Session 1-14 Strategic Analytics 0821.pdf

7/27/2019 Session 1-14 Strategic Analytics 0821.pdf

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Strategic Analytics: insights from sport

SAIS

Term IV, 2013

Indian Institute of Management KozhikodeInstructor: Deepak Dhayanithy


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Title explained Strategic

Sustainable competitive advantage, capabilities (Mintzbergs pattern)

Key (big) decisions Industry connotation

As in agents with vested interests who could upset equilibrium economics

Mintzbergs early views as plan, position, ploy

Analytics

Home equity jingle mail, loan resolution conversation content, recidivism - impacton borrower/ collateral based resolution approaches

Neither purely deductive, exploratory nor inductive

Method agnosticism

Analytics in various industries focuses on consumer decisions

Backgroundworldwide, India and at IIMK HBS, Sloan, UCLA, UK, Australia, Germany

More difficult to researchAnu Vaidyanathan @ IIMA

Significant support over 2 years @ IIMK

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Learning objectives

1. Take a systematic approach, anchored in theory (agnostic), to thinkingabout important [strategic] decisions

2. Help students identify problems [cognition] and structure solutionapproaches

Equip with models, metrics which have been discussed extensively inresearch literature

3. Draw parallels in other sports/ industry settings Critical thinking and reflection important

4. Introduce students to the use of statistical and OR, OM methods inthinking about high value decisions

Not about how to build the models but appreciate the scope and importantsteps involved

Provide a glimpse of the analytical methodology at work

5. Provide exposure to the ways in which the better industry participantshave long thought through important challenges they are faced with

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Course format

The course is organized around four broad themes, which are Policy and design

Ratings, incentives and performance

Strategic interactions

Fans, Society and Sport

From 3rd session onward, groups (of 5) will lead off the discussions

Class presentations and term papers are key vehicles for us to achieve the

Readings from Scorecasting (TMLW) and Stumbling on Wins (SOW) Other readings: abstract, introduction, literature review, methods, data,

results, discussion very important that each of you get your hands aroundamap

Key for our classes to be interesting! Books chosen for readability in addition to richness of the analytics

As managers, you would be in a position to run such projects in organizations/deal with external consultants/ be one such consultant framing the project

Important to ask the right questions

There are multiple papers for a good number of sessions

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Class presentations

Teams of 5same team for both this and termpaper

Class presentations (25) 18 TMLW and 9 + 2 SOW chapters

Each team to choose at least 1 chapter from each bookto resent in class startin from session 3 June 26th .

Presentation schedule per course outline, sync withCoco and instructor

Please discuss presentations with the instructor

Starting at least a week prior to the date of the presentation inclass

Presenting groups and the class to contribute interms of drawing parallels, discussing limitations

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Term paper

Teams of 5same team for both this and classpresentations

Term paper (35) Extension of ideas discussed in the readings to other

sports, business or experimental contexts Pa er to tar et business or s orts mana ement

audience

Trade publications, sports management conferencesgrade material is A+

As a collective, showcase sector focus, strategicanalytics

Shortlisted topics to be discussed with Instructor by22nd June after mailing a 1-pager on the same

6SAIS IIMK 2013 1,2


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Some thoughts on novelty risks and

getting close to potential Potential

Students make a dent in academic/ trade settings in terms oftheir superior understanding of SA On-base % equivalent for batsmen might be of interest to folks at

cricinfospeed is important

Drawing parallels to wider business/ management settings Bases stolenhow big a deal is it really? Sixers versus out caught on the boundary

PO costs of all-out sales promotions [intrusive ones] hardlyquantified, understood

Both the above have outlets that can be targeted, develop

collateral Why the NA sports by and large?

Novelty risk Keeping communication channels open, n-1, COCO

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SAIS Discussions grouped into

Policy and design

Ratings and incentives

Strategic Interactions Fans, Society and Sport

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Topics

# SAIS content area1 P Competitive balance, open/ closed

league, free agency

2 P Doping dilemma

3 R Performance metricswhen less is

more (basketball rebounds)

4 R Matching law (going for 3)

5 R Stress and performance

6 R Assessing adjudicator decisions

7 R Measuring and valuing wins produced

8 R Hot handphenomenon or fallacy9 I Signaling, bluffing and bargaining

10 I Peloton analytics

11 I Monopsony and salary suppression

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Topics

# SAIS content area12 I Value of roster flexibility

13 I Home advantage

14 I Bidding for resourcescoattail effect

15 F Scheduling

16 F Performance and attendance

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Topics and their wider relevance

# SAIS content area Wider business relevance1 P Competitive balance, open/ closed

league, free agencyKey aspects of designing andmanaging competition

2 P Doping dilemma Plagiarism, cutting corners



P(D) to P(D) + LGD

4 R Matching law (going for 3) Project incentives, Skill development

5 R Stress and performance Taskforce formation

6 R Assessing adjudicator decisions High leverage moments

7 R Measuring and valuing wins produced Unit to enterprise performance link

8 R Hot handphenomenon or fallacy Taskforce, high value resource mgmt9 I Signaling, bluffing and bargaining Negotiations

10 I Peloton analytics CooperateCompete decisions

11 I Monopsony and salary suppression Executive pay regulation



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# SAIS content area Wider business relevance12 I Value of roster flexibility Taskforce composition

13 I Home advantage Conformity bias and Audit functions

14 I Bidding for resourcescoattail effect Understanding bundling

15 F Scheduling Media management

16 F Performance and attendance Understanding causality



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Topics and their methodology

# SAIS content area Methodology1 P Competitive balance, open/ closed

league, free agency

Regression, Spearmans rank

correlation

2 P Doping dilemma Prisoners dilemma game, players


more

Frequency tables, distributions

4 R Matching law Log transformed regressions

5 R Stress and performance Shot level data, probit (binary nonlin)

6 R Assessing adjudicator decisions Sub setting


8 R Hot handphenomenon or fallacy Runs test9 I Signaling, bluffing and bargaining Bayesian reasoning

10 I Peloton analytics Basics of wind resistance, physics

11 I Monopsony and salary suppression Policy analysis, Regression



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Topics and their methodology

# SAIS content area Methodology12 I Value of roster flexibility Regression, constrained optimization

13 I Home advantage Multiple

14 I Bidding for resourcescoattail effect Panel regression

15 F Scheduling Optimization

16 F Performance and attendance Co integration, causality tests



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Damned Statistics but Fans may still be right

Why 4 out of 5 almost surely means 4 out of 6 Stories need to be told (not just in the media!)

Rodriguez v. Halladay Streaks versus adequate long-run data

Ricky Pontings first test century in India

Observation distance is important? What does irrational or sub-optimal mean when used to

describe decisions, especially those made by experts?

Why sports?

Transparency Cost of failure

The business is easy to understand Difficult to be in a fools paradise for long



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Challenges, stumbling blocks

Understanding the numbers, stats

Past performanceFuture performance

relationship

Rule changes are far fewer in the popular sports,s ey may ave a s gn can mpac on

outcomes

Back-pass to the goalie

How does the market for skills evolve? Victor Valdes

Best goaliewith the ball at his feet

Not-interfering-with-play interpretations



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Stumbling blocks contd..

Data

Statistical analysis

Human being have limited cognitive abilities

are not lightning calculators of utility

Data + common sense

Coin tosses at the Super Bowl

What data is about performance, ability and whatis noise/ unexplained/ randomness

Money well spent/ wasted is a big deal in sports



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Oakland As story

Billy Beane, Moneyball, Michael Lewis Why is the 1999-2002 seasons record worthy?

Baseline1997

John Hakes and Raymond Sauer

Workers paid in line with their expected productivity What performance characteristics impact wins What teams are willing to pay for the characteristics

Are salaries consistent with wins

30 MLB teams, 1999 to 2003sufficient?

Statistical analysis, panel data regressions On-base % 2x impact on wins as slugging Slugging has a big impact on the salaries of players What happened post 2004?

Recall Ron conversation

National league goes back to1876



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Where does that leave us?

Maybe there is something to learn from thewealth of information + analytics + focus on

the big decisions (good and not so good)

162 regular season games + 20 post in MLB

In order to do so, basic understanding of the

games is needed

Thankfully (perhaps reason for their popularity),

the rules are simple



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Baseball

Like cricket but you can throw only full tosses

Bat is just a club

Beamers are illegal here too

9 innings (?)

3 batters per inning

Diamond Home base, 1st base, 2nd base, 3rd base

Run scored when a batter runners over home base

Bases loadeddifference between a home-run and agrand-slam

Strike out (ballthe .299 story), caught, walks

If a batter hits into fair territory, he runs



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Basketball

Indoor, non-contact sport

Duration4 quarters of 12 minutes each

Twos and Threes, free-throws

Time-outs 2 100 second [1, 3]

3 100 second [2, 4]

Shot clock24 seconds Foul trouble and ejection



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SAIS session 2, 06/20/13

Term IV, 2013



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IPL Player Retention rules tilt level playing field Tariq Engineer, Cricinfo [ESPN Cricinfo], Dec 10 2010

Did IPL player retention rules undermine competitive balance? Franchises retaining players will be docked budget $ for the upcoming player

auction per the following scheme Salary cap moot [MSD could be getting paid $3M and it wont count against

CSK when it goes out to bid for players] What about the ethos of the salary cap?

Iconic players were earlier paid +10% of highest bid other player received Salaries of the 12 la ers retained in the 2010 la er biddin rocess was not

disclosed

# Playersretained

Player

Auction

Budget $docked by(max $9M)

Notional

per playerretainedprice

1 1.8 1.80

2 3.1 1.55

3 4 1.33

4 4.5 1.13 23SAIS IIMK 2013 1,2


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How Competitive are Competitive leagues?

TMLW - Scorecasting

What does dominance mean across industries RetailWalmart 11.3% of $3T US Retail market BankingCitigroup (TOBTOF) 3% Airline in USAmerican and SW 14% MLBYankees 25% of World Series rings

There are 29 teams going at each other in the MLB

But, how do the Yankees dominate like this?

,

Market size is a big driver Similar pattern in the WSF of previous seasons

Is that all then? 162 game season [IPL is 18] 8 top teams in the playoffs, Best-of-5, Best-of-7 League Championship

Series, Best-of-7 World Series (IPL simple knockout) [IPL is 4/9]

Better teams (linked to payroll) win because the system makesupsets that much more difficult law of large numbers?



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Is it is the same in all sports though?

NFLstriving for parity and democracy 16 games per seasonwhy?

12 teams in the post-season

No best-of series, just knockouts

Till 2010, salary caps

Bulk of team revenues from league wide television contracts Su er Bowl since 1967

18 out of 32 franchises have lifted the Lombardi trophy

All but 4 have appeared in the Super Bowl at least once Its been the same since 1903!

Does market size have a big influence? Steelers (6)Pittsburg Green Bay Packers (3)Wisconsin [smallest market]

For the betting types, where would you prefer to wager on the worldchampion?



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What are the important variables that determine

whether a league is competitive or oligopolistic?

$$$ per team Is it a policy/ decision variable?

Games/ season

# teams in the playoffs One-and-done or playoff series

Salary caps

Other key elements of the leagues Free agency

Open or closed leagues



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Impact of Free agency in NA pro team sports

Maxcy and Mondello (2006)

Free agency reintroduced in the 1970s Concern that the star players would congregate in

the big market teams

Rottenberg, Uni of Chicago

1956, ames with uncertain outcomes are morelikely to be viewed by fansUncertainty of

Outcome hypothesis (UOH)

Invariance principle: player talent in a league

would move to the team which valued them most,invariant of team revenues Similar to the Coase (1960) view on resources



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But how do we measure uncertainty or

competitiveness?

Within season and between season [NBA,NFL, NHL] measures of competitive balance

Standard deviation of winning percentage (SDWP) Ratio of actual to ideal (adjusts for number of

observationsmore games in some leagues) scont nu ty o team per ormance across seasons

Spearmans Rank Correlation Coefficient

Minimal large-market dominance (small-market

weakness) Only attention (per paper date) has been anectodal

NFL, NHLimprovement; NBAinverse



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Factors that may influence this balance

Aside: competitive balance (UOH) may be only one of the factors bringingfans to the games Other factors

Rookie drafts

Salary caps

Alternative revenue sharing practices

Imbalanced schedules Teams with similar previous season records playing each other (NFL)

,differences than within league differences, over time

Leagues depending more on national television broadcast revenue Expectation is that NFL fans watch more games involving non-local teams

(Monday night football)

Leagues depending more on gate receipts (MLB, NHL) and other localrevenue Bottom line, league can be designed to maintain competitive balance at the

level where it makes most economic sense for the league But, let us understand the Free AgencyCompetitive Balance



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Free Agency in MLB

Players with 6+ years of MLB service couldbecome free agents at the expiration of theircontracts

Some aspects of compensation to franchiseslosin la ers have been introduced since

CBA between players association and theleague includes salary arbitration for playerswith 23 years service

Free agency link to higher salaries has beenestablished (eg: Scully, 1974), but link tocompetitive balance is ambiguous



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Free Agency in MLB

Free agency link to higher salaries has beenestablished (eg: Scully, 1974), but link to

competitive balance is ambiguous

4,000,000

4,500,000

-

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

1970

*

1977**

1980

1990

2000

2003***

MLB

NFL

NBA

NHL



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Impact of free agency on competitive balance

NBADWP Y1, measuring the ratio of actual to ideal standard deviation of win% in the NBA for each year

from 1951 to 2004

NBADWP t-1 Equals the lagged value of Y

NBASRCC Y2, Spearmans rank correlation coefficient each year from 1951 to 2004

NBURSFA Dummy var = 1, representing the sample years of unrestricted free agency in the NBA

NBACAP1 Dummy var = 1, representing the sample years in which the NBA imposed a cap on team

payrolls (19831999)

NBACAP2 Dummy var = 1, representing the sample years in which the NBA imposed cap on team

payrolls as well as individual player salaries (20002004)

NBARIVAL Dummy var = 1, representing the sample years in which the league faced an economic

challenge from a rival league (ABA: 19671976)

NBASTRIKEDummy var

EXPANSIONDummy var

NBATEAMSnumber of teams in the league in a season32SAIS IIMK 2013 1,2


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Competitive balance modelNBA

Model I Model IIMean Coefficientt ratio Coefficientt ratio

Constant 2.43*** 10.6952.034 *** 5.13

SDWP t-1 2.636 0.155 1.093

NBAURSFA 0.1110.458 1.2860.393 1.192

NBACAP1 0.2960.343 1.0320.332 1.132NBACAP2 0.0 30.132 0.2 30.2 0.51

EXPANSION 0.2040.06 0.4340.068 0.474

NBARIVAL 0.1670.148 0.4890.12 0.415

NBASTRIKE 0.019-1.207*** -2.759-1.246*** -2.796

rho 0.605 5.5380.508 4.298

***p


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OLS Interpretation pointers

What do the variable coefficients mean? What does the t ratio tell us?

What is the var subscripted with t-1?

What does it mean when coefficient signs changein alternative model s ecifications?

What does it mean when the significance of amodel variable changes (appears/ disappears) inalternative model specifications?

What is rho?

Interpret the effect of NBAURSFA

Does free agency improve competitive balance?34SAIS IIMK 2013 1,2


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Discussion of results

For NFL there is an improvement in SRCC Delta over seasons

NHL shows an improved in it DWP

Cross sectional, within a season Introduction of the lagged variable in the

regressions does not change the results much

Direct effect of free agency on competitivebalance is ambiguous

Rottenberg, Coase appear to have gotten it right



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Are closed leagues the only way?

Wladimir Andreff ~ 2007, 08 Some stylized facts about closed and open leagues

[Walrasian equilibrium model]

1. Definitions Closed league: entry barrier created by franchise sales

Entry possible by purchase of an expansion franchise on the basis ofprofit potential of the market Kochi, Pune

Entry (into the cartel) to be approved by a majority of the incumbentteams

Competition to the league from a rival league only (ICL)

Open league: integrated in a hierarchical structure where within

the national federation (Serie A, EPL, Bundesliga, etc.) which inturn is within the continental (UEFA) and global federation(FIFA) Entry and exit by the mode of promotion and relegation

Rags to riches is a possibilitygood story



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Comparisonscontd.

In a closed league, the team enjoys absoluteexclusivity over a urban area

If the local area turns unprofitable then the franchisecan move

48 relocations in the big-four NA leagues (NBA,, ,

In an open league, mobility is only viapromotion or relegation

No territorial exclusivity either Home ground for Inter and AC is San Siro

Derbies very marketableLondon, Manchester, Catalan,Madrid, Paris (?)

East Bengal, Mohun Bagan, Mohammedan Sporting37SAIS IIMK 2013 1,2


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ComparisonsCompetitive balance

Labour market regulations is the main instrument in closedleagues

While attempted in open leagues, the open-ness addressessome of the re-balancing demands

Recall 2005 CL final (Istanbul) Steven Gerrards get-out clause

Considerable effort expended in staying-up and promotion as well as in changing the rules

1st division teams see a 20-40% revenue bump on qualifying for CL

Relegation sees a drop of 7580% in revenues

Promotion sees ~ 5x in revenues

Internationalization of EPL club ownership What does Venky, Russian oligarchs, Oil sheikhs and NESN [Boston

Red Sox] have in common??

Blackburn Rovers is the only EPL championship team to getrelegated!!!



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ComparisonsLabour mobility

Labour market regulations is the maininstrument in closed leagues Monopsony, reserve clause (1879 in baseball)

Anti-trust exemption for baseball

Lockouts and CBA to renegotiate prices/ clauses

Free agency status for veterans (1970)

While attempted in open leagues, the open-nessaddresses some of the re-balancing demands

Recall 2005 CL final (Istanbul)

Steven Gerrards get-out clause Bosman Ruling (1995)

Belgian league versus Jean Marc Bosman

Quotas based on player nationality went [where is IPL?]

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ComparisonsRookie draft, cash transfers, CBA and

tax

Rookie draft Reverse-order-of-finish in NA, with roster limits Bosman deregulation and EU competition policy applies player agents role more pivotal

Trading for cash Forbidden in NA leagues (1960 NFL, 1976 MLB)

Cash, loans, barters all possible in soccer

CBA Player working conditions

no: games in a week

Avoid superstar concentration

A counter for losing the reserve clause

Luxury tax

Degree of player unionization much lower in EU



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Comparisonspooling agreements, ownership,

objective function, investment decisions

Pooling Agreements Closed leagues: TV rights (anti-trust exemption), gate receipts,

sponsorships and merchandizing. Only local TV revenue off limits Open leagues: TV rights pooling, others off limits

Ownership Closed leagues: not public

Open leagues: trend toward listed cos since 90s

Objective function Closed league: struggling franchises objectives turn financial (do

NBA teams tank?) Open league: struggling franchises objectives have to remain sporting

Investment decisions Tend to under-invest in sporting talent, its development Tend to trigger off arms races

Chelsea, Manchester City in the EPL

FCB, RMA in Spain

Financial Fairplay implementation and its effects? Borussia Dortmund 41SAIS IIMK 2013 1,2


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Term paper

Teams of 5same team for both this and classpresentations

Term paper (35)Extension of ideas discussed in the readings to other

sports, business or experimental contexts

Pa er to tar et business or s orts mana ement

audience

Trade publications, sports management conferencesgrade material is A+

As a collective, showcase sector focus, strategicanalytics

Shortlisted topics to be discussed with Instructor by22ndJune after mailing a 1-pager on the same



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Key Ideas

Competitive balance Where else would we be interested in maintaining it?

Why? A complex vendor management problem as the one faced

by WHO in its global procurement of vaccines

Why maintain competition?

How should competition be defined?

Leaguesclosed and open What are the issues facing sport, cricket?

Rookie drafts, reserve system

Free agency

Salary caps

Promotion and relegation43SAIS IIMK 2013 1,2


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Avoiding Relegation

From league to club perspective Fairytales are rate

Bottom echelon problems are different from

Top echelon problems So are resources

Relegation battles are dramatic

Any clues for the clubs in this position? AIB, Istanbul



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Avoiding Relegation

# SEASONS

SINCE

PROMOTIO

N

RELEGA

TED

NOT

RELEGA

TED TOTAL

%

RELEG

1 24 3 27 89%

2 - 3 9 2 11 82%

4 - 7 8 3 11 73%

Table 1: Summary of clubs promoted to EPL 1993-94 to 2011-12

Grand Total 46 10 56 82%

# CLUBS

SOURCES

PLAYER

TRANSFERR

ED IN PER

CLUB

SOURCE

#

PROMO-

TIONS

TRANSFER

$/ LEAGUE

AVG

TIME TO

RELEG

EVENT

(SEASONS)

TIME TO

NO RELEG

EVENT

(SEASONS)

Min 6 1.00 1.00 0.05 1 1

Q1 12 1.08 1.00 0.38 1 1.25

Median 17 1.17 1.00 0.58 1 3

Q3 23 1.29 2.00 0.90 4 6.5

Max 38 1.50 4.00 2.86 16 11

Table 2: Summary of Explanatory variables



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Avoiding Relegationcontd.

Cox Proportional Hazards modelB SE Wal

d

df Sig. Exp

(B)

CLUBS SOURCES < 10

(H1) 0.70 0.42 2.84 1.00 0.09 2.02

CLUBS SOURCES >= 21

(H1) 0.05 0.38 0.02 1.00 0.89 1.05

PLAYERS PER CLUB

Table 3-a: Cox Proportional Hazards model of time to relegation

SOURCE (H2) 1.18 1.22 0.94 1.00 0.33 3.25

FIRST TIME PROMOTION

(H3)

(0.2

2)0.34 0.42 1.00 0.52 0.80

RATIO PURCHASE $ TO

LEAGUE AVG (Control)

(1.0

0)0.41 6.13 1.00 0.01 0.37



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Factors that can shift this baseline curve up or down

are those of interest to the club management!

Figure 2: Survival (from relegation) at the mean of covariates



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Announcements

Groups Okay, 1 group to give their choice of topics

Readings choice

Term paper 22nd to discuss with instructor, after sending a 1-

pager

Please discuss your presentations t1 (24

hours) at leastpretty packed classes

schedule



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Term IV, 2013



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Discussion points

Wider applications of competitive balancediscussion

Where to recruit first?

What isnt in the Mitchell report? Doping Dilemma

Go for it

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Where to recruit first?

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Mitchell Report

Utility infielders jacking 30 home runs in aseason

249/ 274 who tested positive were minorleague

players

CBA of doping!

Relative likelihood of PEDs use X GDP per capita

Age profile of PEDs positive tests Repeat offenders

Variance even in US based on ZIP code groups

Population ecology of the stigma52SAIS IIMK 2013 1,2, 3


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CBA contd.

Positive effect on Surviving

Promoting

More pronounced for baseball athletes fromoorer countries

Testing protocols matter

Baseballmore at the lower levels

Similarities to Tax filing

CV dressing

Dotel, journeymansystem or individual?53SAIS IIMK 2013 1,2, 3


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Shermer

Doping Dilemma is the same as the prisoners dilemma athletes instead ofprisoners

MY OPPONENT'S STRATEGY

COOPERATE DEFECT

(remain silent) (confess)

M

Y

COOPERATE One year in jail Three years

S (remain silent) (High Payoff) in jail

T (Sucker Payoff)

R A

T DEFECT No jail time Two years in jail

E (confess) (Temptation (Low Payoff)

G Payoff) Y

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Model to Reality

Athlete asymmetry Skill

Career stage

Economic stakes

Who are the players, what are their incentives

Are veterans same as debutants

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Willy Voet

Willy Voet, the soigneur, or all-around caretaker, for the Festina cyclingteam in the 1990s, explained how he beat the testers in his tell-all book,

Breaking the Chain: Just in case the UCI doctors arrived in the morning to

check the riders' hematocrit levels, I got everything ready to get them

through the tests I went up to the cyclists' rooms with sodium drips-- The

whole transfusion would take twenty minutes, the saline diluting the blood

and so reducing the hematocrit level by three units--just enough. Thiscon rap on oo no more an wo m nu es o se up, w c mean we

could put it into action while the UCI doctors waited for the riders to comedown from their rooms.

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Joe Papp

a 32-year-old professional cyclist currently banned after testing positive forsynthetic testosterone. Recalling the day he was handed the "secret black

bag," Papp explained how a moral choice becomes an economic decision:

"When you join a team with an organized doping program in place, you are

simply given the drugs and a choice: take them to keep up or don't take

them and there is a good chance you will not have a career in cycling."

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Frankie Andreu

superdomestique, or lead pacer, supporting Lance Armstrong throughoutmuch of the 1990s. "Then, around 1996, the speeds of the races shifted

dramatically upward. Something happened, and it wasn't just training."

Andreu resisted the temptation as long as he could, but by 1999 he could no

longer do his job: "It became apparent to me that enough of the peloton [the

main group of riders in a cycling race] was on the juice that I had to do

something." He began injecting himself with r-EPO two to three times a'wee . s no e e u , w c g ves you ns an energy, e

explained. "But it does allow you to dig a little deeper, to hang on to thegroup a little longer, to go maybe 31.5 miles per hour instead of 30 mph."

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Frankie Andreu

superdomestique, or lead pacer, supporting Lance Armstrong throughoutmuch of the 1990s. "Then, around 1996, the speeds of the races shifted

dramatically upward. Something happened, and it wasn't just training."

Andreu resisted the temptation as long as he could, but by 1999 he could no

longer do his job: "It became apparent to me that enough of the peloton [the

main group of riders in a cycling race] was on the juice that I had to do

something." He began injecting himself with r-EPO two to three times a'wee . s no e e u , w c g ves you ns an energy, e

explained. "But it does allow you to dig a little deeper, to hang on to thegroup a little longer, to go maybe 31.5 miles per hour instead of 30 mph."

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Patterns of PEDs sanctions in US pro sport

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

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0.0%-

1 2 3 4 5 6 7 8 9 10

USADA test/ athlete Sanction rate

2001 to 2012 70 disciplines, ~15K athletes

Age of debut not known


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Some sport-wise numbers

Sport

#

Athletes

te ste d Pre 2 006 Pos t 20 06 TotalA rchery 101 0.0% 0.0% 0.0%

Badminton 50 0 .0% 0.0% 0.0%

Baseball 283 0.9% 0.0% 0.7%

Basketball 440 0.0% 0.9% 0.2%

Basque Pelota 6 0.0% 0.0%

Biathlon 113 0.0% 0.0% 0.0%

Bobsled & Skeleton * 229 4.0% 0.0% 2.2%

Bowling 67 0.0% 0.0% 0.0%

Boxing + 332 2.0% 3.3% 2.7%

Canoe & Kay ak 181 0.0% 0.0% 0.0%

Curling 124 0.0% 0.0% 0.0%

Cy cling * 1,481 4.5% 3.3% 3.7%

Santion rate*

Exhibit 3: Tes ts and Sanctions, USAD A 200 1-'12, No: of

athletes (1/2)

Sport

#Athletes

te ste d Pre 2006

Post

2006 TotalParaly mp ic Row ing 18 0.0% 0.0%

Paraly mp ic Rugby 40 0.0% 3.3% 2.5%

Paraly mp ic Sailing 26 0.0% 0.0% 0.0%

Paraly mp ic Shoot ing 6 0.0% 0.0% 0.0%

Paraly mp ic Sled H ockey 43 11.8% 3.8% 7.0%

Paraly mp ic Soccer 42 0.0% 0.0% 0.0%

Paraly mp ic Swimming 124 0.0% 0.0% 0.0%

Paraly mp ic T able T ennis 15 0.0% 0.0%

Paraly mp ic T ennis 30 0.0% 0.0% 0.0%

ara y mp ic rac ie + 1 . . .

ara y mp ic o ey a . . .

acquet a . . .

Roller Sp orts + 166 1.4% 2.1% 1.8%

Santion rate *

Exhibit 3: Tes ts and Sanctions, USADA 2 001-'12, No : of athletes

(2/2)

61SAIS IIMK 2013 1,2, 3

D iving * 104 2.4% 1.6% 1.9%

Equest rian 131 0.0% 0.0% 0.0%

Fencing * 178 2.5% 0.0% 1.7%

ie ockey 14 . 1. .

F igure Skating * 221 0.9% 0.0% 0.5%

G y mnastics + 296 0.0% 1.2% 0.7%

Ice Hockey * 186 1.1% 0.0% 0.5%

Judo 305 1.3% 1 .3% 1.3%

K arate 87 4.2% 0 .0% 2.3%

uge 1 . . .

M odern Pentathlon 42 0.0% 0.0% 0.0%

Paraly mp ic Alp ine Skiing 69 0.0% 6.8% 4.3%

Paraly mp ic Archery 25 0.0% 5.0% 4.0%

Paraly mp ic Basketball 80 3.6% 0.0% 1.3%

Paraly mp ic Boccia 10 0.0% 0.0% 0.0%

Paraly mp ic Curling 19 0.0% 0.0% 0.0%

Paraly mp ic Cy cling 69 0.0% 1.9% 1.4%

Paraly mp ic Equestrian 16 0.0% 0.0% 0.0%

Paraly mp ic Fencing 14 0.0% 0.0% 0.0%

Paraly mp ic Goalball 28 0.0% 0.0% 0.0%

Paraly mp ic Judo 25 0.0% 0.0% 0.0%

Paraly mp ic Nordic Skiing 15 0.0% 0.0% 0.0%

Paraly mp ic Powerlift ing 16 0.0% 0.0% 0.0%

Rowing + 502 0.0% 0.9% 0.6%

Rugby 34 0.0% 0.0%

Sailing 182 0.0% 0.0% 0.0%

Shoot ing 202 0.0% 1.8% 1.0%

Skiing & Snowboarding * 636 1.6% 0.3% 0.8%

Soccer 299 0.0% 0.8% 0.3%

Softball 158 1.0% 0.0% 0.6%

Sp eedskat ing 311 0.0% 0.5% 0.3%

Squash 29 0.0% 0.0% 0.0%

Sw imming * 1,065 1.9% 0.7% 1.2%

Sy nchroniz ed Swimming 76 3.2% 0.0% 1.3%

a e ennis . . .

ae won o + 1 . . .eam an a 1 1. 1.1 1.1

T ennis 53 0.0% 0.0% 0.0%

T rack & Field * 2,837 3.1% 1.2% 2.0%

T riathlon + 368 0.0% 0.9% 0.5%

Volley ball 193 0.0% 0.0% 0.0%

Water Polo 115 0.0% 1.4% 0.9%

Water Skiing 79 0.0% 0.0% 0.0%

Weightlift ing + 607 1.8% 3.2% 2.5%

Wrest ling 292 2.8% 2.3% 2.4%

Grand Total 14 ,783 1.7% 1.4% 1.5%


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Go for it

What is fourth down? What does 3 and 7 mean?

What dont coaches go for it (why punt)?

Job security Loss aversion

Other sporting contexts where loss aversion is

key

bubble

Keeping the closer for the 9th

Keeping Malinga for the 20th62SAIS IIMK 2013 1,2, 3


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Topics and their wider relevance# SAIS content area Wider business relevance

1 P Competitive balance, open/ closedleague, free agency

Key aspects of designing andmanaging competition


Go for it Loss aversion Negotiations,



P(D) to P(D) + LGD





8 R Hot handphenomenon or fallacy Taskforce, high value resource mgmt

9 I Signaling, bluffing and bargaining Negotiations


11 I Monopsony and salary suppression Executive pay regulation63SAIS IIMK 2013 1,2


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12 I Value of roster flexibility Taskforce composition







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Term IV, 2013



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Matching Law

Log (R1/ R2)

Log(B1/B2)

Log (R1/ R2)

Log(B1/B2)

66SAIS IIMK 2013 1,2, 3

Matching Over and Under Matching

Log (R1/ R2)

Lo

g(B1/B2)

Bias


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Quasi-experiment 3-pt circle movement gives three eras

1991-94 1994-97 (Brought-in)

1997-00 (Moved out)

Xlog (3pt scored/ 2pt scored)

Ylo (3 t att./ 2 t att.)

199194 Y = 0.799x + 0.001 [R sq = 0.961]

199497 Y = 0.826x + 0.046 [R sq = 0.972]

1997-00 Y = 0.871x + 0.047 [R sq = 0.961]

67SAIS IIMK 2013 1,2, 3


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Quasi-experimentcontd.

Mean log relative reinforcement ratios

-1.247 (91-94)

-0.797 (94-97)

-0.797 (97-00)

Mean log relative response rates -1.097 (91-94)

-0.623 (94-97)

-0.760 (97-00)

68SAIS IIMK 2013 1,2, 3


69/166

Other sporting contexts

Cricket

Boundary extent6s attempted and hit

Introduction of T20 impact on ODI scoring

Reinforcement pathway?

Powerplay

rec s s rom ene o ou o

Hawkeye

Soccer

Jabulani introduction

69SAIS IIMK 2013 1,2, 3


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Term IV, 2013



71/166








P(D) to P(D) + LGD

4 R Matching law (going for 3) Project incentives, Skilldevelopment




8 R Hot hand phenomenon or fallacy Taskforce, high value resource mgmt



11 I Monopsony and salary suppression Executive pay regulation 71SAIS IIMK 2013 1,2


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Hot Hand Fallacy and Gamblers Fallacy

What is the hot hand?

Phenomenon or Fallacy?

Gamblers Fallacy

GVT, 1985

Plethora of sports work

Stats

Inside views

When soccer goalies will stop anything

Hero calls and tells

73SAIS IIMK 2013 1-5


74/166

What mechanisms underpin the hot hand? Alter and Oppenheimer, 2005

Moldoveanu and Langer, 2002 Prior assumptions about the processes that underlie

probabilistic phenomena Chance or skill?

If chance, frequent changes expected

Gamblers fallacy

If skill, streaks Hot hand

People dont (do) pay much attention to the underlyingprocess when outcomes alternate frequently(infrequently)

Greater confidence in subsequent expectations after aseries of correct guesses



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What mechanisms underpin the hot hand? 2

Moldoveanu and Langer, 2002

Motivational mechanisms Streaks amplify peoples motivational biases, leading

them to predict that helpful streaks will continue and

harmful streaks will end (?)

Impute meaning to streaks depending on how close theysu jectively feel to the esire outcome

Okay, but belief in the hot handgood?

Adaptive behavior

Maladaptive behavior


h h i d i h h h d? 3


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What mechanisms underpin the hot hand? 3 Subjective significance testing

Teigen, 1994 Overestimate the rarity of streaks under the null

hypothesis (of randomness)

Signal Detection Theory

Hits Correctly rejecting the nh that data are random

Correct rejections Correctly retaining the nh that data are random

False alarms Mistakenly rejecting the nh that data are random

Misses Mistakenly retaining the nh that data are random


S i d


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Some points to ponder

Why do people sometimes abandon the tenets

of randomness? Individual differences in the way people detect

changes in the data pattern

Dual process model

Heuristic

Systematic

Note that many real world processes are non-

random/ not purely random


HH d GF i i


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HH and GF in practice

What should be the basis for call routing in a

Insurance companys inbound call center withspiraling churn?

Metric for call-type agent mapping

How real time?

Monte Carlo fallacy

How HH impacts a subscriptions business


N f S l


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Nerves of Steel Two decades of shootouts in the world cup and

Euro

Stress factors impact on performance Predictable, anticipated and expected

Crowd, level of the competition, kick sequence

Less predictable anticipated factors

Kick to win or lose Positive and negative stress factors

Magnitude of impact on performance

Poor decision prospect impacts a soccer players

performance What about emergency workers, doctors, military

leaders, CEOs

Pros and cons of using sports data79SAIS IIMK 2013 1-5

N f S l


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Nerves of Steel

Tortoises fare better than hares



81/166


Term IV, 2013

Indian Institute of Management Kozhikode

Instructor: Deepak Dhayanithy


82/166






3 R Performance metrics when less is


P(D) to P(D) + LGD difficulty

level

4 R Matching law (going for 3) Project incentives, Skilldevelopment







11 I Monopsony and salary suppression Executive pay regulation 82SAIS IIMK 2013 1,2


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Bl k d h t


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Blocked shot Baskets, assists, blocked shots, steals

Are all blocks the same? If not, how are they different?

Is the difference only a fine one?


G i f 3


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Going for 3

Field goal kicking in football

Who is good at field goal kicking?

Measures

Issues

Difficult level and success rate Team/ coach/ game situation and difficulty level


Predicting the likelihood of field goal s ccess


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Predicting the likelihood of field goal success

Factors affecting field goal success

General Distance

Environment

Situational/ psychological


Predicting the likelihood of field goal success contd


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Predicting the likelihood of field goal successcontd.

Environment

Altitude Precipitation

Windy

Humid

Situational/ psychological Post season

Pressure

Away game

Icing


[Logistic] regression process checklist


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Y variable definition

Univariate analysis

Missing values treatment

Flooring, capping of key variables


[Logistic] regression process checklist contd


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[Logistic] regression process checklistcontd.

Binning

Automatic selection models

Multi-collinearity

Or micro-numerosity?

Sense-makin


So what?


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So what?

Kicker rankings

Difficulty adjusted Added points per attempt

Going back to blocked shots

Dwight or Duncan? Season effects

Stadium effects


So what? contd


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So what?contd.

Psychological factors not so important

Classifying attempts accurately into hits andmisses

Probability cutoff decision

Support Vector Machines

Problems

Observational, relatedness of variables

Environmental vars at game beginning

Could be more dynamic


Measurement issues


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Measurement issues

Conditions at kick-off

Multicollinearity

Only distance?

Now video position tracking is possible

Slim data on hi h altitude Sensitive to categorization, except pressure


Acquisitions Credit actions


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Acquisitions, Credit actions

Acquisitions

To acquire n new accounts subject to say, creditscore conditions

Limit exposure

To reduce risk capital by $x

Who will default perhaps not enough

Authorizations

Marginal $ to be saved


Model scrutiny


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Model scrutiny

Concerns

Privacy Fair lending

Consumer protection

Efficacy Recall Prof. Choudhurys talk


Cricket


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Cricket Earlymid career selection choices

All time lists A dismissal is a dismissal, or?

Opponent batting lineup

Home/ away

DRS or not

Bowling first, last

New ball Bowler type

pre or post-Arm extension interpretations


Cricket contd


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Cricketcontd.

Catches

Stumpings

End of the day question

Would the rankings change with difficulty

adjustments? Would our decisions in credit change with

difficulty adjustments?


Lets not forget!


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Lets not forget! SSAC 2014 dates are out

Research papers Every year, the MIT Sloan Sports Analytics

Conference Research Paper Competition bringsexciting and innovative insight and changes the waywe analyze sports.

9/1/13: Form for submission of interest

10/1/13: Deadline for abstracts

10/15/13: Notification of advancement

1/6/14: Deadline for full paper submission 2/10/14: Selection and notification of results

$20K


Lets not forget!


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Lets not forget! SSAC 2014 dates are out

EOS Presenting at EoS provides an opportunity to present a

message, an idea, or a revolutionary thought that couldsomeday change the face of sport.

Be Bold *Be Unique *Be Inventive *Be* * *

Curious *Be Humorous *Be Honest *Be Inspiring

Dates that matter 10/1/13: Form for submission of interest

11/15/13: Deadline for abstracts 1/15/14: Selection and notification of speakers

$7.5K



99/166

SAIS session 8; 07/25/13

Term IV, 2013





100/166


Y variable definition

Univariate analysis

Missing values treatment

Flooring, capping of key variables

100SAIS IIMK 2013 1-5

[Logistic] regression process checklist contd


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[Logistic] regression process checklist contd.

Binning

Automatic selection models Multi-collinearity

Or micro-numerosity?

Sense-makin Some basic measures and visualization

101SAIS IIMK 2013 1-5

T i d th i id l


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1 P Competitive balance, open/ closed

league, free agency

Key aspects of designing and

managing competition


Go for itLoss aversion Negotiations,



P(D) to P(D) + LGDdifficulty level






9 I Signaling, bluffing and bargaining Negotiations, Litigation





103/166







103SAIS IIMK 2013 1,2

Wi d d ti


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Wins produced perspective

Similarities Wins as function of points scored and conceded

Can be tracked to individual aspects

Can be assigned to specific players performance?

NFL focus on the QB (RELWP100) versus

NBA PAWS48

Significant wins produced stats in both sports?

What does it mean?

104SAIS IIMK 2013 1-5

A thors approach for NBA


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Authors approach for NBA

Production Points, field goals attempted, free throws

attempted, offensive boards, defensive boards,

turnovers, steals, free throws made, blocks, assists

Mate48, TMDEF48 (opponent pts from fga, Opp.

Fgm, Opp. TO (not steals), TMTO, TMRB

Adjust for playing position Calculate WP48

105SAIS IIMK 2013 1-5

Authors approach for NFL QB scores


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Authors approach for NFL QB scores

Offense Acquisition of the ball

Moving the ball

Maintaining possession

scoring

106SAIS IIMK 2013 1-5

Questions


107/166

Questions

Implications of assembling a team of playerswith high PAWS48

Given PAWS48 measures, what are the areas

to look into now?

Differences between NBA and NFL wins

metrics (for QBs)?

107SAIS IIMK 2013 1-5

Location based Advertising


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Location based Advertising

Mobile Business idea

Beta testing

Alternative measures

Footfall

Footfall + sale

108SAIS IIMK 2013 1-5

Paired Pitching


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Paired PitchingGreg Rubin

Average MLB starter faced a batter 2.8 times inthe 2009 season IP x BF per IP / 9

Starting pitching talent is expensive

Batters do better once they see the pitcher Fati ue release oint

Analysis of covariance Study interaction between TF and RA

ANCOVA on RA using TF, BF for a sample of 83pitchersstandardized residuals for each observation

19.9 RA > 24.8 > 25.5 > 13.3 (why low suddenly?)

75% variance in RA explained by variance in TF, BF

109SAIS IIMK 2013 1-5

Paired Pitching


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Paired PitchingGreg Rubin

Moving from TF basis analysis to IP basisrecommendations

Can be used to

Reduce RA

Reduce Payroll $

Reduce pitches per season

Assemble a pitching team of average talent

110SAIS IIMK 2013 1-5

Sport application?


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Sport application?

Specialists versus bits and pieces bowlers

Penalty corners

111SAIS IIMK 2013 1-5

Elsewhere?


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Elsewhere?

A realtor needs to decide the sequence in whichhis best salesmen would schedule property

inspections with high value customer prospects

Salesman problem/ Retail problem

112SAIS IIMK 2013 1-5

Of course, the business and the customer has a more

congenial relationship

Format in which final offers are expected from a

recruiting team

Methodology issues


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Methodology issues

Endogeneity Is the n-th play independent of the earlier play?

113SAIS IIMK 2013 1-5

Stripped down poker v1


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Stripped down pokerv1

Gamerules Volunteers

114SAIS IIMK 2013 1-5

Fundamental theorem of Poker


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David Sklansky

Word on probability Games that progress through roundsof

revealing cards, of betting

Poker

Stud

Draw

Holdem

Teen patti

Pot odds and the fundamental theorem

115SAIS IIMK 2013 1-5

Discussion points


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Discussion points

Is this a fair game? Two ways of making v1 game fair?

Manipulate

Odds

n ormat on ynam cs

116SAIS IIMK 2013 1-5



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Gamerules Volunteers

117SAIS IIMK 2013 1-5

Discussion points


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Discussion points

Is this a fair game? Two ways of making v1 game fair?

Manipulate

Odds

n ormat on ynam cs

118SAIS IIMK 2013 1-5

Applications isomorphism


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Applications, isomorphism

Settlement between plaintiff and defendant Defendant offers a generous (fold) or stingy (bet)

settlement

Plaintiff folds (accepts the stingy settlement) or calls

re ects the stin settlement

Tax filing and auditing decisions

119SAIS IIMK 2013 1-5

Bargaining Initiating


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BargainingInitiating

Tournaments Increasing blinds and antes

Final table

Which processes of bargaining succeed? Who initiates them?

Why online tournaments?

120SAIS IIMK 2013 1-5


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SAIS session 11; 08/13/13

Term IV, 2013





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10 I Peloton analytics Cooperate Compete decisions




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14 I Bidding for resources coattail effect Understanding bundling

Sports Franchise simulation game Competing for franchise, resources


er ormance an atten ance n erstan ng causa ty

123SAIS IIMK 2013 1,2

Executive pay regulation


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Executive pay regulation

Bone of contention Value creators or selfish risk takers?

Should government intervene?

Tax a er ex ectations ost a bail-out?

124SAIS IIMK 2013 1-5

Executive pay regulationwhat can be learnt

f j l ?


125/166

from major league sports?

Salaries of sports professionals discussedwidely, data available

125SAIS IIMK 2013 1-5

Executive pay regulationwhat can be learnt

f j l ?


126/166

from major league sports?

Executives and athletes pay Similar in $ amount

Significant differences in the pay structure

Executives

ar a e pay om na e

Bonus, stock grant, stock options, long-term incentive

payouts (6080%)

Athletes

Mainly fixed, small variable component (~25%)

Performance linked, extra-ordinary performance linked

126SAIS IIMK 2013 1-5

Pay regulation in pro-sports


127/166

y g p p

Why regulate players salaries? Competitive balance

Ruinous salary cost escalation

127SAIS IIMK 2013 1-5


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p a w va# SAIS content area Wider business relevance


















130/166

p# SAIS content area Wider business relevance







130SAIS IIMK 2013 1,2

SDP - 2


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Team-up and play 10 hands of SDP (with re-raising)

From 11th hand onward Antes double

There are some extra things that the playerscan do

131SAIS IIMK 2013 1-5

SDP - 2


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Did either of the players negotiate? Who began the negotiation?

When did the negotiation begin?

Relative osition when ne otiation be an? Was an agreement reached?

What were the terms and conditions?

Who benefited?

132SAIS IIMK 2013 1-5

Online Tournamentfeatures


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Wide range of stakes (buy-in) Typical

~1000 players

~100 in the money finishers

~25% of prize money for the top finisher

Insane volumes

> 1,246 for 1Q, 1 research, 1 range of stakes, 1room

Average $80K per tournament

133SAIS IIMK 2013 1-5

Final tabledeals


134/166

Anybody can initiate Anybody can veto

Often before any deal specifics emerge

Play paused to accommodate chat for the deals Aggregators provide player (only nicks of

course) stats

Quality, skill level can be researched

134SAIS IIMK 2013 1-5

Initiating BargainingDavid Goldreich and Lukasz Pomorski 2011 Review of Economic Studies


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David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies

Data Table 1: p. 1303

Only a subset of tournaments played on this single

site in 1Q of 2007

135SAIS IIMK 2013 1-5

Key questions pertain to


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y q p

Delay in bargaining Identity of the initiator

Initiators effect on the completion of the

bargaining successfully Imitators effect on the terms of the deal

What is conducive to reaching agreement

through the bargaining process

136SAIS IIMK 2013 1-5


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SAIS IIMK 2013 1,2 137

Why bargain?David Goldreich and Lukasz Pomorski 2011 Review of Economic Studies


138/166


Two players with log utility functions Two equal skill players occupy 1, 2 spot

Negotiated payoffCE of continuing to play

SAIS IIMK 2013 1,2 138

Expected value of the tournamentIndependent chip model (ICM)


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p p ( )David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies

139SAIS IIMK 2013 1-5

Expected value of the tournamentIndependent chip model (ICM)


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p p ( )David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies

140SAIS IIMK 2013 1-5

When does bargaining


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occur?David Goldreich and Lukasz Pomorski, 2011,

Review of Economic Studies

141SAIS IIMK 2013 1-5

Who initiates bargaining?


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142SAIS IIMK 2013 1-5

Equality and the success of negotiations

other measures


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other measuresDavid Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies

143SAIS IIMK 2013 1-5


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SummaryD id G ld i h d L k P ki 2011 R i f E i S di


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145SAIS IIMK 2013 1-5

SummaryD id G ld i h d L k P ki 2011 R i f E i S di


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Delay in

bargaining

Identity of

initiator

Initiators

effect oncompletion

ofbargaining

Initiators

effect onterms of

deal

Characterist

ics of theenvironment

conducive tobargaining

Empirical

results

Bargaining

occurs with a

Initiator

typically in

Deals more

likely when

No effect.

Leader

Larger gains

to trade; high

146SAIS IIMK 2013 1-5

delay or not

at all

position;

initiator less

likely to be

experienced

or highly

ranked

stronger

position or

experienced/

highly

ranked

,in the middle

gets

squeezed

of outside

options; few

remaining

players; few

experienced

players Theoretical lenses

complete information bargaining, cib with learning, cibwith biases (overconfidence), incomplete information,dissolving a partnership (Cramton et al., 1987)


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SAISsession 14, 15; 08/21-22/13

Term IV, 2013





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# SAIS content area Wider business relevance


















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# SAIS content area Wider business relevance

12 I Value of roster flexibility Taskforce composition13 I Home advantage Conformity bias and Audit functions





149SAIS IIMK 2013 1,2


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Measuring the Coattail effect


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JuniorSenior; SeniorJunior coattails

151SAIS IIMK 2013 1-5

poss e

Measuring the Coattail effect


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JuniorSenior; SeniorJunior coattails

152SAIS IIMK 2013 1-5

poss e

11,540 college players, 890 schools, 1984

2003 drafts


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2003 drafts

153SAIS IIMK 2013 1-5

2.66 players drafted from a given school in seasons with a possible

coattail effect, only .43 otherwise

Value of a draft pick and the coattail effect


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Does Coattail effect mean that teams aremaking costly/ less than optimal decisions

though?

=draft pick, sq, cube, Coattail)

154SAIS IIMK 2013 1-5

Value of a draft pick and the coattail effect


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Assumptions

155SAIS IIMK 2013 1-5

eams a ways c oose t e p ayer t ey ee to e t e

best availableprospectin the draft

But younger players may have more leverage

Sums demanded not known unless deal is done

But players who benefited from the coattail do

not


156/166

not

156SAIS IIMK 2013 1-5

Coattail Conclusion


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Six, Seven figure investments inlottery

tickets Additional .76 players drafted from a school in

a year where there is a top prospect (.43 Avg)

Effect seems to have otten stron er between1984 and 2003 (?)

But players who were teammates with topplayers actually outperformed

Most draft eligible college players recruitedthrough concurrent programs

157SAIS IIMK 2013 1-5

Where, closer to home, could we see a similar

Coattail phenomenon?


158/166

Coattail phenomenon?

158SAIS IIMK 2013 1-5

The Value of Flexibility in Baseball Roster

Construction


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Timothy CY Chan and Douglas S. Fearing, 2013, 7th Annual SSAC

Estimate Likelihood and duration of player injuries through

a season

Fielding abilities at secondary fielding

pos t ons

Robust optimization model to measuredegradation of team performance due to

injuries Measure difference in performance between

teams with and without positional flexibility

159SAIS IIMK 2013 1-5

Basic PlayerPosition Assignment Model


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160SAIS IIMK 2013 1-5

vij fraction of all innings in a season that player (factories) j is

assigned to position (products) i

J players, I positions

Player j can play up to cj innings per season

Each position must be assigned a player up to di innings per season,which can be further divided into L and R

Constraints to ensure catchers play at most 85% of the innings perseason

Assumptions


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Capability values are additive across players

Injuries which determine a players capacity

values happen before player-innings

Innings in which players are injured is

controlled by the team - ???

161SAIS IIMK 2013 1-5

Robust PlayerPosition Assignment Model


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162SAIS IIMK 2013 1-5

Augment basic model to allow an extra decision (by nature) to

determine the worst case combination of player injuries

I: budget of disruption given to Nature

Value of Flexibility in the absence of injuries


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163SAIS IIMK 2013 1-5

But what is the source of this flexibility?

Position Assignment for LAD w/ wo flexibilityno

injuries


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164SAIS IIMK 2013 1-5

Value of Flexibility with simulated injuries


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165SAIS IIMK 2013 1-5

Think about resources flexibility in cricket


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Bat, Bowl, All-rounder, W-all rounder, fielding IPL: foreign versus domestic players

LeftRight handed top-3 batsmen Left

Ri ht handed new ball bowlers LeftRight handed slow bowlers Left

Right handed batsmen

How would franchises strategize in various

bidding rounds?E l d biddi lt i t t l t

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Session 1-14 Strategic Analytics 0821.pdf

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