptolookat.I learnedmanythings
abouthow a QB’s performance canimpacthow much moneyhe makes.Some of the relationshipswere
prettysimple andself-explanatory,suchassalary-QBRand salary-yards.Some of the relationshipswere
more complex andoverlooked,suchasa player’sDPIandhissalary.
Age-Experience
Afterevaluatingthe relationshipsbetweenaquarterback’sperformance andhissalary,Iwanted
to lookat more variables.Ibeganto lookintoa player’s age andthe yearsof experience inthe league.
Do olderquarterbacksmake more moneythanyoungerquarterbacks?Domore experienced
quarterbacksmake more moneythanlessexperiencedquarterbacks?Ieventiedage andexperience
back to performance andlookedatpossible relationshipsthere.Whenaquarterbackages,doeshe tend

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to performworse?Atwhatage isthere a maximumvalue forhow mucha playermakesor how well he
tendsto perform?These were questionsIhopedtoanswerinmynextregressionoutput.
My firstregressionoutputlookedsomethinglike this:
The resultswere surprisingandexpected.Initially,Ithoughtthatbothvariableswouldhave the
same signon theircoefficient.Iexpectedbothage andexperience tohave eitherapositive coefficient
(as salaryincreases,bothage andexperience increase)ora negative coefficient(bothage and
experience decrease assalaryincreases).Thiswasnotthe case,as the resultsshowedone positive
coefficientandone negative coefficient.Assalaryincreased,age tendedtodecrease,meaningthatthe
olderplayerstendedtomake lessmoneythanyoungerplayers.Asaquarterbackages,he makesless
money.Thiswasquite surprisingtome,asthe highestpaidquarterbacksare typicallythe most
experiencedveterans.DrewBrees,PhilipRivers,Eli Manning,andTomBradyare some of the more
olderquarterbacksinthe league andhave some of the highestsalariesinthe entire NFL.PlayerslikeKirk
CousinsandJameisWinstonwhoare prettyyoungplayersstartout withfairlylow salariescomparedto
the olderveterans.Experiencewasthe opposite.Asaplayergainsmore experience inthe league,he
tendsto make more money. Thismakessense inmanyways.ImentionedBrees,Rivers,Eli,andBrady
earlier,whoare some of the olderquarterbacksinthe league.Theyare alsothe mostexperienced
quarterbacksinthe league,andtheirsalaryshowsthat.Thisinformationwasveryuseful tome.If Iwere
to workfor a team,analyzinghowmucha quarterbackshouldmake,Ican use thisregressionto

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determine whattheirsalaryshouldbe basedoff of theirage andtheiroverall experience.While there
are some outliersinthe data,the resultswere statisticallysignificant,tellingme thatthere isevidence
that as age increases,salarydecreases,andasexperience increases,salaryincreases.
My initial reactiontothe results stumpedme.Ihadno ideaas to whyage woulddecrease as
salaryincreased,butsalaryincreasedwhenexperience increased.Ifeltasthough theyshouldgohandin
handwithone another.I put togetheralistof all the quarterbacksat a specificage:
Above,I’ve listedthe quarterbackswhowere atacertainage ina certainseason.Forexample,
the only39 year oldstoeveryplayQB inthe NFLin the last4 seasonswere Matt HasselbeckandPeyton
Manningin 2015. I thentook the average of each quarterback’sstatfor theirage class.For example in
the 39 year oldgroup,I averagedHasselbeck’s -41DYAR and Manning’s -328 DYAR to give me an
average -184.5 DYAR for39 yearolds.I didthisforeveryvariable andforeveryage,stretchingfrom21
to 39. At whatageswouldquarterbacks performbetterthanotherages?
I was able toput togetheranexcel sheetof everyage average,stretchingfrom21 to 39, for
each one of the stats that I lookedatfrom FootballOutsiders.It lookedsomethinglike this:

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Thissheetallowedme tolookateveryage and make a generalizationaboutsome of the ages.
Before IwentintoEViews,Imade graphsinexcel of eachstatistic.Some of themare listedbelow.
Basedoff of the excel graphsIcreated,I couldsee where EViewswascomingfromintermsof
experience.Itappearedthatasquarterbacksgainedmore andmore experience,theirDVOA,DYAR,
-500
0
500
1000
1 3 5 7 9 11 13 15 17 19 21 23
DYAR
DYAR
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
1 3 5 7 9 11 13 15 17 19 21 23
DVOA
DVOA
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000
1 3 5 7 9 11 13 15 17 19 21 23
Salary
Salary
0
20
40
60
80
1 3 5 7 9 11 13 15 17 19 21 23
QBR
QBR

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salary,and QBR tendedtoincrease.Iwasstill a little confused,however,becauseIwaslookingatage
rather thanexperience.Itappearedasthoughthe graphsshowedanoverall increase insalaryasthe
playeraged,whichcontradictedthe negativecoefficientthatEViewsproduced. Mostof the variables
that I lookedatshowedageneral increasingtrendwithage.
I thentookmy age/experience researchfurther,andpluggeditintoEViews.Aftergetting
multiple resultsinwhichmyp-valuewasmuchlargerthan.05 or even.10,thus showingthe resultsto
be statisticallyinsignificant, Isquaredmyage and experience variables.Thisgotmyp-value todecrease,
and become more significant.Fromthere,Itookthe coefficientfromEViewsforage andmultiplied itby
the age of the player.Ithentookthe squaredcoefficientfromEViewsandmultiplieditbythe age
squared.Finally,Isubtractedmyfirstnumberfrommysquarednumber,whichdeterminedmysalary
curve basedoff of age.It lookedlike this:
0
10000000
20000000
30000000
40000000
50000000
60000000
70000000
80000000
90000000
100000000
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
salary

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The fact that I got a parabolaallowedme todraw conclusionsastoa “peakage” forsalary.As
the graph illustrated,aroundthe time the playerturns24 or 25 iswhenhe makesthe mostamountof
money. Itincreasesfromwhenaplayerentersthe league until he turns 24 or 25 and thenbeginsto
decrease. Thisgave me a much betterunderstandingastowhyEViewsgave me a negative coefficient
whencomparingsalaryandage. AsI mentionedearlier,thisresultisveryinterestingtome because if I
were towork ina team’sfrontoffice,Icanuse thisdata to explainhow mucha playertypicallymakes.I
can confidentlysaythatif a playeris32 yearsold,typicallyhe won’tmake asmuchas a 27 yearold.If a
playeris22, I can say that he will expecttomake more moneydownthe road,sosigninghimtoa long-
termcheap contract wouldbe beneficial tothe organization.
I thendidthe same thingfor experience andsalary. Igot a prettysimilarcurve,exceptthe peak
experience wasafewyears laterthanthe peakage.
-25000000
-20000000
-15000000
-10000000
-5000000
0
5000000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Series1

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As the graph illustrates,aplayer’speaksalaryoccursaroundwhenhe is4 or 5 yearsintothe
league (aroundages25-27). Thiswas veryinterestingbecause itsomewhatcontradictsthe age graph.
The age graph toldme that whena playerisa year or twoyounger,he tendstomake his highestsalary.
The experiencegraphtellsme thatif a playerspendsabout4 yearsinthe league,thenhe willmake his
highestsalary. Thatwouldmeanplayersleavingtocome intothe NFL at aroundage 19, isquite unheard
of.This graphalso confusedme atfirstbecause whenIran EViews,Isaw a positive coefficientonmy
experience variable,meaningthatas experience increases,salaryincreases.Accordingtothisgraph,it
doesincrease atfirst,butthentends to decrease asthe playergainsmore experience. Again,Ican use
thisgraph to myadvantage inthe future. If a playerwith8 yearsof experienceislookingforanew
contract, I can determine anaccurate contract (before lookingatthe actual player).Like age,if ayoung
playerislookingfora newcontract,I can signhimto a deal that benefitsthe organizationbasedoff of
the experience curve.
EViews-Age/Experience
Afterlookingatthe relationshipsbetweensalary-age andsalary-experience,Idecidedtobring
QB performance intoplay.Doesperformance tendtoincrease withage ordo quarterbacksdeteriorate
as theygrow older?Domore experiencedquarterbacksperformbetterordolessexperienced
quarterbacksperformbetter?These are questionsthat couldeasilybe explainedthroughEViews.
I firstlookedintothe possible relationshipbetweenage andperformance.Formyperformance
metric(s) IusedQBR,DVOA,andDYAR. I setthose as my dependentvariableandage remainedmy
independentvariable.

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All three dependentvariablesgave me statisticallysignificantresults,andall three gave me a
positive coefficient.Itappearedthatasage increases,DVOA,DYAR,andQBRall increase as well,asthe
graphs inexcel showedme earlier.Thiswasquite eye-openingforme,assome of the worst
quarterbacksthispastseason(PeytonManning,TonyRomo,Matt Hasselbeck) weresome of the oldest
quarterbacks. Italsodoesmake sense,asthe oldera quarterbackgets,the more experience he gets,
and the betterstatshe tendstoproduce.The fact that DVOA,DYAR,and QBR all increase withage tells
me that signinga veteranquarterbackwho’sbeeninthe leagueforyearsmaybe a smarter move than
tryingto draft a quarterback(if lookingatthe short-term).
My nextorderof businesswastolookat the relationshipbetweenperformance andexperience.
Like age,I usedthe same three differentvariables,DVOA,DYAR,andQBR as my performance metrics.
The resultswere:

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Like age,all three variablesshowedanincrease whenexperience increased. Thismade sense to
me,as playersgetoldertheygetmore yearsinthe league.Thus,asage showedperformance variables
to increase,experience shouldaswell.Once again,thisshowsthe importance of aveteran quarterback,
as more addedyearsof experience helpsoverall performance.Goingbeyondthese three stats,TDsand
completionpercentage alsoincreasedasexperience increased. Itisimportanttoteamsto have
experiencedquarterbacks,astheyare the ones likelytoperformbetterthanrookiesandsophomores.
Thisshowsteams,once again,to go afterexperiencedveteransinfree agencymore thanyounger
veterans.
Overall,QBR,DVOA,andDYAR tendedtoincrease asage and experience increased.The more
experiencedquarterbacks typically performbetterthanthe lessexperiencedquarterbacks.

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Weather/Performance
Afteranalyzingthe heckoutof age/experience of quarterbacks,Ithenwantedtoturnmy
attentiontoone lastproject.It tookme awhile tofigure outwhatspecificallyIwantedtodo,so I looked
at prior spreadsheetsfrommySPM300-Sport Data Analysisclass.Inthatclass,we had one football unit
inwhichwe took an NFLteam andlookedat attendance,weather,andteamperformance andtriedto
draw relationshipsbetweenthem.Itimmediatelysparkedanideainmyhead- canweatherimpact
quarterbackperformance?We hearoverandoverthat in coldweatherteamsshouldrunthe ball like
the “good olddays” of tough,cold,frozentundrarunning,but isthat reallytrue?Do quarterbacksreally
sufferif the ball isa little more wetthannormal?Or evenif the windisblowingalittle harderthan
normal?These were some of the questionsthatIwantedtoanswerinmy weather-performance section
of myindependentstudy.
I firstput togetheralistof all the quarterbacksof everyteamof the season,andeverygame’s
stats forthat week.Ialsoincludedthe weatherforthe game.Ilookedattemperature,humidity,wind
speed,winddirection,sealevelpressure,andprecipitation.The spreadsheetlookedlike this:
Thiswas the example forSanDiego’squarterbackthisyear,PhilipRivers.Some teams,like the
Browns,rotatedquarterbackseveryotherweek.Itookintoconsiderationthe location(some
quarterbacksplayedingamesthatwere indomes).Thisspreadsheetallowedme tosee QBperformance

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on a game-by-game basis,andallowedme tolookat eachspecificgame.Iwas able tosee if there were
possible externalfactorsinfluencingperformance. Intermsof QBperformance,the variablesthatI
lookedatare listed above.Theywere QBR,PasserRating,CompletionPercentage,Yards,Touchdowns,
and Interceptions. Whenitcame todomes,I factoredthemintothe same spreadsheet.A further
researchprojectI coulddois comparingdome statisticswithoutdoorstatistics, anddeterminingif dome
conditionscanleadtoquarterbacksperformingverywell.
EViews-Weather/Performance
For thiscurrentproject,I wantedtolookat overall stats,andsee if weatherdidinfact playa
role inQB performance.Ifirstlookedat rating.I setrating as mydependentvariable and temperature,
humidity,andsealevel pressure asmyindependentvariables.The outputlookedsomethinglikethis:
These resultstoldme afewthings.Firstoff,Icouldn’tuse all the weathervariableswhen
lookingatratingsince none of themproducedstatisticallysignificantresults.Ilimitedittojustthe three.
The resultstoldme that temperature generallyfavorsrating.The warmerthe temperature gets,the
betterchance the quarterbackhas of gettinga decentrating(whensettingp-value at.1).Sealevel
pressure toldme somethingthatIwouldn’tsuspect.Sealevelpressureactuallyincreasesa
quarterback’s rating,andby a decentamount.Again,Ican onlysay thisisstatisticallysignificantwhen

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my p-value is.1as opposedto.05 or even.01. Sealevel pressure actuallyhelpsquarterbacksout.When
thinkingof highersealevel,we tendtothinkof baseball playersbeingthe onestoreceive the benefit.
CoorsFieldinDenverisnotoriousforbeingaveryhitter-friendlyballpark,withthe oddsof players
hittinghome runsincreasingdue toDenverbeingveryhighabove sealevel.Quarterbacksalsoappearto
receive some sortof benefitwhensealevel increases. Thisisuseful becauseif Iaminvolvedinateam’s
frontoffice andwe are playingagame in Denveroranothercity witha highsealevel pressure,Ican say
that quarterbackstypicallyperformbetter ingreateraltitudesthanlesseraltitudes.Throw the ball.If we
are playinginloweraltitudes,Iwouldadvise the coachingstaff toputthe ball on the groundmore.
Researchlike this,onthingsthatwouldbe oftenoverlooked,isextremelyuseful whendetermininghow
to gaina competitiveadvantage.Are the statsskewedabitbecause of the greatseasonsManninghad
inDenverin2012, 2013, and2014? Possibly,butitcan be reasonablyconcludedthatsealevelpressure
increasesQBrating.
Precipitation wasone of the biggervariablesthatIwantedto lookat.The overall thoughtisthat
precipitationhurtsQBperformance.Quarterbackshave ahardertime holdingontothe ball (whether
beinginterceptedorfumbling) inrainy/snowyconditionsthaninsunny conditions.Forlookingat
precipitation,IlookedatQBR as mydependentvariable.Iinitiallyletprecipitationstandalone asmy
independentvariable.
We can reasonablyassume,basedoff of the p-value,thatprecipitationdoesinfacthurtQB play.
Precipitationpostedanegative coefficientvalue,at -6.91. Whenit rains,quarterbackstypicallyperform

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worse thanwhenit issunny,accordingto the QB’s QBR (ESPN hasnot releasedthe formulaforthis
statistic,soit ishard to determine howitis calculated). Again,thisinformationisuseful.Icansay that
the statisticsbackup the general thoughtthatquarterbacksperformworse inrainy/snowyconditions
than insunnyweather.Ican advise ateam the day of to put the ball on the groundmore as opposedto
throughthe air.
Initially,Ilookedatprecipitationalone.Iwantedtoadd more variablesandIthoughtthe perfect
one to pair alongwithprecipitationwaswindspeed.The general assumptionaboutwindspeedisthat
the windieris,the worse the quarterbackwill typicallyperform.WhenIpluggedbothprecipitationand
windspeedinEViews,Igotthis:
Windspeed,like precipitation,backedupthe general assumption.Aswindspeedincreases,QBR
actuallydecreases.Itisnota highamount,but still statisticallysignificant.Thisisimportantforteams
playinginwindiercitieslike Chicago.Itcanbe generallyadvisedthatquarterbacksmayperformworse in
citieslike Chicago(The WindyCity) thanincitieswithdome-like conditions orverylittle wind. Ironically,
whenI pairedthese twoforanotherequationandputTDs as my dependent,bothshowedveryhighand
statisticallyinsignificantvalues.However,whenIusedINTsasmy dependent,windspeedshoweda
positive coefficient.Thisshowsme thatwindspeedactuallyhasnoeffectontouchdowns,buthasa
prettysignificanteffectoninterceptions.Again,thisisaconclusionpeoplehave drawnforyears,the
windieritisthe worse the QB will probablyplay. The more rainy/snowyconditionsorthe windierthe
conditionsare,the more likelyateamwill runthe ball as opposedtothrowingthe ball.Thisisuseful in

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citieslike Seattle(where notonlythe Legionof Boomplaysbuttendstorain more than mostcitiesin
the country) or GreenBay/Buffalo(whenitstartstosnow aroundHalloween).
The general findingsfromEViewswerethat temperature helpsquarterbacksout(throughmany
performance variablessuchascompletionpercentage,QBR,andrating),humidityhasrelativelylittle
effectonQB performance,precipitationandwindspeedtendtodecrease aquarterback’sperformance,
and sealevel pressureactuallyhelpsquarterbacksout.Findinginformationlikethisisveryusefultome,
especiallyif Iwantto workfor a team’sfront office.The morningof the game,I couldhelpinfluence a
game planjust basedoff of watchingthe weather.
While lookingatthe weather/performance variablesandseeingif there wasarelationship
betweenthemwasveryinsightful,Iwasable tofurthermyresearch.On one of my lastweekly
independentstudymeetings, we lookedateveryquarterbackonEViews,usingthe @expandplayertool.
The firstone we lookedathadinterceptionsasourdependentvariable,andwindspeed,temperature,
and humidityasourindependentvariables.EViewsproducedthis:
DependentVariable:INT
Method: LeastSquares
Date: 04/20/16 Time:15:43
Sample:1 512
Included observations:512
Variable Coefficient Std. Error t-Statistic Prob.
C 1.288858 0.363966 3.541151 0.0004
WIND_S 0.025444 0.010138 2.509669 0.0124
TEMP -0.002724 0.003156 -0.863136 0.3885
HUMID -0.001663 0.002652 -0.627238 0.5308
PLAYER="Bradford" -0.143361 0.325420 -0.440542 0.6598
PLAYER="Brady" -0.702314 0.315089 -2.228942 0.0263
PLAYER="Brees" -0.281272 0.320319 -0.878098 0.3804
PLAYER="Bridgewater" -0.618204 0.314551 -1.965354 0.0500
PLAYER="Carr" -0.406914 0.314771 -1.292730 0.1968
PLAYER="Cassel" -0.130926 0.424067 -0.308740 0.7577
PLAYER="Clausen" -0.118653 0.559075 -0.212232 0.8320
PLAYER="Cousins" -0.459060 0.314155 -1.461250 0.1446
PLAYER="Cutler" -0.470914 0.319278 -1.474932 0.1409
PLAYER="Dalton" -0.655372 0.338998 -1.933264 0.0538
PLAYER="Davis" 0.105626 0.671239 0.157360 0.8750

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PLAYER="Fitzpatrick" -0.214682 0.314203 -0.683259 0.4948
PLAYER="Flacco" -0.002452 0.357911 -0.006851 0.9945
PLAYER="Foles" -0.207941 0.347325 -0.598693 0.5497
PLAYER="Freeman" 0.021128 0.912951 0.023142 0.9815
PLAYER="Gabbert" -0.277509 0.385260 -0.720315 0.4717
PLAYER="Hasselbeck" -0.183273 0.383613 -0.477754 0.6331
PLAYER="Hoyer" -0.376384 0.357270 -1.053502 0.2927
PLAYER="Jones" -0.175984 0.666453 -0.264061 0.7919
PLAYER="Kaepernick" -0.517095 0.383801 -1.347300 0.1786
PLAYER="Keenum" -0.899634 0.455370 -1.975609 0.0488
PLAYER="Luck" 0.639609 0.401113 1.594585 0.1115
PLAYER="Mallett" -0.104678 0.453569 -0.230787 0.8176
PLAYER="Manning" -0.064645 0.287276 -0.225029 0.8221
PLAYER="Manuel" 0.303706 0.666985 0.455341 0.6491
PLAYER="Manziel" -0.568570 0.404294 -1.406326 0.1603
PLAYER="Mariota" -0.209148 0.346507 -0.603588 0.5464
PLAYER="McCarron" -0.708270 0.501717 -1.411693 0.1587
PLAYER="McCown" -0.629055 0.385389 -1.632261 0.1033
PLAYER="Mettenberger" 0.293494 0.495166 0.592718 0.5537
PLAYER="Moore" 0.919170 0.557337 1.649218 0.0998
PLAYER="Newton" -0.508406 0.313565 -1.621374 0.1056
PLAYER="Osweiler" -0.494301 0.397692 -1.242923 0.2145
PLAYER="Palmer" -0.475428 0.318636 -1.492073 0.1364
PLAYER="Rivers" -0.348605 0.313441 -1.112189 0.2666
PLAYER="Rodgers" -0.716249 0.314847 -2.274913 0.0234
PLAYER="Roethlisberger" 0.146605 0.340321 0.430784 0.6668
PLAYER="Romo" 0.687849 0.494986 1.389631 0.1653
PLAYER="Ryan" -0.076743 0.313144 -0.245073 0.8065
PLAYER="Sanchez" 0.364968 0.664188 0.549495 0.5829
PLAYER="Schaub" 0.760733 0.664505 1.144813 0.2529
PLAYER="Smith" -0.760597 0.314500 -2.418431 0.0160
PLAYER="Stafford" -0.226316 0.314752 -0.719029 0.4725
PLAYER="Tannehill" -0.441789 0.313964 -1.407131 0.1601
PLAYER="Tanney" -0.978872 0.912951 -1.072207 0.2842
PLAYER="Taylor" -0.775490 0.327738 -2.366188 0.0184
PLAYER="Vick" -0.645268 0.664434 -0.971155 0.3320
PLAYER="Weeden" -0.678292 0.455048 -1.490594 0.1368
PLAYER="Wilson" -0.620240 0.315597 -1.965293 0.0500
PLAYER="Winston" -0.143509 0.313388 -0.457928 0.6472
PLAYER="Yates" -1.000495 0.912905 -1.095946 0.2737
It shouldbe firstnotedthatone quarterbackismissingfromthe list,Blake Bortles.Thiswas
because we needtohave a dummyvariable dropped;similartoif we wantedtocompare male/female
data. One typicallygetsdropped,asyoucannot compare both.What the coefficients tolduswasthis:
whenholdingwindspeed,temperature,andhumidityconstant, that’show manymore/less
interceptionsthe QBwouldthrowcomparedtoBortles.Forinstance,if lookingatsome statistically
significantquarterbackresults,like Brady(-.7),Dalton(-.65),andBridgewater(-.61),we candetermine

22
that Brady wouldthrow.7 INTlessthan Bortleswouldunderthe same windspeed,temperature,and
humidityvalues,Daltonwouldthrow.65INTlessthan Bortles,andBridgewaterwouldthrow .61 INT
lessthanBortles.Whenlookingatthe list,alot of the valuesappeartobe negative.Itcan be relatively
concluded(andvalidatedbylookingatexactstats) thatBortlesthrew a lotof interceptionsinthe 2015
campaign.
We thenlookedatthe ratingof the quarterbacks,while keepingourindependentvariablesthe
same as well asour dummyvariable asBlake Bortles,andgotthis:
DependentVariable:RATING
Method: LeastSquares
Date: 04/20/16 Time:15:46
Sample:1 512
Included observations:512
Variable Coefficient Std. Error t-Statistic Prob.
C 81.35138 10.07307 8.076123 0.0000
WIND_S -0.625500 0.280587 -2.229254 0.0263
TEMP 0.144268 0.087348 1.651656 0.0993
HUMID 0.025243 0.073389 0.343966 0.7310
PLAYER="Bradford" 0.160955 9.006285 0.017871 0.9857
PLAYER="Brady" 15.29993 8.720357 1.754508 0.0800
PLAYER="Brees" 7.916919 8.865112 0.893042 0.3723
PLAYER="Bridgewater" -0.397073 8.705468 -0.045612 0.9636
PLAYER="Carr" 5.013549 8.711569 0.575505 0.5652
PLAYER="Cassel" -18.63285 11.73642 -1.587610 0.1131
PLAYER="Clausen" -20.96276 15.47288 -1.354806 0.1761
PLAYER="Cousins" 15.55784 8.694528 1.789383 0.0742
PLAYER="Cutler" 8.564230 8.836303 0.969210 0.3330
PLAYER="Dalton" 23.81773 9.382059 2.538646 0.0115
PLAYER="Davis" -20.31495 18.57712 -1.093547 0.2747
PLAYER="Fitzpatrick" 1.851454 8.695832 0.212913 0.8315
PLAYER="Flacco" -5.432990 9.905487 -0.548483 0.5836
PLAYER="Foles" -15.32402 9.612522 -1.594173 0.1116
PLAYER="Freeman" -27.15486 25.26672 -1.074728 0.2831
PLAYER="Gabbert" 0.120077 10.66241 0.011262 0.9910
PLAYER="Hasselbeck" -8.929648 10.61683 -0.841084 0.4007
PLAYER="Hoyer" -0.959183 9.887752 -0.097007 0.9228
PLAYER="Jones" 18.43098 18.44466 0.999258 0.3182
PLAYER="Kaepernick" -11.12796 10.62202 -1.047631 0.2954
PLAYER="Keenum" 2.298640 12.60277 0.182392 0.8554
PLAYER="Luck" -15.32934 11.10117 -1.380876 0.1680
PLAYER="Mallett" -22.62593 12.55291 -1.802445 0.0721
PLAYER="Manning" -0.376617 7.950610 -0.047370 0.9622
PLAYER="Manuel" -7.262659 18.45939 -0.393440 0.6942
PLAYER="Manziel" -2.063938 11.18920 -0.184458 0.8537
PLAYER="Mariota" 3.265216 9.589896 0.340485 0.7336

23
PLAYER="McCarron" 14.42546 13.88545 1.038890 0.2994
PLAYER="McCown" 6.574654 10.66597 0.616414 0.5379
PLAYER="Mettenberger" -14.62578 13.70414 -1.067252 0.2864
PLAYER="Moore" -22.72978 15.42479 -1.473588 0.1413
PLAYER="Newton" 10.17304 8.678193 1.172253 0.2417
PLAYER="Osweiler" 2.525559 11.00648 0.229461 0.8186
PLAYER="Palmer" 17.01549 8.818519 1.929518 0.0543
PLAYER="Rivers" 5.024797 8.674747 0.579244 0.5627
PLAYER="Rodgers" 7.210895 8.713658 0.827539 0.4084
PLAYER="Roethlisberger" 9.488349 9.418671 1.007398 0.3143
PLAYER="Romo" -15.98242 13.69918 -1.166670 0.2440
PLAYER="Ryan" -1.170946 8.666539 -0.135111 0.8926
PLAYER="Sanchez" 3.003228 18.38199 0.163379 0.8703
PLAYER="Schaub" -10.06957 18.39075 -0.547534 0.5843
PLAYER="Smith" 8.479386 8.704069 0.974186 0.3305
PLAYER="Stafford" 6.561148 8.711050 0.753198 0.4517
PLAYER="Tannehill" 2.333744 8.689242 0.268579 0.7884
PLAYER="Tanney" 21.84514 25.26672 0.864582 0.3877
PLAYER="Taylor" 16.19806 9.070442 1.785808 0.0748
PLAYER="Vick" -5.070416 18.38880 -0.275734 0.7829
PLAYER="Weeden" 3.636304 12.59384 0.288737 0.7729
PLAYER="Wilson" 20.39872 8.734421 2.335440 0.0200
PLAYER="Winston" -3.370929 8.673293 -0.388656 0.6977
PLAYER="Yates" -3.726698 25.26546 -0.147502 0.8828
Thisregressionpostsalotof mixedsigns.There are quite afew quarterbackswithanegative
coefficientaswell asquite afewquarterbackswithapositive coefficient. Again,the coefficienttellsus
that if we keptwindspeed,temperature,andhumiditythe same forall of the quarterbacks,theywould
performthatmuch betteror worse onthe ratingthan Bortles.Whenlookingatquarterbackslike Russell
Wilson(20.39), Tom Brady(15.29), andRyan Mallett(-22.62), we can draw a couple different
conclusions.If those three weathervariableswerethe same foreveryquarterback,Russell Wilsonwould
receive about20.4 pointsmore on hisratingthan Bortles,Bradywouldreceive about15.3pointsmore
on hisratingthan Bortles,andMallettwouldactuallyreceive about22.6 pointslessonhisratingthan
Bortles. Thisisuseful forteamswhoare lookingfora quarterbackinfree agencyor inthe draft.
Comparingthe quarterbacksthattheyhave withwhatquarterbackstheycan getis extremelyuseful.If a
playeristhoughtto be verygood butactuallypostsa negative coefficientinthisregression,itmaynot
be worth takinga chance on him. Runningregressionslikethese canbe extremelyusefulandeven
unheardof for teams. We can now see whatquarterbacksare oftenoverlooked,whatquarterbacksare

24
a bit overrated,oreven if quarterbacksare performingatthe level thatisexpectedof them. Iwas
extremelygladthatI decidedtoruna few spreadsheetsthroughEViews onweather-performance,as
theyreallyshowedme howquarterbackscanbe comparedto one another. ThisisdefinitelydataIcan
use inthe future,anddata that I can hopefullyshow teamsone day.
Conclusion
Thisindependentstudyopenedmyeyesfurthertohow analyticscouldbe usedinfootball.Right
now,the onlysportto heavilyuse analyticsinstrategyisbaseball.Iwantto be a part of the future of
analyticsinfootball,anditstartswiththisindependentstudy.Iwasable to furthermyknowledge on
EViews,asIpreviouslyhadbeenconfusedbyitduringSPM300-Sport Data Analysis. Iplanonusingthe
software inthe future.
I was firstable tolookat salary andQB performance,andmake conclusions fromthere.Overall,
I foundthat the betterthe QB plays,the more moneyhe will typicallymake.Ialsofoundoutthatthe
more pass interference callsthatthe QBforces,the more moneyhe will typicallybe rewarded.Thisis
oftenoverlooked. Thistopicallowsme inthe future tolookat QB performance asa whole and
determine howmuchaplayershouldbe rewarded,orevenhow muchlesstheyshouldbe rewarded.
I was thenable totake those same spreadsheetsandaddage and experience tothem.Iran
multiple regressionsonEViewsandfoundouta couple things:the olderinage a playergets,the better
he typicallyperforms.There isa“peakage”for salary,and itis whena playerisabout24. For
experience, the more experiencedaplayeris,the better he typicallyperforms.There isalsoa“peak
age” forsalary,and it iswhena playerisabout5 yearsintothe league. Thistopicallowsme inthe future
to factor age and experience tomyothervariables,anddetermine basedoff of the player’sage or
experience howmuchmoneytheyshouldmake orhow well theyshouldperform.

25
I finallywasable toswitchgearscompletelyandfocusona new topic.I went intoweatherand
performance,andtriedtosee if a relationshipexisted.Ifoundouta couple things fromthere.The
warmerthe weathergets,the betterthe quarterbackperforms.Inwarmervenueslike Miami,Tampa,or
JerryWorldin Dallas, a quarterbackshouldthrow the ball more.Humidityhadrelativelynoeffectona
QB’s performance.Precipitationand windspeeddecreasedQBperformance.Inrainier/snowier
conditionslike Seattle,GreenBay,orBuffalo,aquarterbackshouldexercisecautionandhanditoff to
hisrunningback more.Inwindiercitieslike Chicago,aquarterbackshouldgetthe ball outof the air
more and ontothe ground.Windstypicallytake ballsthe wrongwayandintodefenders’arms. Sealevel
pressure increasedQBperformance.Inhigherelevatedvenues,suchasDenver,quarterbacksshould
throwthe ball a bitmore,as performance tendstoincrease. Thiswillbe helpful forme inthe future for
whenwe make game plans.Justsimplylookingatthe weatherforecastcancreate a winor two. After
that, I wasable to lookat individual quarterbacksandcompare themwhilekeepingweathervariables
(windspeed,temperature,humidity) the same.Idroppedone of the quarterbacks(Bortles) and
comparedeveryotherquarterbacktohisstatistics. Iwas able todraw conclusionsasto which
quarterbackswere betterthanothersbasedoff of coefficients.
I cannot waitto see whatthe future hasin store for football analytics,andIam hopeful thatthis
independentstudycanreallygive me aliftinthe sportanalyticsworld.Ihad a blastlearningthe insand
outsof EViews,aswell asfurtheradvancingmyknowledge inexcel. Icannotwaittopresentthis
researchto mypeersand otherteams.

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