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Sports Analytics: Consultant Perspective
J. Michael (Mike) Boyle The University of Utah
Utah Winter Operations Conference, February 2013
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Objective and Approach
• Objective
– Identify opportunities for future research
• Approach
– Provide examples of decision-making in the National Hockey League (NHL)
– Describe the data available to understand/model decision-making
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Presentation overview
• Background – Attendees – Presenter – Hockey – NHL – NHL Organizations – Our clients
• Data and technology infrastructure • Hockey operations objectives and decision-making
– 3+ years – Current year – Current day/week/2-week period – In-game
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BACKGROUND
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Attendees
• Expectations/objectives for this session
• Research area(s) overview
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Presenter
• Education – Computer Science
• Industry – Consulting – Software Engineering – Product Management – Analytics
• Teaching – Business Intelligence and Analytics (strategies and applications) – Web Analytics – Data Warehousing – Databases
• Objectives for this session
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Hockey
Source: http://en.wikipedia.org/wiki/Ice_hockey_rink, accessed on Feb. 8, 2013
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…and then there’s this part
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National Hockey League (NHL)
• Revenues for the 2011-12 season: $3.4 billion • 30 teams:
– Canada(7); USA (23) – Profitable (17); Not Profitable (13)
• NHL Board of Governors (one governor per team) • Executives
– Commissioner – Deputy Commissioner – Chief Legal Officer – CFO – Dir. of Hockey Operations – SVP of Player Safety
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Hockey Organizations
Hockey Operations Coaching Scouting Player
Development
Executive
Players
Ownership
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Analytics in organizations today
• Highlights*
– Top-performing organizations are twice a likely to apply analytics to activities
– The biggest challenges in adopting analytics are managerial and cultural
– Visualizing data differently will become increasingly valuable
* Source: LaValle et al, “Big Data, Analytics and the Path From Insights to Value”, MIT Sloan Management Review (2011)
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“The biggest challenges in adopting analytics are managerial and cultural”
• Story: “Your model works for all of our players with the exception of John Smith.”
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“The biggest challenges in adopting analytics are managerial and cultural”
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“The biggest challenges in adopting analytics are managerial and cultural”
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Our clients
• Recall: Top-performing organizations are twice a likely to apply analytics to activities*
* Source: LaValle et al, “Big Data, Analytics and the Path From Insights to Value”, MIT Sloan Management Review (2011)
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Our clients: Competing on Analytics Optimization What’s the best that can
happen?
Predictive modeling What will happen next
Forecasting/extrapolation What if these trends continue?
Statistical analysis Why is it happening?
Alerts What actions are needed?
Query/drill down Where exactly is the problem?
Ad hoc reports How many, how often, where?
Standard reports What happened?
Sophistication
Co
mp
etit
ive
Ad
van
tage
An
alytics R
epo
rting
Source: Davenport et al, “Competing on Analytics,” Harvard Business Press (2007)
From Troy’s presentation: “…manage them better…”
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DATA AND TECHNOLOGY INFRASTRUCTURE
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Data
• Event level (shots, hits, penalties, etc.) – Each event type has additional attributes (e.g. shot has: shot
type, location, location on net)
• Player-game level (e.g. player time on ice by manpower) • Team-game level • Game level • Player demographics • Salary • Derived • 2007 – present • NHL • Some American Hockey League data (AHL)
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Technology infrastructure
Business Data Marts
Data Source n
Data Source 2
Data Source 1
Data Staging Services: • Clean • Standardize • Full load vs.
Partial/Real-time load
• Derived metrics and dimensions
• Descriptive and predictive models implemented
Data Mart 1 (Biz)
Data Access Tools
Data Mart 2 (Biz)
Data Mart m (Biz)
Research Data Marts Data Mart 1 (Research)
Data Mart 2 (Research)
Data Mart m (Research)
• Pentaho User Console (BI client)
• Recurring Reports (e.g. PDF/Excel)
• Saiku Analysis • Excel • Tableau • R/Stata • SQL Client
Extract
Extract
Extract
Load
Load
Access
• R/Stata • SQL Client • Excel • Tableau
Access
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HOCKEY OPERATIONS: OBJECTIVES AND DECISION-MAKING
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Objectives: 3+ years
• Have an “acceptable” level of success each year
• Draft effectively
• Develop players effectively
• Make effective personnel changes
– Cap management
– Team strategy
• Question: Is there anything missing?
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Objectives: Current year
• Make the play-offs
• Get to the nth round/win the play-offs
• Make effective personnel changes
– E.g. Trade unrestricted free agents when the play-offs are not achievable
• Question: Is there anything missing?
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Objectives: Current day/week/2-week period
• Perform as expected
– Win games the right way
– Lose games that should be lost
• Players perform as expected
• Question: Is there anything missing?
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Objectives: In-game
• Win
• Stay healthy
• Question: Is there anything missing?
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Decision-making: 3+ years
• Decisions:
– Who to draft
– Which personnel changes to make
– What a player is worth
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Decision-making: Current year
• Decisions:
– What to do with the compressed schedule this season
– Story: “I don’t give the scouts the reports. I use them to make sure our scouts are doing their jobs effectively.”
– How to adjust for injuries
– Dump unrestricted free agents or aim for a championship
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Decision-making: Current day/week/2-week period
• Decisions: – Regular pro/amateur scouting – How to help players perform to expectations
• Story: “Why isn’t John Smith scoring at historical rate?”
– Game preparation • Which approach to use against upcoming opponent • What 3 focus-items to provide players • How long a player’s shift should be • How many minutes a player should play
– Which goaltender to play
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Decision-making: In-game
• When to pull the goalie
• When to change the approach based on winning/losing
• How will a fight impact the likelihood of a win
• Dump-and-chase or carry the puck into the offensive zone
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Challenges
• (general ones covered earlier) • Player market isn’t very liquid • Individual vs. team incentives • Modeling
– Players are playing offense and defense at the same time
– Players play differently based on the state of the game – Interactions between line-mates and opponents – Some key gaps in the data that are captured – Limited amount of data in the NHL’s “feeder” leagues
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Challenges (cont’d)
• Getting side-tracked on analytics
– Chasing the “perfect” metric(s)
– Investing too heavily in technology relative to great analysts
– Waiting for more/new/cleaner data to support decision-making
– Perception of immunity to and/or lack of understanding of human biases in decision-making