Marica Manisera Paola Zuccolotto - UNIBS
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Marica Manisera Paola Zuccolotto
paola.zuccolotto@unibs.it marica.manisera@unibs.it
Big Data analytics in sports
bdsports.unibs.it
ISI Special Interest Group on SPORTS STATISTICS https://www.isi-web.org/community/committees/special-interest-groups?id=127
Our Book
Package
developed by Marco Sandri
Agenda
1. Data Science in basketball
2. Basketball analytics: state of the art
3. Basketball data
4. Introduction to the R package
BasketballAnalyzeR
1 - Data Science in Basketball
What is Data Science? Discipline aimed at extracting knowledge from data in various forms Multidisciplinary Applicable to a wide range of fields
Data Science… • … aimes at extracting knowledge from the data (interpretation of results is extremely delicate) • … can deal with any field of human knowledge • … can potentially answer any question, if it has the right data • … will never be able to describe everything • … is not a crystal ball • … does not provide decisions, but support for decisions Basketball data science has no ambition to replace basketball experts, but to support them in their decisions
The virtuous cycle of Sports Analytics
Anatomy of a decision
Are stats killing the game of basketball?
(2017)
True: False:
• If people keep thinking that
Statistics is merely PPG, AST,
• If modern approaches to
REB, … basketball analytics are used
• If people don’t learn how • If we are able to integrate
Stats have to be interpreted analytics and technical
(“Do not put your faith in experience
what statistics say until you • If we are able to spread the
have carefully considered culture of Statistics
what they do not say.”
W. W. Watt)Are stats killing the game of basketball?
“…the confidence of the person who
showed me the data convinced me
about them. We were used to seeing
images of games, but they
transformed those images into
numbers. Once you have the data,
they help you make better
decisions”…. [they should] “educate
players on the importance and
benefits of data. The best thing they
can do is make the most of them to
squeeze as much as possible out of
games, as data is very important
and beneficial for winning”
(2021)2 – Basketball Analytics: state of the art
Basketball Analytics
Basketball Analytics
Basketball Analytics
Basketball Analytics
Scientific journals
Scientific literature Special
IssuesBasketball Analytics
Scientific literature
• Predicting the outcomes of a game or a tournament
• Determining discriminating factors between successful and
unsuccessful teams
• Examining the statistical properties and patterns of scoring during
the games
• Analysing a player's performance and the impact on his team's
chances of winning
• Monitoring playing patterns with reference to rolesBasketball Analytics
Scientific literature
• Designing the kinetics of players' body movements with respect to
shooting efficiency, timing and visual control on the field
• Depicting the players' movements, pathways, trajectories and the
network of passing actions, the flow of events and the connected
functional decisions
• Studying teams' tactics and identifying optimal game strategies
• Investigating possible referee biasesBasketball Analytics
Scientific literature
• Measuring psychological latent variables and their association to
performance
• Epidemiology of basketball injuries, physical, anthropometric and
physiological attributes of players, hematological parameters or
other vitals
• Special training programmes to stimulate muscle strength, jumping
ability and physical fitness in general
• Scheduling problemsBasketball Analytics
Scientific literature
• This list is far from being complete
• The range of possible research questions is going to grow, thanks to
the availability of large data sets and the incresaing computational
power
• A complete theory explaining the relationships among the variables
involved in basketball analytics is still not available
→ Answering to all those questions is a very interesting challenge
for Data Scientists3 – Basketball data
Basketball Data
Data are essential to Data science and Analytics, so the
procedures for obtaining and organizing data sets must be
structured and validated to guarantee Quality:
Exhaustiveness
Accuracy
Completeness
Consistency
Accessibility
TimelinessBasketball Data
Another important issue about data is Context (all the
additional information necessary to correctly interpret data):
“Data without context are just numbers”
Several sources (Federations, sporting organizations,
professional societies, associations, …)Basketball Data
The web is a massive store of data:
• Data on payment or freely available
• Open data often require web scraping procedures
• Variety of datasets (traditional data matrices, multidimensional
data cubes, unstructured text data, pixels from sensors and
cameras, data from wearables, mobile phones, tablets, geocode,
timestamps, …), requiring relational databases and
datawarehousing toolsBasketball Data We can distinguish four main macro-categories: • Data recorded manually • Data detected by technological devices • Data from psychometric questionnaires • Other data
Basketball Data
Basketball Data
Basketball Data
Basketball Data
Basketball Data
Data Big Data
www.espn.com/nba
stats.nba.com
www.fiba.com
Leagues
…Basketball Data Data Big Data
Basketball Data Data Big Data
4 - Introduction to the R package
BasketballAnalyzeRBook and codes
Book and codes
https://bdsports.unibs.it/basketballanalyzer/
Install R and BasketballAnalyzeR
Data
data(package="BasketballAnalyzeR")
• Tbox – Teams’ box scores NBA Regular Season 17/18
• Obox – Opponents’ box 82 games
scores
• Pbox – Players’ box scores Play-by-play: 82 games played by
the Champions, Golden State Warriors
• PbP.BDB – Play-by-play data (made available by BigDataBall
www.bigdataball.com)
• Tadd – Additional informationData data(package="BasketballAnalyzeR")
Data data(package="BasketballAnalyzeR")
Data
Boxscores (1., 2., 3.) and Additional information (5) are about all the
teams and players of the 82 games in the regular season of the NBA
championship 2017/2018
Play-by-play data are relative to the 82 games played by Golden
State Warriors (the champions) during the regular season (data made
available by BigDataBall, www.bigdataball.com)
18/19 NBA boxscores and play-by-play data of Cleveland Cavaliers
(17/18) are available at
https://bdsports.unibs.it/basketballanalyzer/Data data(package="BasketballAnalyzeR")
R script bdsports.unibs.it/basketballanalyzer/
• Basic Statistical Analyses • Discovering patterns in data • Finding groups in data • Modelling relationships in data
• Basic Statistical Analyses • Discovering patterns in data • Finding groups in data • Modelling relationships in data
• Basic Statistical Analyses • Discovering patterns in data • Finding groups in data • Modelling relationships in data
•
•
•
•
Finding groups in data
Basic Statistical Analyses
Discovering patterns in data
Modelling relationships in data
0 20 40 60 80
Ryan Anderson
Troy Daniels
Darius Miller
Kyle Korver
Anthony Tolliver
Andre Iguodala
Luc Mbah a Moute
Tony Snell
Garrett Temple
Justin Jackson
Bryn Forbes
Frank Kaminsky
Tobias Harris
Reggie Bullock
Jamal Crawford
Bobby Portis
Zach Randolph
Harrison Barnes
Aaron Gordon
Lauri Markkanen
Kevin Love
Danny Green
AlFarouq Aminu
Marvin Williams
Dirk Nowitzki
PJ Tucker
Stanley Johnson
Mario Hezonja
Jeff Green
Evan Turner
Dwight Powell
Michael KiddGilchrist
Justise Winslow
David Nwaba
Pascal Siakam
Dewayne Dedmon
JaMychal Green
Wilson Chandler
Dragan Bender
Robin Lopez
Jerami Grant
Al Horford
Pau Gasol
TJ Warren
Rondae HollisJefferson
Nikola Vucevic
Brandon Ingram
Michael Beasley
Kyle Anderson
Dejounte Murray
Kent Bazemore
Kris Dunn
Caris LeVert
DeAaron Fox
Frank Ntilikina
Lance Stephenson
Jonathon Simmons
Courtney Lee
Shabazz Napier
Darren Collison
Cory Joseph
Fred VanVleet
Gary Harris
Jimmy Butler
JJ Barea
Nicolas Batum
Tyreke Evans
DJ Augustin
Lonzo Ball
Rajon Rondo
Jarrett Jack
TJ McConnell
Marcus Smart
Tyler Ulis
Ish Smith
Jerian Grant
Tomas Satoransky
Josh Richardson
Robert Covington
Jayson Tatum
Andrew Wiggins
Thaddeus Young
Otto Porter Jr
Kelly Oubre Jr
Dillon Brooks
Josh Jackson
James Johnson
Kelly Olynyk
Markieff Morris
Denzel Valentine
Jaylen Brown
DeMarre Carroll
Carmelo Anthony
Kyle Kuzma
Dario Saric
Kyrie Irving
Stephen Curry
Wayne Ellington
JJ Redick
Klay Thompson
Eric Gordon
Kentavious CaldwellPope
Buddy Hield
Trevor Ariza
Terry Rozier
Justin Holiday
Wesley Matthews
ETwaun Moore
Austin Rivers
Bogdan Bogdanovic
Allen Crabbe
Joe Harris
Patty Mills
Bojan Bogdanovic
Tim Hardaway Jr
Evan Fournier
JR Smith
Yogi Ferrell
Jeremy Lamb
Tyler Johnson
James Harden
LeBron James
Russell Westbrook
Jeff Teague
Ricky Rubio
Eric Bledsoe
Jrue Holiday
Khris Middleton
Donovan Mitchell
Victor Oladipo
Paul George
Lou Williams
Kemba Walker
Damian Lillard
Spencer Dinwiddie
Chris Paul
Kyle Lowry
Joe Ingles
Devin Booker
Goran Dragic
Dennis Schroder
Dennis Smith Jr
Will Barton
Taurean Prince
Jamal Murray
DeMar DeRozan
CJ McCollum
Bradley Beal
Kristaps Porzingis
Myles Turner
Brook Lopez
Serge Ibaka
John Henson
Jakob Poeltl
Jarell Martin
Marquese Chriss
Domantas Sabonis
Enes Kanter
Jonas Valanciunas
Derrick Favors
Marcin Gortat
Willie CauleyStein
John Collins
Taj Gibson
LaMarcus Aldridge
Clint Capela
Rudy Gobert
Julius Randle
Steven Adams
Jusuf Nurkic
DeAndre Jordan
Andre Drummond
Dwight Howard
KarlAnthony Towns
Giannis Antetokounmpo
Ben Simmons
Nikola Jokic
Draymond Green
Anthony Davis
Kevin Durant
Joel Embiid
Marc Gasol
DeMarcus Cousins• Basic Statistical Analyses • Discovering patterns in data • Finding groups in data • Modelling relationships in data
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