Botta Daniels Bahill Zachman Framework Pop w Bb Models Thrust-Clarify

8/13/2019 Botta Daniels Bahill Zachman Framework Pop w Bb Models Thrust-Clarify

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Keynote address at Asia-Pacific Congress on Sports Technology held at the Tokyo Institute of Technology inSeptember 200!

The Zachman Framework

Populated with Baseball Models

Rick Botta1Jesse Daniels1and Terry Bahill1,2,*

"#A$ S%ST$&S' "0(20 Technology Place' San )iego' CA (2"2*

2Systems and Industrial $ngineering' +ni,ersity of Ariona' Tucson' A. /*2"-0020

Author to 1hom correspondence should be addressed e-mail terry3sie!ariona!edu4!

5unning head6 &odels of #aseball

Key 1ords6 systems analysis' enterprise architecture' science of baseball' modeling methodology

ABSTRACT

There are guidelines for making good models! Amongst these are frame1orks' 1hich help

people organie integrated models of their enterprises! This organiation helps ensure

interoperability of systems and helps control the cost of de,eloping systems!The .achmanframe1ork for enterprise architecture is a si7 by si7 classification schema' 1here the si7 ro1s

represent different perspecti,es of the enterprise and the si7 columns illustrate different aspects!

In this paper' a .achman frame1ork is populated 1ith models for #aseball! These models should

be easy to understand 1ithout a steep learning cur,e! &ost of the cells in this e7ample are filled

1ith 8uantitati,e simulatable models that ha,e been published in peer re,ie1ed 9ournal papers!

The other cells are filled 1ith simple thought models! :ac8ues #arun "(;4 1rote'


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e7amples a,ailable in the public domain illustrating a completeset of models that comply 1ith

the .achman frame1ork! This paper 1ill attempt to fill a complete frame1ork for the game of

#aseball! +se of the #aseball as an e7ample allo1s us to use non-proprietary

models that can be understood by a large number of people 1ithout a steep learning cur,e! A

limitation of this paper is that 1e do not model the entire game of baseball due to the

comple7ities in,ol,ed and the number of models that 1ould be re8uired!

The concept of e7amining a system at many orders of magnitude 1as presented by #oeke

B"(*D! Gater Charles and 5ay $ames B"(E/D created a film of this concept and finally Philip and

Phylis &orrison B"(**D popularied the idea 1ith the bookPowers of Ten! 5ouse et al! B"((2D

added the other dimension =hat' o1 and =hy4 to make it a matri7! o1e,er' these authors

treated the decomposition as more and more le,el of detail! .achman made it different

perspecti,es on the enterprise! e also added the other aspects of =ho' =hen and =here!

?rame1orks pro,ide an organiational schema by 1hich enterprises can be de,eloped!

o1e,er' they alone are not sufficient! A complete enterprise engineering de,elopment toolkit

1ould be comprised of a frame1ork' a process such as the I#& 5ational +nified Process or

SI&IGA5B#ahill and Hissing' "((/D4' a method B&artin' "((*D' a notation such as +&G4 and

tools such as I#& 5ational 5ose' $nterprise Architect' or a mass spectrometer4!

Todays businesses are comple7! The systems that implement those business processes are

e8ually comple7! #ut people ha,e a hard time agreeing on ho1 to define comple7ity! Jne 1ay to

define comple7ity is as a measure of ho1 1ell 1e can predict e7pected performance! The more

comple7 a system is' the harder it is to predict actual performance! =e can deal 1ith comple7ity

and performance predictions using integrated models and simulations of a business! ?rame1orks

can help organie these models into multiple le,els of abstraction to better understand ho1 the

business rules' organiational strategies and resources are turned into a physical system! This

paper has more to do 1ith the concept of frame1orks and integrated models for dealing 1ith

comple7ity than it does 1ith the game of baseball! Then 1hy should 1e model baseball using a

.achman4 ?rame1ork =e can use it as a 1ay to easily con,ey the concept of integrated

models and simulations 1hen de,eloping enterprise and system architectures that is independent

of any business domain! #ecause most people understand baseball' 1hich itself is a comple7

enterprise!

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$ach cell in the schema contains at least one model or artifact! A primiti,e model

consists of information specific to a single column!

Table I illustrates the .achman frame1ork schema! +sing common stakeholders as kno1n

from the building industry' the ro1s of the schema pro,ide multiple perspecti,es of the o,erall

architecture from the points of ,ie1 of the planner ro1 "4' the o1ner ro1 24' the designer or

architect ro1 F4' the builder ro1 ;4 and subcontractors ro1 4! The columns present a

classification of the ,arious items of interest' or dimensions' of the architecture from each of

those perspecti,es!

BTable I goes here!D

2.1 The Ro)s

The follo1ing description of the .achman frame1ork ro1s is based on a Soft1are $ngineering

Institute document http6111!soft1are!orgpubafcachman!asp! The frame1ork contains si7

ro1s and si7 columns yielding FE uni8ue cells as sho1n in Table I! The ro1s represent different

perspecti,es ,ie1s4 and roles of the enterprise!

Scoedescribes the systems ,ision' mission' conte7t' boundaries' architecture and

constraints! The scope states 1hat the system is to do! It is called a black bo7

model' because 1e see the inputs and outputs' but not the inner 1orkings! This

ro1 is also referred to as the Conte7t ro1!

B+siness odeldefines goals' strategies' structure and processes that are used to support

the mission of the system or enterprise! This ro1 is also referred to as the Concept

ro1!

Syste odel contains system re8uirements' ob9ects' acti,ities and functions that

implement the business model! The system model states ho1 the system is to

perform its functions! It is called a 1hite bo7 model' because 1e see its inner

1orkings! This ro1 is also referred to as the Gogical ro1!

Technolo-y odelconsiders the constraints of humans' tools' technology and materials!

This ro1 is also referred to as the Physical ro1!

Detailed reresentationpresents indi,idual' independent components that can be

allocated to contractors for implementation! This ro1 is also referred to as the

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Jut-of-Conte7t ro1 since models in this ro1 are typically so detailed that they are

essentially 1ith models in the upper ro1s of the frame1ork!

Real systedepicts the operational system or the sli,er of that system4 that is under

consideration!

The ro1s do not represent a physical decomposition of the system! @or do they portray

finer and finer le,el of detail! 5ather the ro1s sho1 different perspecti,es or ,ie1s4 of the

enterprise!

Table II from the .achman Institute 1eb site 111!ifa!com4 sho1s an e7ample of these

perspecti,es for a house! The left column identifies the perspecti,e and defines the role in the

enterprise that 1ould o1n that perspecti,e! Some modelers ignore the perspecti,e and focus on

the roles! Jthers ignore the roles and focus on the perspecti,e! In this paper' 1e 1ill try to co,er

both the perspecti,e and the roles!

BTable II goes here!D

&any roles are possible' such as Planner' J1ner' Architect' )esigner' #uilder' C$J'

President' )irector' Chief Systems $ngineer' Program &anager' Systems $ngineering Team'

Systems Analyst' 5e8uirements $ngineer' +se Case $ngineer' +ser Interface )esigner'

Architect' Component $ngineer' Test $ngineer' System Integrator' Integration Tester' System

Tester' Assembler' Technician' &achinist' +ser and &aintainer! The identified roles 1ill be the

primary stakeholders for that ro1 ,ie14!

?or the #aseball e7ample in this paper' the o1ner of and the person most interested in4

the scope models in ro1 " 1ill be an $7ecuti,e' like the Commissioner of #aseball! The o1ner

of the business models in ro1 2 1ill be an J1ner of a baseball team! The o1ner of the system

models in ro1 F 1ill be a Heneral &anager of a baseball team! The o1ner of the technology

models in ro1 ; 1ill be a Team &anager! The o1ner of the detailed models in ro1 1ill be the

Scientist' $ngineer or Coach 1ho created each model! And finally' the o1ner of the real system

in ro1 E 1ill be an indi,idual ma9or-league #aseball Player! The o1ner should be the role thatcontrols the flo1 of money in that ro1!

2.2 The Col+ns

I Keep six honest serving men

(They taught me all I knew):

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Their names are What and Why and When

And How and Where and Who!

"rom The Elephants Child#y $udyard Kipling %&'*

In 9ournalism class' 1e 1ere taught to start e,ery story 1ith 1ho' 1hat' 1hen' 1here' 1hy and

sometimes ho1! This is also good ad,ice for understanding a system! The columns of a .achman

frame1ork present these aspects of the enterprise!

hat data/ describes the entities that are considered important to the business' as

,ie1ed from each perspecti,e! These entities are the things for 1hich information

is to be maintained! $7amples include e8uipment' business ob9ects and system

data!

(o) 0+nctions/ defines the functions' or acti,ities' the enterprise is concerned about

relati,e to each perspecti,e! Inputs and outputs are also considered in this column!

here net)orks/sho1s geographical locations and interconnections bet1een acti,ities

1ithin the enterprise! This includes ma9or business geographical locations'

net1orks and the playing field!

ho eole/represents the people 1ithin the enterprise and metrics for assessing their

capabilities and performance! The design of the enterprise has to do 1ith the

allocation of 1ork and the structure of authority and responsibility! This column

also deals 1ith human-machine interfaces and relationships bet1een people andthe 1ork they perform!

hen tie/represents time' or the e,ent relationships that establish performance

criteria! This is useful for designing schedules' the processing architecture' the

control architecture and timing systems!

hy otiation/describes the moti,ations of the people and the enterprise! This

re,eals the enterprise goals' ob9ecti,es' business plan' kno1ledge architecture'

and reasons for thinking' doing things and making decisions! It is concerned 1ith

ho1 the goals and strategies are translated into specific ends and means!

2. (ori3ontal and 4ertical %nte-ration

The models organied by ro1 and column in the .achman frame1ork should be horiontally and

,ertically integrated! This means that you should not 1ork models in a gi,en cell 1ithout

considering impacts to other cells in the same ro1 and in the same column! As an e7ample of

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horiontal integration' consider a functional model de,eloped in the o1 column! The

inputsoutputs' resources' deployment' time constraints' and goals related to each function should

be considered in the =hat' =ho' =here' =hen' and =hy columns' respecti,ely' in the same

ro1! As an e7ample of ,ertical integration' consider a re8uirement in the Physical ro1' =hy

column for the performance of some aspect of a soft1are component! This re8uirement should

be linked to other moti,ational models in higher ro1s' such as an ob9ecti,e re8uirements

document in the Concept ro1' =hy column! This is nothing more than re8uirements traceability

and is a common best practice for systems engineering! Through this traceability' 1e call the

re8uirement in the Physical ro1 ,ertically integrated 1ith the re8uirements in the Concept ro1!

These impacts and traceabilities can be modeled 1ith dependency relationships!

This means that no cell is an island! $ach cell is fundamentally related 1ith other cells in the

same ro1 and same column! ?rom a practical standpoint' information in multiple columns is

typically disco,ered simultaneously 1hile constructing models in a gi,en ro1! It is suggested

that this information be captured in models in the appropriate cell!

$ach ro1 ,ie14 has at least one role stakeholder4! That stakeholders concerns 1ould be the

primary focus of the Whycell in that ro1!

. BASEBA'' E5AM6'E

@o1 1e 1ill take models from #aseball and sho1 1here they fit 1ithin the conte7t of the

.achman frame1ork! =hen possible' 1e selected models that ha,e been published in peer-

re,ie1ed 9ournals! Although in Tables I to IN 1e print the cells from top to bottom' 1e think

they are best read and discussed from bottom to top for the purpose of this paper!

#ote to reie)ers7 The follo1ing material is duplicated in the Tables on pages FF to F/! =e ha,e no

desire to publish both formats! =e are asking the re,ie1ers to e7press their preferences!

Col+n 1, hat data/. The physical product data item4 depicted in column " is the baseball

batM column 1 (what), row 6 (real system)!

There are many models for a baseball bat that e7plain the Center of Percussion CoP4'

moment of inertia &oI4' coefficient of restitution Co54' etc BAdair' "((;M Cross' "((/M @athan'

2000 and 200FM Sa1icki' ubbard and Stronge' 200FM #ahill' 200;D! ?rom the perspecti,e of a

bat manufacturer' this detailed representation of the bat can be represented as a model depicting

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the re8uired length' the taper of the handle' the 1idth of the barrel' the bat 1eight' etc! This

model maps to column1 (what), row 5 (detailed representation).

The s1ing of a bat can be modeled 1ith a translation and t1o rotations' one about the batters

spine and the other bet1een the t1o hands B#rancaio' "(/*M =atts and #ahill' 2000DM column 1

(what), row 5 (detailed representation).

There is an ideal bat 1eight and a best 1eight distribution for each batter B#ahill and

Karna,as' "(/( and "(("M #ahill and &orna ?reitas' "((M #ahill' 200;D! The team helps the

indi,idual select and ac8uire the right batM column 1 (what), row 4 (technology model)!

$ach organiation pro,ides facilities for batting practice' conditioning and skills

de,elopmentM column 1 (what), row (system model)!

The @ational Collegiate Athletic Association @CAA4 controls college sports! In this role' it

has created rules go,erning the allo1ed dimensions and performance of aluminum bats! ?or

e7ample' the bat shall not 1eigh less in ounces4 than its length in inches4 BCrisco' "((*6

@athan' 200FD! ?inancial models could be in e,ery column! Those appropriate for column "

include cost of e8uipment such as the bats' cost of training facilities and cost of physical

conditioning e8uipmentM column 1 (what), row ! ("usiness model)!

$aston Sports and illerich O #radsby Company gi,e 1ooden bats to ma9or league players

for free! =hy To build their brand image' so that they can sell more of their regular sports

e8uipmentM column 1 (what), row ! ("usiness model)!

The creator of baseball' until recently belie,ed to be Abner )oubleday' 1ould ha,e listed

rules' bats' balls' players' and fields among the list of things important to the game! The rules of

ball-and-stick games baseball' softball' cricket' tennis' etc!4 are 1ritten to challenge the

physiological limits of the human in many dimensions B5egan' "((2D! The bat is regulated to

make the game e7citing' but traditionalM column 1 (what), row 1 (scope)!

#y considering other ball-and-stick games' 1e are testing and defining the scope of our

chosen enterprise!

=e do not ha,e enough room to co,er all of #aseball! That 1ould take thousands of models!

Therefore' 1e only sho1 sli,ers of #aseball! ?or e7ample' in column "' 1e only looked at the

baseball bat! In contrast' 1e could ha,e looked at the ball' or the bat and the ball! =e used a

different sli,er for each column' but e,en at that' 1e did not restrict our models to only that one

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sli,er! ?or e7ample' sometimes 1e talk about ma9or league baseball and sometimes 1e talk about

uni,ersity baseball go,erned by the @ational Collegiate Athletic Association @CAA4!

Table III sho1s the 1hole .achman frame1ork for #aseball! =e no1 continue to describe

the models cell by cell!

BTable III goes here!D

Col+n 2, (o) 0+nction/. The acti,ity modeled in column 2 is one pitch and peoples

response to itM column ! (how), row 6 (real system).

Jnce the ball is in the air' the mo,ement of the pitch depends only on ,elocity' spin rate and spin

a7is B=atts and #ahill' 2000M #ahill and #ald1in' 200;DM column ! (how), row 5

(detailed representation).

T1o strategies are used by the batter for tracking the pitch using the saccadic and smooth pursuit

eye mo,ement systems B#ahill and Ga5it' "(/;M &cugh and #ahill' "(/DM column !

(how), row 5 (detailed representation).

A neurophysiological model sho1s ho1 the batter predicts 1here and 1hen the ball 1ill cross

the plate B#ahill and! Karna,as' "((FM #ahill and #ald1in' 200;DM column ! (how), row 5


+nderestimating the pitch speed can induce the perceptual illusion of the rising fastball

BKarna,as' #ahill and 5egan' "((0M #ahill and! Karna,as' "((FDM column ! (how), row 5

(detailed representation)!

Team1ork and signals enable the manager' the batter and the runners to e7ecute tactics such as

hit and run' bunt' steal' take the pitch' s1ing a1ay' etc!M column ! (how), row 4

(technology model).

The pitcher pitches the ball! The batter s1ings and hits the ball! e runs to1ard first base! $tc!

Jur acti,ity diagram of ?igure " sho1s one pitch and subse8uent acti,itiesM column !

(how), row 4 (technology model).

B?igure " goes here!D

Stadiums can be e8uipped 1ith a ,ariety of optional e8uipment that can record and playback the

pitch' such as the multiple tele,ision cameras used to aid the umpires

B111!uesTec!comD and the stadium instant replay screens for the benefit of the players

and spectatorsM column ! (how), row (system model).

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Armstrong "((/4 applied the Capability &aturity &odel to impro,e the performance of his

softball teamM column ! (how), row (system model).

The umpire calls balls and strikes! o1e,er' there has been ,ariability in the strike one! To

reduce this ,ariability' multiple tele,ision cameras track each pitch and computers

reconstruct the tra9ectory of each pitch B111!uesTec!comD! After the game' the

umpires decisions are compared to the computers decisions! This feedback helps the

umpires impro,e their decision-makingM column ! (how), row ! ("usiness model).

&a9or Geague #aseball Inc! defines the strike one and manages umpiresM column ! (how), row 1

(scope).

The rules of baseball e,ol,ed o,er its first 0 years' but ha,e been relati,ely constant o,er the

last centuryBHould' 200FDMcolumn ! (how), row 1 (scope).

Col+n , here net)ork/. The topic of column F is the baseball fieldM column (where),

row 6 (real system).

The human brain does not ha,e 7' y and coordinates of ob9ects! umans must track ob9ects

using neurophysiological parameters! As a result outfielders run a cur,ed path 1hen

tracking do1n fly balls B&c#eath' Shaffer and Kaiser' "((M Shaffer and &c#eath'

2002DM column (where), row 5 (detailed representation)

#atters must predict 1here and 1hen the ball 1ill cross the plate BKarna,as' #ahill and 5egan'

"((0M #ahill and #ald1in' 200;DM column (where), row 5 (detailed representation)!

#efore e,ery pitch all fielders mentally rehearse 1here they 1ill thro1 the ball if they recei,e a

ground ball or a fly ball and they establish understandings 1ith nearby fielders about

1here each player 1ill goM column (where), row 4 (technology model)

The placement in the stadium of home plate effects the area behind the plate' the design of

protecti,e netting' the orientation to the sun' the distance to the fences and therefore

safety and playing performanceMcolumn (where),row (system model)!

Stadiums can be designed for baseball only or they may be shared by baseball' football and other

e,entsM column (where), row ! ("usiness model).

#aseball is played in stadiums and broadcast on tele,isionM column (where), row 1 (scope).

The teams are organied into leagues and di,isions according to geographyM column (where),

row 1 (scope).

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Col+n 8, ho eole/. The people modeled in column ; are ma9or-league baseball playersM

column 4 (who), row 6 (real system)!

The physiological state of indi,idual players determines 1hether and ho1 1ell they play an

indi,idual game' or if they are put on the disabled listM column 4 (who), row 5 (detailed

representation)!

The count is the model for ho1 the batter is doing during an at-batM column 4 (who), row 5


)efining and locating the s1eet spot of the bat is a human-machine interface problem6 the teams

help indi,iduals understand this issue B#ahill' 200;D! The s1eet spot of the bat is four

inches 1ide' but only one-third of an inch high! This 1as determined using a ne1

criterion function of getting a hit' rather than the old criterion function of getting a home

run B#ald1in and #ahill' 200;DM column 4 (who), row 4 (technology model)!.

In section 2!F' 1e discussed horiontal and ,ertical integration of models! The t1o pre,ious

paragraphs offer an opportunity to e7plore this for the baseball models! In basic research' one

team often uses the results of another team' but they seldom use the actual products! An

e7ception to this 1as the model of #ald1in and #ahill B200;D! This model used ne1 programs as

1ell as the e8uations and the actual programs of @athan B200FD' Sa1icki' ubbard and Stronge

B200FD' and #ahill and Karna,as B"((FD! #ecause the models 1ere not designed to be used by

others' 1e encountered the follo1ing interoperability problems6 SI ,ersus $nglish units' right-

handed ,ersus left-handed coordinate systems' bat mass ,ersus effecti,e bat mass' different

nomenclature' and most seriously lack of stated assumptions

Indi,idual player performances are published daily in the bo7 scores in the sports sections of

ne1spapers! Player a,erage performances are published 1eeklyM column 4 (who), row 4

(technology model).

Indi,idual player statistics' &arko, models and manager decisions such as batting order4 are

used to simulate games and seasons6 this is called fantasy baseball B#urkiet' arold and

Palacios' "((*M 111!stats!comD! Scouts make obser,ations and e,aluations of players

performances and report this information back to their organiationsM column 4 (who),

row (system model)!

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The Heneral &anager H&4 creates the 2-man roster and trades players to impro,e it! It must

contain a balance of players at each position' including short relie,ers' long relie,ers'

etc!M column 4 (who),row (system model)!

#illy #eane' Heneral &anager H&4 of the Jakland Athletics' e,aluates the 1orth of players

1ith inno,ati,e high-le,el metrics' and he has the most successful lo1-salary team in the

ma9or leagues BGe1is' 200FD! The H& must consider player positions' salaries'

performance' etc!M column 4 (who), row (system model)!

Heorge Steinbrenner' o1ner of the @e1 %ork %ankees' e,aluates the 1orth of his players 1ith

traditional metrics' and he has the most successful high-salary team in the ma9or leagues!

The team o1ner must consider return on in,estmentM column 4 (who), row ! ("usiness

model).

Creating rules for selecting members of the Jlympic team effects the enterpriseM column 4 (who),

row ! ("usiness model).#ecause ro1 2 e7amines the system boundaries' it 1ill often

contain models that are outside of the organiation!

#efore the 200" season began' the o1ners of the Te7as 5angers created a ten-year Q22 million

contract for Ale7 5odrigue A-5od4! Their comple7 contract analysis included pro9ected

team re,enue' team performance and franchise ,alue as 1ell as salary' bonuses' ta7es and

contract insurance! Applying an /R discount rate to the re,enues and e7penses' they

obtained a net present ,alue of negati,e Q"!F million' 1hich is close to break-e,en! So

they signed the contract BCohen and =allace' 200FM Cohen' 200FDM column 4 (who), row

! ("usiness model).

The Commissioner of #aseball coordinates the teamsM the &a9or Geague #aseball Players

Association and players agents orchestrate the acti,ities of the players! The

Commissioner must consider salary caps' retirement plans' drug testing' re,enue sharing

and the reser,e clauseM column 4 (who), row 1 (scope).

Col+n 9, hen tie/. The fundamental unit of time in a baseball game is one pitchM column

5 (when), row 6 (real system)!

The batters mental model for the pitch is based on the last one or t1o pitches or perhaps on the

last 20 seconds B#ahill and #ald1in' 200;M Hray' 2002 and 200FDM column 5 (when), row

5 (detailed representation).

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A successful tactic of pitchers is B#ahill and

#ald1in' 200;DM column 5 (when), row 5 (detailed representation)!

Pitch count - pitchers are often remo,ed after' say' "20 pitchesM column 5 (when), row 4

(technology model).

Sometimes pitching rotations are planned' but sometimes they are merely a default' e!g!' column 5 (when), row (system model)

Tele,ision net1orks determine the starting times of many gamesM column 5 (when), row

(system model).

Season schedules for all of the teams are comple7 because of the many constraintsM column 5

(when), row ! ("usiness model)

)ecisions must be made about interleague play' playoff structure' e7pansion teams' etc!M column

5 (when), row 1 (scope).

Col+n :, hy otiation/. Column E concerns 1hy people think the things they do and

make the decisions they doM column 6 (why), row 6 (real system)!

=hy does the pitcher decide to thro1 a fastball' a slider' a cur,eball or a changeupcolumn 6

(why), row 5 (detailed representation).

Critical flicker fusion fre8uency C???4 e7plains 1hy pitchers think there is a difference

bet1een the t1o-seam and the four-seam fastballs although physics sho1s no difference

B#ahill and #ald1in' 200M #ahill' 200DM column 6 (why), row 5 (detailed

representation).

#atters use many heuristics to decide 1hat to do! Among them is' 1ith a F-0 count e7pect a

fastball because the pitcher 1ill ha,e the greatest confidence in thro1ing it for a strike

B=illiams and +nder1ood' "(/2M #ahill and #ald1in' 200;DM column 6 (why), row 5

(detailed representation)!

The manager moti,ates his players by kno1ing 1hen blame and 1hen not to B#ald1in' 200"DM

column 6 (why), row 4 (technical model).

=hy does a manager decide to pitch to a famous slugger rather than intentionally 1alk him In

making this decision the manager considers the score' runners on base' batting a,erage'

slugging a,erage' etc! B5eiter' 200;D# column 6 (why), row 4 (technical model).

Heneral &anagers trade players in order to ha,e a 1inning season 1ithin their constraints! They

are moti,ated by their dri,e for success# column 6 (why), row (system model).

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orchestrate players acti,itiesM =hen' chronologyM decisions about interleague play' playoff

structure' 1orld series datesM and =hy' the purpose of baseball is entertainment! These models

are also at e8ui,alent le,els of abstraction! An e7ample of bad granularity for ro1 " of this

frame1ork 1ould be using a team manager or the president of the +nited States for =ho!

To help 1ith horiontal granularity' you could consider time scales as 1ell as

perspecti,es and roles! In this #aseball e7ample' the models in ro1 " should run 1ith time scales

of a fe1 years! The models in ro1 2 should deal 1ith seasonal e,ents! The models in ro1 F

should ha,e time scales on the order of one game! The models in ro1 ; should ha,e time scales

of one at-bat! And the models in ro1 should ha,e time scales of one s1ing!

It might be desirable to ha,e the same real system in each column! In Table III' 1e did

not ha,e room to co,er all of #aseball! That 1ould take thousands of models! =e only sho1ed

sli,ers of #aseball! ?or e7ample' in column "' 1e only looked at the baseball bat and this

produced the rules about baseball bats in ro1 "! In contrast' 1e could ha,e looked at the ball' or

the bat and the ball! If 1e had looked at all possible sli,ers' then ro1 " 1ould ha,e contained all

baseball rules! 5o1 E had e,en granularity' but each column looked at a different thing' e! g!

column " had the bat' column F had the field and column ; had the baseball players! ?or a more

uniform frame1ork 1e could ha,e looked at =hat' the bat and ballM o1' the pitch and collisionM

=here' the pitching rubber' the flight path' the plate' and the batters bo7M =ho' the batterM

=hen' the duration of the pitch and s1ingM and =hy' to out1it the opponent!

$7amining the granularity of a frame1ork is a 1ay of ,alidating the frame1ork!

Modelin- $eneralities.=hen architecting an enterprise' either generate models that

address all of the cells' or document rationale for 1hy certain cells 1ere left out! This best

practice ensures that there is complete co,erage and that the modelers ha,e at least considered

each and e,ery aspect and perspecti,e of the problem in the conte7t of the frame1ork! In other

1ords' this helps ensure that nothing has been inad,ertently ignored! In our e7perience' 1e ha,e

found that complete co,erage is typically necessary in the top three ro1s!

ere are t1o additional generaliations about modeling! All elements in the same model

should be at the same le,el! &odels should e7change inputs and outputs only 1ith other models

of the same le,el' or maybe one le,el higher or lo1er!

Consider the #atter in the acti,ity diagram of ?ig! "! =e could model the state of his mind

1ith the follo1ing attributes and states6

""2E20"F "E


17/43


e


18/43


In going from the bottom to the top' ro1 models indi,idual componentsM ro1 ; models the

interfaces bet1een these components and the resulting subsystems of ro1 ;M and ro1 F models

the interfaces bet1een the subsystems of ro1 ; and the resulting systems of ro1 F!

Methodolo-y! A process or methodology should be follo1ed 1hen generating models

according to a frame1ork! The frame1ork by itself is simply not enough for practical

implementation! The frame1ork can and should be used to tailor and structure a process so that

the process prescribes that models be built to sufficiently address each aspect and perspecti,e in

the frame1ork! Process also can enforce ,ertical and horiontal integration and consistency

among models!

To do)n or ;otto += In this paper' 1e discussed the models from bottom to top!

That is the 1ay many of these models 1ere deri,ed' because in basic research' ne1 models are

built on pre,ious research! o1e,er' in designing ne1 systems or modeling e7isting systems'

better results 1ill usually be obtained 1ith a top-do1n approach! Architects and Systems

$ngineers start at the top and 1ork do1n! =hereas discipline engineers and physicists usually

start at the bottom and 1ork up! Soft1are engineers often start in the middle 1ith use cases'

ob9ects and class diagrams! People usually start at the le,el that they are most familiar 1ith!

+nfortunately' this means different people may be 1orking at different le,els! Therefore' not

e,eryone 1ill be designing to the same re8uirements! .achman does not impose any order in

1hich the ro1s or columns are populated! The decision of the order in 1hich ro1s and columns

are addressed is in the domain of the methodology follo1ed by the architecting team and not the

frame1ork itself!

8.. >sin- the Frae)ork as an Assessent Tool.

In addition to organiing the de,elopment of ne1 architecture products' the .achman frame1ork

has also been sho1n to be useful as a general assessment tool! The frame1ork' as a normalied

schema for organiing a complete and holistic set of architecture descriptions can be effecti,ely

applied to assess e7isting artifact sets' or e,en determine an optimal skills mi7 for architecture

de,elopment! =hen first embarking on an architecture de,elopment effort' it is useful to gather

e7isting models and documentation about the sub9ect of the architecture and arrange them into

appropriate cells 1ithin the frame1ork! This gi,es the architecture team an idea of the e7tent to

1hich e7isting models' graphics' documents' etc!' pro,ide co,erage in terms of a holistic

description of the architecture! The results of this mapping of e7isting information into the

""2E20"F "/


19/43


20/43


If a young researcher 1ants to do research in #aseball' then he or she had better

understand the funding mechanisms' the flo1 of money! Jne 1ay to do this is to create a

collection of models for the #aseball enterprise that comply 1ith the .achman frame1ork!

Jn the other hand' if a young engineer 1ants to start a research program in Jptimiation

&ethods for 5outing Problems on @et1orks 1ith Stochastic ?ailures' then he or she should

populate a .achman frame1ork for the Jptimiation &ethods for 5outing Problems on

@et1orks 1ith Stochastic ?ailures enterprise!

8.8. Content and Arran-eent o0 the Cells

The ri-ht tool.There is no correct modeling tool for any particular cell! ?or each cell' use 1hich

e,er modeling tool is most appropriate' e! g!' physical analogs' analytic e8uations' state

machines' functional flo1 block diagrams' block diagrams of linear systems theory' transfer

functions' state-space models' differential or difference e8uations' ob9ect-oriented models' +&G

diagrams' &onte Carlo simulations' statistical distributions' graphical animated4 simulations'

mathematical programming' &arko, processes' time-series models' financial models' Pert charts'

Hantt charts' computer programs' use cases or mental models!

$ach entity in a cell should be a model a system4 in its o1n right! That means it should

ha,e defined inputs' functions' outputs' states' ob9ects' figures of merit' metrics' technical

performance measures' interfaces' etc! If a different modeling tool is used for each model' then

the interfaces 1ill be harder to design! That is one ad,antage of the +&G6 it allo1s the models to

communicate better!

Col+n order.A ne1spaper article should start 1ith 1ho' 1hat' 1hen' 1here' 1hy and

sometimes ho1' usually in that order' but 1e ha,e seen .achman frame1orks 1ith many

different column orders! In fact' .achman e7plicitly says that the ordering of the columns is not

important! o1e,er' 1e think column order is important! ?or the reasons gi,en in the ne7t t1o

paragraphs4 1e suggest the original .achman column order6 =hat' o1' =here' =ho' =hen

and =hy!

hich cells are ost iortant= To increase the performance of baseball players' the

lo1er-left cells are the most important and the upper-right cells are least important 1ith the

column order that 1e ha,e used4! +&Gdiagrams are also most useful in the lo1er-left corner! In

contrast' a C$J1ould be interested in the top ro1s and in particular the upper-right corner!

&odels in ro1s " and 2 are the domain of the President' C$J' board of directors and rule making

""2E20"F 20


21/43


organiations! &odels in ro1s F and ; are o1ned by managers! 5o1 tasks are performed by

the most engineers!

The Sports $ngineering conference 1as held at the +ni,ersity of California at )a,is in

September 200;! "*2 papers 1ere presented Bubbard' &ehta and Pallis' 200;D! These papers as

1ell as those published by ung and Pallis B200;D 1ere categoried into the cell that best

represented the fundamental characteristic of each paper! This categoriation is sho1n in Table

N! The authors' for the most part' 1ere research engineers! As can be seen' most of the papers

fall into the lo1er left-hand corner! A re,ie1er of this paper 1rote'


22/43


Modelin- ;+siness rocesses.To understand a business process such as modeling and

simulation in a particular company' that company could create a database that contains data that

ans1ers these 8uestions for each model and simulation they ha,e created! Purpose J1ner

Architecture Inputs Jutputs ?unctions Interfaces Interacts 1ith Cost #usiness case

Ge,el =ho =hat =hen =here =hy o1 Standard e!g! +&G' 5+P' CJ5#A4

&odeling tool Simulation tool .achman ro1 .achman column Then they could reduce the

number of classes using affinity analysis! And finally' they could abstract this into a metamodel

that sho1s ho1 that company does modeling and simulation!

As a summary e7ample' Table NI sho1s a .achman frame1ork for another sport' Holf! It

does not ha,e models that 1ere published in peer-re,ie1ed 9ournalsM rather it contains simple

thought models! It should pro,ide another e7ample of the lessons learned of this paper! ?or

e7ample' it repeats the message that there can be se,eral roles per ro1 depending on the sli,er

that you are e7amining' e! g! ro1 " uses City 5ecreation )epartment' +nited States Holf

Association +SHA4' @ational Collegiate Athletic Association @CAA4' Professional Holf

Association PHA4' and a uni,ersity Athletic )irector A)4!

BTables NIa and NIb go here! They should be on facing pages!D

9. S>MMAR?

The .achman frame1ork pro,ides a general si7-by-si7 schema that can be used to

organie and assess completeness of descripti,e representations for any comple7 enterprise such

as an organiation' your customer' a system or a sport! To ensure a complete and holistic

understanding of the enterprise architecture' it is necessary to de,elop models that address the

perspecti,es and interrogati,es that constitute the ro1s and columns' respecti,ely' of the

frame1ork! =hen constructing these models it is important to use 1hiche,er modeling notation

and tool that is most appropriate for con,eying the ,alue of the information captured in the

models! This may lead to a list of entities' a differential e8uation model' a set of )o)A?artifacts

using +&Gor I)$?notation' or may simply be a paragraph describing some aspect of the

enterprise!

?or systems engineering applications' a .achman frame1ork is typically used to organie

models 1hose ultimate purpose is to better understand and communicate the conte7t'

re8uirements' and detailed design for a system or a system of systems that is to be built and

deployed! As a result' there is typically more detail in the models arranged in the lo1er ro1s of

""2E20"F 22


23/43


the frame1ork! These detailed models are used to e7plain to engineers and customers ho1 the

system should be structured and implemented to most effecti,ely realie the capabilities and

re8uirements that are identified by analying models describing the Planner and #usiness J1ner

perspecti,es of the enterprise! Cells in the top ro1s typically contain models that e7press the

organiations ,ision and mission statements' hard technical problems' and concept of

operational! Cells in the lo1er ro1s describe the logical' technical and physical solutions that

ideally pro,ide a better' more re1arding 1ay to conduct business!

&odels de,eloped in a gi,en ro1 should contain information that is roughly at the same

le,el of detail granularity4 as other models in the same ro1! This helps keep the models

understandable and allo1s for better correlation integration4 bet1een models in the same ro1!

&odels organied 1ithin the .achman frame1ork should be horiontally and ,ertically

integrated in terms of ro1s and columns of the schema! This implies that models in a gi,en ro1

should e7hibit consistency in terms of ho1 the primiti,e moti,ational' data' function' people'

net1ork' and time elements are used' re-used' and depicted in the models! Nertical integration

means that models in a gi,en column should demonstrate traceability 1hen mo,ing from one

ro1 to the ne7t! ?inally' the .achman frame1ork can be used as a ,aluable assessment tool to

determine the 8uality of co,erage for e7isting artifacts such as re8uirements and diagrams' as

1ell as identify gaps in skills 1ithin a company or architecture team!

=e ha,e found the .achman frame1ork to be useful! Jur intention in this paper 1as to

e7amine it' e7plain ho1 to use it and look at its strengths and 1eaknesses! =e 1anted to help the

reader to decide 1hether or not the .achman frame1ork 1ould help him or her to organie the

models in his or her business!

AC"#!'ED$EME#T

=e thank )a,e #ald1in' &ark Nriesenga and 5euben Settergren for comments on the

manuscript! This paper 1as partially supported by A?JS5&+5I ?;(E20-0F-"-0F**!

REFERE#CES

5! K! Adair' The Physics of %ase"all.@e1 %ork6 arper O 5o1' "((;!

""2E20"F 2F


24/43


:! 5! Armstrong' o1 maturity modeling sa,ed my softball team'Proceedings of the &th 'nnual

nternational ymposium of the nternational *ouncil on ystems +ngineering I@CJS$4'

Nancou,er' #ritish Columbia' Canada' :uly 2E-F0' "((/

A! T! #ahill' The ideal moment of inertia for a baseball or softball bat'+++ Transactions on

ystem, an, and *y"ernetics - Part ' ystems and /umans' 824' 200;4' "(*-20;!

)! .! #ahill' #aseball ,ideo! http6sie!ariona!edusysengrbaseball2Seam-;Seam-Nideo!ANI'

200!

A! T! #ahill and )! H! #ald1in' Predicting a #aseballs path''merican cientist,@F4' pp! 2"/-

22' 200!

A! T! #ahill and )! H! #ald1in' The rising fastball and other perceptual illusions of batters' in H!

K! ung and :! &! Pallis $ditors4'%iomedical +ngineering Principles in ports' Klu1er

Academic' 200;' pp! 2*-2/*!

A! T! #ahill' 5! #otta and $! Smith' =hat Are Ge,elsProceedings of the 15th 'nnual

nternational ymposium of the nternational *ouncil on ystems +ngineeringI@CJS$4'

:uly "0-"' 200' 5ochester' @%

A! T! #ahill and :! )aniels' +sing ob9ect-oriented and +&G tools for hard1are design6 a case

study' ystems +ngineering' :"4' 200F4' 2/-;/!

A! T! #ahill and =! :! Karna,as' )etermining ideal baseball bat 1eights using muscle force-

,elocity relationships'%iological *y"ernetics' :26 "(/(4' /(-(*!

A! T! #ahill and =! :! Karna,as' The ideal baseball bat'0ew cientist' 1"*EF46 E April

"(("4' 2E-F"!

A! T! #ahill and =! :! Karna,as' The perceptual illusion of baseballs rising fastball and breaking

cur,e ball'ournal of +2perimental Psychology /uman Perception and Performance,1@6

"((F4' F-";!

A! T! #ahill and T! Ga5it' =hy cant batters keep their eyes on the ball''merican cientist' 2'

&ay-:une "(/;4' 2;(-2F!

A! T! #ahill and &! &orna ?reitas' T1o methods for recommending bat 1eights''nnals of

%iomedical +ngineering' 28' "((4' ;FE-;;;!

)! H! #ald1in' o1 to 1in the blame game'/ar$ard %usiness 3e$iew' :uly-August 200"4'

50"0*C!

""2E20"F 2;


25/43


)! H! #ald1in and A! T! #ahill' A model of the bats ,ertical s1eetness gradient' in &! ubbard'

5! )! &ehta and :! &! Pallis $ditors4'The +ngineering of port 5' ,ol! 2' Proceedings of the

th International $ngineering of Sport Conference' September "F-"E' 200;' )a,is' CA'

International Sports $ngineering Association IS$A4' Sheffield' +K' pp! F0-F""!

:! #arun' ods *ountry and ine# ' declaration of lo$e spiced with a few harsh words,

Atlantic-Gittle' #ro1n and Company' "(;!

K! #oeke' *osmic iew, the 7ni$erse in 48 umps':ohn )ay Company' @e1 %ork' "(*!

P! #rancaio'


26/43


5! Hray' &arko, at the bat6 a model of cogniti,e processing in baseball batters'Psychological

cience' 1E46 200F4' ;2-;*!

&! ubbard' 5! )! &ehta and :! &! Pallis' $ditors4' The +ngineering of port 5' International

Sports $ngineering Association IS$A4' Sheffield' +K' 200;!

H! K! ung and :! &! Pallis' $ditors4'%iomedical +ngineering Principles in ports' Klu1er

Academic' 200;!

I#& 5+P' 5ational +nified Process'http6111!rational!comproductsrupinde7!9sp' 200F!

=! :! Karna,as' A! T! #ahill and )! &! 5egan' A model for the rising fastball and breaking cur,e

ball'Proceedings of the 1::8 +++ nternational *onference on ystem, an, and

*y"ernetics' (2-(2/!

&! Ge1is'oney"all The 'rt of Winning an 7nfair ame' =!=! @orton O Company' 200F!

&! =! &aier' )! $mery and 5! illiard' A@SII$$$ ";*" and systems engineering' ystems

+ngineering' *F4' 2*-2*0' 200;!

:! @! &artin' ystems +ngineering uide"oo; ' process for de$eloping systems and products'

C5C Press' "((*!

)! $! &cugh and A! T! #ahill' Gearning to track predictable target 1a,eforms 1ithout a time

delay'n$estigati$e 9phthalmology and isual cience' 2E' "(/4' (F2-(F*!

&! K! &c#eath' )! &! Shaffer and &! K! Kaiser' o1 baseball outfielders determine 1here to

run to catch fly balls' cience' 2:' April "((4' E(-*F'

:! A! &oody' =! G! Chapman' ?! )! Nan Noorhees and A! T! #ahill'etrics and *ase tudies

for +$aluating +ngineering


27/43


)! &! 5egan' Nisual 9udgments and mis9udgments in cricket' and the art of flight'Perception'

216 "((24' ("-""!

:! P! 5eiter' Should teams 1alk or pitch to #arry #onds%ase"all 3esearch ournal,2' 200;4'

EF-E(!

=! #! 5ouse' :! A! Cannon-#o1ers and $! Salas' The role of mental models in team performance

in comple7 systems'+++ Transactions on ystems, an, and *y"ernetics' 22E4' "((24'

"2(E-"F0/!

H! S! Sa1icki' &! ubbard and =! :! Stronge' o1 to hit home runs6 Jptimum baseball bat

s1ing parameters for ma7imum range tra9ectories''m. . Phy! 1""4' 200F4' ""2-""E2!

)! &! Shaffer and &! K! &c#eath' #aseball outfielders maintain a linear optical tra9ectory 1hen

tracking uncatchable fly balls'ournal of +2perimental Psychology /uman Perception and

Performance' 2' 20024' FF-F;/!

5! H! =atts and A! T! #ahill'=eep >our +ye on the %all *ur$e %alls, =nuc;le"alls and

?allacies of %ase"all,=!! ?reeman' 2000!

T! =illiams and :! +nder1ood! The cience of /itting,@e1 %ork6 Simon and Schuster' "(/2!

:! A! .achman' A frame1ork for information systems architecture'% ystems ournal' 2:F4'

"(/*!http6research1eb!1atson!ibm!com9ournals9F/2achman!pdf

""2E20"F 2*
http://www.whfreeman.com/generalreaders/book.asp?disc=&id_product=1124001751&@id_course=http://www.whfreeman.com/generalreaders/book.asp?disc=&id_product=1124001751&@id_course=http://researchweb.watson.ibm.com/journal/sj/382/zachman.pdfhttp://www.whfreeman.com/generalreaders/book.asp?disc=&id_product=1124001751&@id_course=http://www.whfreeman.com/generalreaders/book.asp?disc=&id_product=1124001751&@id_course=http://researchweb.watson.ibm.com/journal/sj/382/zachman.pdf


28/43


Fi-+re 'e-ends and Ta;le Titles

Table I! An $mpty .achman ?rame1ork

Table II! &odels for #uilding a ouse that )iffer in )etail and @atureTable III! A .achman ?rame1ork Populated 1ith #aseball &odels

Table INa! Go1-le,el Class )iagram for the #atter!

Table INb! igh-le,el Class )iagram for the #atter!

Table N! @umber of Papers 1ith Primary $mphasis in $ach Cell

Table NIa! A .achman ?rame1ork for Holf

Table NIb! A .achman ?rame1ork for Holf continued4

?igure "! An acti,ity diagram for a pitch and a partial response to it Assuming it is a groundball

hit into fair territory! The fielder on first base catches the thro1! There are no other base

runners!4! Copyright ' 200;' #ahill' from http6111sie!ariona!edusysengrslides used 1ith

permission!

?igure 2! &odels should not skip le,els in e7changing information! Copyright ' 200;' #ahill'

from http6111sie!ariona!edusysengrslides used 1ith permission!

""2E20"F 2/


29/43


Table I! An $mpty .achman ?rame1ork

@ame of $nterprise"! =hatdata4

2! o1function4

F! =herenet1ork4

;! =hopeople4

! =hentime4

E! =hymoti,ation4

"! Scope

conte7t42! #usiness modelconcept4

F! System modellogical4

;! Technologymodel

physical4

! )etailedrepresentation

component4

E! 5eal system

""2E20"F 2(


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Table II! &odels for #uilding a ouse that )iffer in )etail and @ature

A ouse &odel @ature or Purpose

"! Scope

Planner4

#ubble charts' i! e!

rough sketches

#asic concepts for the building

Hross sie' shape and relationshipsArchitect-o1ner mutual understandingInitiate pro9ect

2! #usiness model

ome J1ner4

Architects dra1ings ?inal building as seen by the o1ner?loor plans' site plansArchitect-o1ner agreement on the building$stablish a contract

F! System model

)esigner4

Architects plans ?inal building as seen by the designerTranslation of o1ners ,ie1 into a product)etailed dra1ings in a doen categories#asis for negotiation 1ith general contractor

;! Technologymodel

#uilder4

Contractors plans ?inal building as seen by the builderArchitects plans constrained by technologyo1 to build descriptions)irects construction acti,ities


Subcontractor4

$lectrical schematics'Plumbing blueprints

Subcontractors plans)etailed stand-alone modelSpecification of 1hat is to be built)irects installation of specific items!

E! 5eal system+ser' &aintainer4

ouse Physical building

""2E20"F F0


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Keynote address at Asia-Pacific Congress on Sports Technology held at the Tokyo Institute of Technology in September 200!

Table III. A Zachman Framework Populated with Baseball ModelsBaseball 1. What 2. ow !. Where ". Who #. When $. Wh%

1. &cope'(ommissionero) Baseball*

+,uipmentrules

-ules o)baseball

&tadiums T/0 eaues di3isions

-e3enue sharin0-eser3e clause

(hronolo% +ntertainment0Intellectualstimulation

2. Business

model'Team 4wner*

5(AA bat

rules0 Freebats

6mpire calls

balls strikes

&hared use o)

stadiums

A7-od0 4l%mpic

teams

&eason

schedules

Mone%0 Power0

Pride

!. &%stem model'8eneralManaer*

Battinpractice)acilities

Pitch tracker0&tadiuminstant repla%

Placement o)home plate

Fantas% baseball0Pla%er contracts02#7man roster0

Pitchinrotations

Wants awinninseason

". Technolo% model'TeamManaer*

Ideal batweiht weihtdistribution

Teamwork sinals )or hit run0 bunt0etc.

Mentalrehearsal understandins

Weekl% statistics0Bo9 scores0&weet spot

Pitch count Intentionalwalks0 Theblame ame

#. :etailed representation'&cientist0+nineer0(oach*

A swin0(oP0 MoI0(o-

-isin)astball0 +%emo3ementstrateies0&peed spin

Predict where when0Fielders runcur3ed paths

Pla%ers;ph%sioloicalstate0 The count

Mentalmodels0Work )ast chanespeeds

(FFF0 +9pect)astball with !7< count

$. -eal s%stem'BaseballPla%er*

Baseballbat

4ne pitch responses

Baseball )ield Ma=or leauebaseball pla%ers

Pitchinter3al

Moti3ation )ordecisions

""2E20"F F"


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The #aseball $nterprise' Column "' =hat data4

5o1 number'5o1 name'typicalstakeholder4

Specific models 5eferences

"! Scope

Commissionerof #aseball4

The creator of baseball' until recently belie,ed to be Abner

)oubleday' 1ould ha,e listed rules' bats' balls' players' and fieldsamong the list of things important to the game! The rules of ball-and-stick games baseball' softball' cricket' tennis' etc!4 are 1ritten tochallenge the physiological limits of the human in many dimensions

B5egan' "((2D

2! #usinessmodelTeam J1ner4

The @ational Collegiate Athletic Association @CAA4 controlscollege sports! In this role' it has created rules go,erning the allo1eddimensions and performance of aluminum bats! ?or e7ample' the batshall not 1eigh less in ounces4 than its length in inches4

BCrisco' "((*6@athan' 200FD!

$aston Sports and illerich O #radsby Company gi,e 1ooden bats toma9or league players for free! =hy To build their brand image' sothat they can sell more of their regular sporting e8uipment!

F! System modelHeneral

&anager4

$ach organiation pro,ides facilities for batting practice' conditioningand skills de,elopment

;! TechnologymodelTeam &anager4

There is an ideal bat 1eight and a best 1eight distribution for eachbatter! The team helps the indi,idual select and ac8uire the right bat!

B#ahill andKarna,as' "(/( and"(("M #ahill and&orna ?reitas' "((M#ahill' 200;D

! )etailedrepresentationScientist'$ngineer' Coach4

The s1ing of a bat can be modeled 1ith a translation and t1orotations' one about the batters spine and the other bet1een the t1ohands

B#rancaio' "(/*M=atts and #ahill'2000DM

There are many models for a baseball bat that e7plain the Center ofPercussion CoP4' moment of inertia &oI4' coefficient of restitutionCo54' etc ?rom the perspecti,e of a bat manufacturer' this detailedrepresentation of the bat can be represented as a model depicting there8uired length' the taper of the handle' the 1idth of the barrel' the bat1eight' etc!

BAdair' "((;M Cross'"((/M @athan' 2000and 200FM Sa1icki'ubbard andStronge' 200FM#ahill' 200;D!

E! 5eal system#aseball Player4

The baseball bat

""2E20"F F2


33/43


The #aseball $nterprise' Column 2' o1 function4

5o1 number'5o1 name'typical

stakeholder4


"! ScopeCommissionerof #aseball4

The rules of baseball e,ol,ed o,er its first 0 years' but ha,e beenrelati,ely constant o,er the last century

BHould' 200FD

&a9or Geague #aseball Inc! defines the strike one and managesumpires!


The umpire calls balls and strikes! o1e,er' there has been,ariability in the strike one! To reduce this ,ariability' multipletele,ision cameras track each pitch and computers reconstruct thetra9ectory of each pitch After the game' the umpires decisions arecompared to the computers decisions! This feedback helps theumpires impro,e their decision-making!

B111!uesTec!comD!

F! System modelHeneral

&anager4

Stadiums can be e8uipped 1ith a ,ariety of optional e8uipment thatcan record and playback the pitch' such as the multiple tele,ision

cameras used to aid the umpires and the stadium instant replayscreens for the benefit of the players and spectators! The C&& canimpro,e performance!

B111!uesTec!comMArmstrong' "((/D


Team1ork and signals enable the manager' the batter and therunners to e7ecute tactics such as hit and run' bunt' steal' take thepitch' s1ing a1ay' etc!

The pitcher pitches the ball! The batter s1ings and hits the ball! eruns to1ard first base! $tc! Jur acti,ity diagram of ?igure " sho1sone pitch and subse8uent acti,ities!

?igure "


+nderestimating the pitch speed can induce the perceptual illusion ofthe rising fastball!

BKarna,as' #ahill and5egan' "((0M #ahilland! Karna,as' "((FD

A neurophysiological model sho1s ho1 the batter predicts 1here

and 1hen the ball 1ill cross the plate!

B#ahill and!

Karna,as' "((FM#ahill and #ald1in'200;D

T1o strategies are used by the batter for tracking the pitch using thesaccadic and smooth pursuit eye mo,ement systems!

B#ahill and Ga5it'"(/;M &cugh and#ahill' "(/DM

Jnce the ball is in the air' the mo,ement of the pitch depends onlyon ,elocity' spin rate and spin a7is!

B=atts and #ahill'2000M #ahill and#ald1in' 200;DM


The acti,ity modeled in column 2 is one pitch and peoples responseto it

""2E20"F FF


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The #aseball $nterprise' Column F' =here net1ork4


stakeholder4



The teams are organied into leagues and di,isions according togeography!

#aseball is played in stadiums and broadcast on tele,ision!


Stadiums can be designed for baseball only or they may be shared bybaseball' football and other e,ents!

F! System modelHeneral&anager4

The placement in the stadium of home plate effects the area behindthe plate' the design of protecti,e netting' the orientation to the sun'the distance to the fences and therefore safety and playingperformance!

;! Technologymodel

Team &anager4

#efore e,ery pitch all fielders mentally rehearse 1here they 1illthro1 the ball if they recei,e a ground ball or a fly ball and they

establish understandings 1ith nearby fielders about 1here eachplayer 1ill go!


#atters must predict 1here and 1hen the ball 1ill cross the plate BKarna,as' #ahill and5egan' "((0M #ahilland #ald1in' 200;D

The human brain does not ha,e 7' y and coordinates of ob9ects!umans must track ob9ects using neurophysiological parameters! Asa result' outfielders run a cur,ed path 1hen tracking do1n fly balls!

B&c#eath' Shafferand Kaiser' "((MShaffer and &c#eath'2002D


The topic of column F is the baseball field!

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The #aseball $nterprise' Column ' =hen time4

5o1 number'5o1 name'typicalstakeholder4


"! Scope

Commissionerof #aseball4

)ecisions must be made about interleague play' playoff structure'

e7pansion teams' etc!


Season schedules for all of the teams are comple7 because of themany constraints!


Tele,ision net1orks determine the starting times of many games!

Sometimes pitching rotations are planned' but sometimes they aremerely a default' e!g!'


Pitch count - pitchers are often remo,ed after' say' "20 pitches!


Scientist'$ngineer' Coach4

A successful tactic of pitchers is

B#ahill and #ald1in'200;D

The batters mental model for the pitch is based on the last one ort1o pitches or perhaps on the last 20 seconds! B#ahill and #ald1in'200;M Hray' 2002 and200FD


The fundamental unit of time in a baseball game is one pitch!

""2E20"F FE


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The #aseball $nterprise' Column E' =hy moti,ation4


stakeholder4



Hould e7plains the intellectual comple7ity that causes sagaciousAmericans to be fascinated 1ith baseball!

Hould B200FD

The purpose of baseball is entertainment 1ith t1o ma9orsubdi,isions6 tele,ision and baseball stadiums!


=hat moti,ates baseball team o1ners Po1er' ego' money!

=hat moti,ates ma9or league baseball players &oney' prestige andpride! The top players can also get money from endorsements ofclothing' e8uipment' ,ideo games' ,ideos and books!


Heneral &anagers trade players in order to ha,e a 1inning season1ithin their constraints! They are moti,ated by their dri,e forsuccess!

;! Technology

modelTeam &anager4

=hy does a manager decide to pitch to a famous slugger rather than

intentionally 1alk him In making this decision the managerconsiders the score' runners on base' batting a,erage' slugginga,erage' etc!

B5eiter' 200;D

The manager moti,ates his players by kno1ing 1hen blame and1hen not to!

B#ald1in' 200"D


#atters use many heuristics to decide 1hat to do! Among them is'1ith a F-0 count e7pect a fastball because the pitcher 1ill ha,e thegreatest confidence in thro1ing it for a strike!

B=illiams and+nder1ood' "(/2M#ahill and #ald1in'200;D

Critical flicker fusion fre8uency e7plains 1hy pitchers think there isa difference bet1een the t1o-seam and the four-seam fastballsalthough physics sho1s no difference!

B#ahill and #ald1in'200M #ahill' 200D

=hy does the pitcher decide to thro1 a fastball' a slider' a cur,eball

or a changeupE! 5eal system#aseball Player4

Column E concerns 1hy people think the things they do and makethe decisions they do

""2E20"F F*


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Table INa! Go1-le,el Class )iagram for the #atter!

#atter#allsStrikesJuts

s1ing4takePitch4bunt4predict=here4predict=hen4

Table INb! igh-le,el Class )iagram for the #atter!

#atter

$7perienceSalaryPhysiologyCompetition

talkToPress4talk=ithAgent4deal=ithJ1ner4take)rugTest

""2E20"F F/


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Table N! @umber of Papers 1ith Primary $mphasis in $ach Cell

"! =hat 2! o1 F! =here ;! =ho ! =hen E! =hy

"! Scope "

2! #usiness model 2F! System model 2 ; 2

;! Technology model

E F" E "

! )etailed representation

/2 ;" E E "

E! 5eal system

""2E20"F F(


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Table NIa! A .achman ?rame1ork for Holf

Holf "! =hat 2! o1

"! Scope The +SHA O @CAA 1rite rules about golf e8uipment!$ach year four billion dollars 1orth of golf e8uipment is

sold 1orld 1ide!

The original rules for playinggolf 1ere 1ritten in Scotland in

the "/thcentury!2! #usiness model

A ne1 set of e8uipment should cost Q"00 to Q"000 for(0R of golfers!

Holfer normally plays "/ holesof golf! Hreens fee range fromQ"0 to Q200 for (0R of courses!

F! System model

$ach club has a different length O is designed to hit theball a different distance! Jn each hole' you 1ill use a,ariety of clubs! Jn a short "0-yard par-F hole' youmight use a iron O a putter! =hereas on a long ;0-yard par- hole you might use a dri,er' a F-iron' a (-ironO a putter! A golfer 1ill probably take 2 to"0 strokes per hole!

Holfer hits ball from the tee!Holfer finds ball on fair1ay Ohits it repeatedly until it lands onthe green! Holfer putts ball intocup! 5epeat for ne7t hole! Thelo1est score is best!

;! Technology model

Clubs6 head O grip are attached to opposite ends of theshaftM head design effects the s1eet spotM shaft materialeffects fle7ibility!#all6 the core effects compression O the outside co,ereffects durability!Club O ball effect Co5' launch angle' speed O spin!

Holfer s1ings club' club hits ballO transfers momentum to ball'1hich can be modeled 1ith theCo5! The impact force canproduce accelerations of up to0'000 g!


Clubs6 length' 1eight' material of shaft' grip O head'head design#all6 core' co,er' shape O sie of dimplesTee6 length

$nergy is stored in the bendingO mo,ement of the club O in thecompression of the ball!

E! 5eal system Holf e8uipment Club hits ball

""2E20"F ;0


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Table NIb! A .achman ?rame1ork for Holf continued4

Holf F! =here ;! =ho ! =hen E! =hy

"! Scope Holf comple7 comprised ofcourses' pro shop' dri,ingrange' food ,endors

PHA' Athletic)irector

Season schedule @et1orking'5ecreation'Competition

2! #usiness model

Some courses are designedfor carts O some for1alking! Course rules applyto shirts' shoes O carts!&ost use reclaimed 1ater!

Coach' Holf pro1ho gi,es lessonsO has traininge8uipment' Holfcourse architect

A game lasts ;hours allo1ing ;0foursomes to playeach course perday!

Holfer 1ants to play1ell to increasenet1orking time'pride O satisfaction!

F! System model

Tee bo7es' ?air1ays'Hreens' Sand traps' =aterhaards' Jut of boundsterritory' "0 yard markers

Team' Club'Informationstorage Oprocessing

A,erage hole timeis "2 minutes 1ithup to minutessearching for lostballs!

Holf courses aredesigned to be fun'challenging'uniform O friendly!

;! Technology

model

Topology' i! e! hills' creeks'

boulders' trees' steps'drainage

?oursome' Score' The ball is in the

air 0! to "0seconds!

Holfer 1ants a

consistent prefects1ing in order tominimie thenumber of strokeson each hole


Hrass' Sand' =ater' Cup'?lag' Cutting of grass' Setof daily pin placements!

@ame' andicap'Clubs' $ligibility'Status ProAm4!

The ball-club headcollision lasts abouta half millisecond

Holfer chooses aparticular clubbased on distance tothe cup O the lie ofthe ball!

E! 5eal system Holf course Holfer could beuni,ersity'1eekend or pro4

Time &oti,ation forHolfers decisions

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1.1 1.31.2

1.3.2 1.3.31.3.1

1.3.1.2 1.3.1.31.3.1.1

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?igure 2!

Botta Daniels Bahill Zachman Framework Pop w Bb Models Thrust-Clarify

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