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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
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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
3 R Performance metricswhen less is
more (basketball rebounds)
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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Topics and their wider relevance
# 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
3 R Performance metricswhen less is
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
7 R Measuring and valuing wins produced Unit to enterprise performance link
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
Indian Institute of Management KozhikodeInstructor: Deepak Dhayanithy
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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
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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
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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 (?)
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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
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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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SAIS session 3, 06/26/13
Term IV, 2013
Indian Institute of Management KozhikodeInstructor: Deepak Dhayanithy
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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
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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)
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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
2 P Doping dilemma Plagiarism, cutting corners
Go for it Loss aversion Negotiations,
3 R Performance metricswhen less is
more (basketball rebounds)
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 mgmt
9 I Signaling, bluffing and bargaining Negotiations
10 I Peloton analytics CooperateCompete decisions
11 I Monopsony and salary suppression Executive pay regulation63SAIS IIMK 2013 1,2
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Topics and their wider relevance# SAIS content area Wider business relevance
12 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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SAIS session 4, 06/28/13
Term IV, 2013
Indian Institute of Management KozhikodeInstructor: Deepak Dhayanithy
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Matching Law
Log (R1/ R2)
Log(B1/B2)
Log (R1/ R2)
Log(B1/B2)
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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]
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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)
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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
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SAIS session 5, 07/10/13
Term IV, 2013
Indian Institute of Management KozhikodeInstructor: Deepak Dhayanithy
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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
2 P Doping dilemma Plagiarism, cutting corners
Go for it Loss aversion Negotiations,
3 R Performance metricswhen less is
more (basketball rebounds)
P(D) to P(D) + LGD
4 R Matching law (going for 3) Project incentives, Skilldevelopment
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 hand phenomenon or fallacy Taskforce, high value resource mgmt
9 I Signaling, bluffing and bargaining Negotiations
10 I Peloton analytics CooperateCompete decisions
11 I Monopsony and salary suppression Executive pay regulation 71SAIS IIMK 2013 1,2
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Topics and their wider relevance# SAIS content area Wider business relevance
12 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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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
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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
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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
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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
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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
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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
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SAIS session 6, 07/17/13
Term IV, 2013
Indian Institute of Management Kozhikode
Instructor: Deepak Dhayanithy
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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
2 P Doping dilemma Plagiarism, cutting corners
Go for it Loss aversion Negotiations,
3 R Performance metrics when less is
more (basketball rebounds)
P(D) to P(D) + LGD difficulty
level
4 R Matching law (going for 3) Project incentives, Skilldevelopment
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 hand phenomenon or fallacy Taskforce, high value resource mgmt
9 I Signaling, bluffing and bargaining Negotiations
10 I Peloton analytics CooperateCompete decisions
11 I Monopsony and salary suppression Executive pay regulation 82SAIS IIMK 2013 1,2
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Topics and their wider relevance# SAIS content area Wider business relevance
12 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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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?
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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
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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
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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
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[Logistic] regression process checklist
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[Logistic] regression process checklist
Y variable definition
Univariate analysis
Missing values treatment
Flooring, capping of key variables
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[Logistic] regression process checklist contd
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[Logistic] regression process checklistcontd.
Binning
Automatic selection models
Multi-collinearity
Or micro-numerosity?
Sense-makin
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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
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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
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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
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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
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Model scrutiny
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Model scrutiny
Concerns
Privacy Fair lending
Consumer protection
Efficacy Recall Prof. Choudhurys talk
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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
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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?
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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
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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
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SAIS session 8; 07/25/13
Term IV, 2013
Indian Institute of Management Kozhikode
Instructor: Deepak Dhayanithy
[Logistic] regression process checklist
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[Logistic] regression process checklist
Y variable definition
Univariate analysis
Missing values treatment
Flooring, capping of key variables
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[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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Topics and their wider relevance# SAIS content area Wider business relevance
1 P Competitive balance, open/ closed
league, free agency
Key aspects of designing and
managing competition
2 P Doping dilemma Plagiarism, cutting corners
Go for itLoss aversion Negotiations,
3 R Performance metricswhen less is
more (basketball rebounds)
P(D) to P(D) + LGDdifficulty level
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 hand phenomenon or fallacy Taskforce, high value resource mgmt
9 I Signaling, bluffing and bargaining Negotiations, Litigation
10 I Peloton analytics CooperateCompete decisions
11 I Monopsony and salary suppression Executive pay regulation102SAIS IIMK 2013 1,2
Topics and their wider relevance
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Topics and their wider relevance# SAIS content area Wider business relevance
12 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
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?
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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
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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
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Questions
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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)?
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Location based Advertising
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Location based Advertising
Mobile Business idea
Beta testing
Alternative measures
Footfall
Footfall + sale
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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
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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
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Sport application?
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Sport application?
Specialists versus bits and pieces bowlers
Penalty corners
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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?
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Stripped down poker v1
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Stripped down pokerv1
Gamerules Volunteers
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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
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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
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Stripped down poker v2
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Stripped down poker v2
Gamerules Volunteers
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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
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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
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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?
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SAIS session 11; 08/13/13
Term IV, 2013
Indian Institute of Management Kozhikode
Instructor: Deepak Dhayanithy
Topics and their wider relevance
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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
2 P Doping dilemma Plagiarism, cutting corners
Go for itLoss aversion Negotiations,
3 R Performance metricswhen less is
more (basketball rebounds)
P(D) to P(D) + LGDdifficulty level
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 hand phenomenon or fallacy Taskforce, high value resource mgmt
9 I Signaling, bluffing and bargaining Negotiations, Litigation
10 I Peloton analytics Cooperate Compete decisions
11 I Monopsony and salary suppression Executive pay regulation122SAIS IIMK 2013 1,2
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Topics and their wider relevance# SAIS content area Wider business relevance
12 I Value of roster flexibility Taskforce composition
13 I Home advantage Conformity bias and Audit functions
14 I Bidding for resources coattail effect Understanding bundling
Sports Franchise simulation game Competing for franchise, resources
15 F Scheduling Media management
er ormance an atten ance n erstan ng causa ty
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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?
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Executive pay regulationwhat can be learnt
f j l ?
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from major league sports?
Salaries of sports professionals discussedwidely, data available
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Executive pay regulationwhat can be learnt
f j l ?
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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
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Pay regulation in pro-sports
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y g p p
Why regulate players salaries? Competitive balance
Ruinous salary cost escalation
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Topics and their wider relevance
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p a w va# SAIS content area Wider business relevance
1 P Competitive balance, open/ closedleague, free agency
Key aspects of designing andmanaging competition
2 P Doping dilemma Plagiarism, cutting corners
Go for itLoss aversion Negotiations,
3 R Performance metricswhen less is
more (basketball rebounds)
P(D) to P(D) + LGDdifficulty level
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 hand phenomenon or fallacy Taskforce, high value resource mgmt
9 I Signaling, bluffing and bargaining Negotiations, Litigation
10 I Peloton analytics CooperateCompete decisions
11 I Monopsony and salary suppression Executive pay regulation129SAIS IIMK 2013 1,2
Topics and their wider relevance
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p# SAIS content area Wider business relevance
12 I Value of roster flexibility Taskforce composition
13 I Home advantage Conformity bias and Audit functions
14 I Bidding for resources coattail effect Understanding bundling
Sports Franchise simulation game Competing for franchise, resources
15 F Scheduling Media management
er ormance an atten ance n erstan ng causa ty
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
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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?
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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
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Final tabledeals
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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
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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
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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
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SAIS IIMK 2013 1,2 137
Why bargain?David Goldreich and Lukasz Pomorski 2011 Review of Economic Studies
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David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies
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
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Expected value of the tournamentIndependent chip model (ICM)
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p p ( )David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies
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When does bargaining
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occur?David Goldreich and Lukasz Pomorski, 2011,
Review of Economic Studies
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Who initiates bargaining?
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David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies
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Equality and the success of negotiations
other measures
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other measuresDavid Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies
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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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David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies
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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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David Goldreich and Lukasz Pomorski, 2011, Review of Economic Studies
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
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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
Indian Institute of Management Kozhikode
Instructor: Deepak Dhayanithy
Topics and their wider relevance
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# SAIS content area Wider business relevance
1 P Competitive balance, open/ closedleague, free agency
Key aspects of designing andmanaging competition
2 P Doping dilemma Plagiarism, cutting corners
Go for itLoss aversion Negotiations,
3 R Performance metricswhen less is
more (basketball rebounds)
P(D) to P(D) + LGDdifficulty level
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 hand phenomenon or fallacy Taskforce, high value resource mgmt
9 I Signaling, bluffing and bargaining Negotiations, Litigation
10 I Peloton analytics CooperateCompete decisions
11 I Monopsony and salary suppression Executive pay regulation148SAIS IIMK 2013 1,2
Topics and their wider 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
14 I Bidding for resources coattail effect Understanding bundling
Sports Franchise simulation game Competing for franchise, resources
15 F Scheduling Media management
er ormance an atten ance n erstan ng causa ty
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Measuring the Coattail effect
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JuniorSenior; SeniorJunior coattails
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poss e
Measuring the Coattail effect
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JuniorSenior; SeniorJunior coattails
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poss e
11,540 college players, 890 schools, 1984
2003 drafts
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2003 drafts
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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)
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Value of a draft pick and the coattail effect
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Assumptions
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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
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not
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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
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Where, closer to home, could we see a similar
Coattail phenomenon?
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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 - ???
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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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But what is the source of this flexibility?
Position Assignment for LAD w/ wo flexibilityno
injuries
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Value of Flexibility with simulated injuries
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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