University of Salerno, profile picture
Uploaded byUniversity of Salerno
PDF, PPTX75 views

Players Movements and Team Performance

The document outlines a study on the interaction between players' movements and team performance in basketball, utilizing data mining techniques and global positioning systems (GPS). It details the methodology for data collection from professional games, including the use of clustering analysis and play-by-play reports to analyze team shooting performance and spatial relations. Future research directions include exploring external factors such as coaching tactics and integrating ball trajectory data.

Embed presentation

Download as PDF, PPTX
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Players Movements and Team Shooting Performance: a Data Mining approach for Basketball. Rodolfo Metulini University of Brescia - Department of Economics and Management Palermo - June 20th, 2018
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Table of contents 1 Overview 2 Data & Methods 3 Analysis & Results 4 Future developments 5 Acknowledgm. & References
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Aims To study the interaction between players in the court, in relation to team performance Analysts want to explain movements in reaction to a variety of factors and in relation to team performance • Goal I: to segment the game into homogeneous phases in terms of players’ spatial relations, to retrieve significant moments of the game • Goal II: to characterize game phases in terms of team (shooting) performance
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Global Positioning Systems (GPS) • Object trajectories capture the movement of players or the ball • Trajectories are captured using optical- or device-tracking and processing systems • The adoption of this technology and the availability of data is driven by various factors, particularly commercial and technical
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Play-by-play Play-by-play (or ‘event-log”) reports a sequence of relevant events that occur during a match • Players’ events (shots, fouls) • Technical events (time-outs, start/end of the period) • Web scraping techniques (user-friendly R and Phyton routines) • Video analysis
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Cluster Analysis in Sport Literature • Sampaio & Janeira (2003) investigate the discriminatory power of game statistics between winning and losing teams in the Portuguese Professional Basketball League • Carpita et al. (2013,2015) identify the drivers that most affect the probability to win a football match • Ross (2007) segment team sport spectators identifying potential similarities according to demographic variables • Gonccalves (2018), using GPS data, applies a two-step cluster to classify the regularity in team-mates dyads positioning.
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Cluster analysis on time instants Metulini, Manisera & Zuccolotto (2017) split an amatorial basketball game in a number of separate time-periods, each identifying homogeneous spatial relations among players in the court Improvements in 2018 paper: • analysis extended to multiple matches, • use of professional basketball games, • use of the active moments of the games only (applying a filtering procedure on the initial dataset), • introduction of transition moments. • We characterize clusters in terms of team performance
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Our dataset(s) • We have tracked data from three games played by Italian professional basketball teams, at the Italian Basketball Cup Final Eight. Data provided by MYagonism MYa • Each player worn a microchip, collecting the position (1 cm2 pixels), velocity and acceleration in the x-axis (court length), y-axis (court width), and z-axis (height) • The initial dataset considers the full game length, for all the ms in which the system captured at least one player. We cleaned it by dropping inactive moments, according to our filtering procedure • The final dataset for team 1 counts for 206,332 rows, team 2 counts for 232,544 rows, while team 3 counts for 201,651 rows (Frequency: 80/90 Hz)
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Filtering Procedure From: Metulini, R., Filtering procedures for sensor data in basketball. Statistics & Applications. 2017-2 Full data matrix X (nrow = T); ↓ 1-A Remove row if players on the court = 5 ↓ 1-B Remove row if a player is on the free throw circle ↓ 1-C Remove row if players veloc- ity < h2 for h3 consecutive seconds ↓ Reduced data matrix (nrow = T’ ≤ T) ↓ 2-A Assign offense or defense labels ↓ 2-B Assign actions’ sorting ↓ Reduced data matrix with actions’ labelling and sorting
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Approach - I • We consider 5 total lineups from 3 different games • Separately to each one, we apply a k-means cluster analysis to group objects • We group time instants • The similarity is expressed in terms of distance between players’ dyads. • We, consistently along different lineups, find k = 6 based on the value of the between deviance (BD) / total deviance (TD) ratio for different numbers of clusters
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Profiles plot 05101520 C1 13.31% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C2 19.76% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C3 3.4% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C4 29.8% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C5 6.41% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C6 27.31% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 Figure: Profile plots representing, for each of the 6 clusters, the average distance among players’ dyads. Lineup 1 in CS1. Profiles plot for other case studies Go to
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Multidimensional scaling −10 −5 0 5 10 −4−2024 C1 13.31% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C2 19.76% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C3 3.4% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C4 29.8% Dimension 1 Dimension2 13 6 7 8 −10 −5 0 5 10 −4−2024 C5 6.41% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C6 27.31% Dimension 1 Dimension2 1 3 6 78 Figure: Map representing, for each of the 6 clusters, the average position of the five players in the two dimensions. Lineup 1 in CS1. Multidimensional scaling for other case studies Go to
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Transition matrix Cluster 1 2 3 4 5 6 TR 8.41 21.76 82.11 7.08 54.49 10.53 D 22.74 10.28 6.6 70.48 23.98 17.95 O 68.85 67.97 11.29 22.45 21.53 71.52 Table: Percentages of time instants classified in Transition (TR) Defense (D) or Offense (O), for each cluster. Lineup 1 in CS1. Cluster label C1 C2 C3 C4 C5 C6 C1 - 11.27 10 8.45 15 10.34 C2 31.03 - 10 23.94 15 35.34 C3 0.00 1.41 - 0.00 0 7.76 C4 34.48 21.13 0 - 25 35.34 C5 3.45 4.23 0 4.23 - 11.21 C6 31.03 61.97 80 63.38 45 - Table: Transition matrix reporting the relative frequency subsequent moments (t, t + 1) report a switch from a group to a different one. Lineup 1 in CS1.
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Team Shooting Performance - Video Analysis Play-by-play data are not freely available online for this tournament We retrieve shoots by doing a video analysis: • We install an app in the smartphone to take trace of time (aTimeLogger) • We open the video of the match, which is available online • We trace made/missed shoots with aTime Logger while running the video • The App create a .txt report with the shooting events and related ms, which can be attach to the final dataset
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Team Shooting Performance Cluster label Made Missed Total % C1 2 2 4 50% C2 0 2 2 0% C3 0 0 0 - C4 0 0 0 - C5 0 1 1 0% C6 5 3 8 62.5% Lineup 1 7 8 15 46.7% Table: Team Shooting performance. Lineup 1 in CS1. Duration: 8m:21s
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Examples An example of offensive play which ends in Cluster C6 An example of a transition-to-offensive play which ends in Cluster C6
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Highlights • Spacing matters • The team (shot-) performs better when a player is free to shot • The team goes in search of the best pattern for a good shot (average length in a cluster: 2 seconds)
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Future research • Examining the effect of external factors, such as coach advices (playbooks, tactics) • Including ball trajectories • ...
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References Acknowledgements Big & Open Data Innovation (BODaI) laboratory BODaI Big Data analytics in Sports (BDS) laboratory BDS Paola Zuccolotto, Marica Manisera (University of Brescia) and Tullio Facchinetti (University of Pavia)
Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. & References References 1. Sampaio, J., Janeira, M.: Statistical analyses of basketball team performance: understanding teams wins and losses according to a dierent index of ball possessions. International Journal of Performance Analysis in Sport 3.1 (2003): 40-49. 2. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P. (2013). Football mining with r. Data Mining Applications with R. 3. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P. (2015). Discovering the drivers of football match outcomes with data mining. Quality Technology & Quantitative Management 4. Ross, S. D.: Segmenting sport fans using brand associations: A cluster analysis. Sport Marketing Quarterly, 16.1 (2007): 15. 5. Gonalves, B. S. V.: Collective movement behaviour in association football. UTAD Universidade de Tras-os-Montes e Alto Douro (2018) 6. Metulini, R., Marisera, M., Zuccolotto, P.: Space-Time Analysis of Movements in Basketball using Sensor Data. Statistics and Data Science: new challenges, new generations SIS2017 proceeding. Firenze Uiversity Press. eISBN: 978-88-6453-521-0 (2017). 7. Metulini, R.: Filtering procedures for sensor data in basketball. Statistics&Applications 2 (2017).
Movements and Performance Metulini More 0510152025 C1 11.32% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C2 24.45% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C3 14.42% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C4 6.21% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C5 40.4% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C6 3.2% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 (a) lineup 2, CS1 05101520 C1 2.17% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C2 34.52% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C3 8.4% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C4 10.59% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C5 9.18% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C6 35.14% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 (b) lineup 1, CS2 051015 C1 5.46% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C2 20.5% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C3 4.3% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C4 12.65% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C5 36.11% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C6 20.98% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 (c) lineup 2, CS2 05101520 C1 26.55% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C2 4.49% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C3 3.85% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C4 20.92% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C5 37.8% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C6 6.39% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 (d) lineup 1, CS3 Back to Lineup1, CS1
Movements and Performance Metulini More −5 0 5 10 −40246 C1 11.32% Dimension 1 Dimension2 1 4 57 10 −5 0 5 10 −40246 C2 24.45% Dimension 1 Dimension2 1 4 5 7 10 −5 0 5 10 −40246 C3 14.42% Dimension 1 Dimension2 1 4 5 7 10 −5 0 5 10 −40246 C4 6.21% Dimension 1 Dimension2 1 4 5 7 10 −5 0 5 10 −40246 C5 40.4% Dimension 1 Dimension2 14 5 710 −5 0 5 10 −40246 C6 3.2% Dimension 1 Dimension2 1 4 5 7 10 (e) lineup 2, CS1 −10 −5 0 5 −4024 C1 2.17% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C2 34.52% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C3 8.4% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C4 10.59% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C5 9.18% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C6 35.14% Dimension 1 Dimension2 124 5 6 (f) lineup 1, CS2 −10 −5 0 5 −4024 C1 5.46% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C2 20.5% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5−4024 C3 4.3% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C4 12.65% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C5 36.11% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C6 20.98% Dimension 1 Dimension2 1 2 5 6 8 (g) lineup 2, CS2 −5 0 5 10 −4024 C1 26.55% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C2 4.49% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C3 3.85% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C4 20.92% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C5 37.8% Dimension 1 Dimension2 25 69 10 −5 0 5 10 −4024 C6 6.39% Dimension 1 Dimension2 2 5 6 9 10 (h) lineup 1, CS3 Back to Lineup1, CS1

More Related Content

PDF
Metulini280818 iasi
byUniversity of Salerno
 
PDF
Metulini, R., Manisera, M., Zuccolotto, P. (2017), Space-Time Analysis of Mov...
byUniversity of Salerno
 
PDF
Metulini, R., Manisera, M., Zuccolotto, P. (2017), Sensor Analytics in Basket...
byUniversity of Salerno
 
PDF
Metulini1503
byUniversity of Salerno
 
PDF
Detecting and classifying moments in basketball matches using sensor tracked ...
byUniversity of Salerno
 
PDF
Basketball players performance analytic as experiential learning approach
byNurfadhlina Mohd Sharef
 
PDF
BASKETBALL SPATIAL PERFORMANCE INDICATORS
byUniversity of Salerno
 
PPTX
NBA Player Statistics
byGanesh N Prasad
 
Metulini280818 iasi
byUniversity of Salerno
 
Metulini, R., Manisera, M., Zuccolotto, P. (2017), Space-Time Analysis of Mov...
byUniversity of Salerno
 
Metulini, R., Manisera, M., Zuccolotto, P. (2017), Sensor Analytics in Basket...
byUniversity of Salerno
 
Metulini1503
byUniversity of Salerno
 
Detecting and classifying moments in basketball matches using sensor tracked ...
byUniversity of Salerno
 
Basketball players performance analytic as experiential learning approach
byNurfadhlina Mohd Sharef
 
BASKETBALL SPATIAL PERFORMANCE INDICATORS
byUniversity of Salerno
 
NBA Player Statistics
byGanesh N Prasad
 

Similar to Players Movements and Team Performance

PPTX
Basketball matches outcomes prediction using data mining
byArioo Barzan
 
PDF
2012 UEFA Euro Efficiency Evaluation Based On Market Expectations
byNathan Mathis
 
PDF
Lineup Efficiency
byMatt Hanamoto
 
PPTX
NBA playoff prediction Model.pptx
byrishikeshravi30
 
PPTX
7 Ways Sports Teams Win With Sports Analytics
byTableau Software
 
PPTX
Winning in Basketball with Data and Machine Learning
byKonstantinos Pelechrinis
 
PDF
Olympics Sports Data Analysis
byIRJET Journal
 
PPTX
The Flexible “Motion Offense” as a Core Basketball Strategy
byCraig Raucher New York
 
PPTX
RLOEB PPT — SportVU
byRobbie Loeb
 
PDF
2018_04_Zuccolotto_Manisera_basketball_data_science.pdf
bypedro218150
 
PPTX
Performance Analysis Guide - AFC Kempston
byMichael Malone
 
PPTX
Data science in baseball
byKartikKakani1
 
PDF
Ai final module (1)
byDevansh Chawla
 
PPTX
Week 2: Introduction to Sports analytics
byVINAY SINGH
 
PPTX
The Importance of Being Open: What Player Tracking Data Can Say About NBA Fie...
bySloan Sports Conference
 
PDF
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
byEdge AI and Vision Alliance
 
PPT
Basketball signals mech-2014
byZahid Dewan
 
DOCX
Phoenix Suns SportVU
byRyan Milne
 
DOCX
Phoenix Suns SportVU
byRyan Milne
 
PPTX
Motion Analysis of a Basketball Being Shot Towards the Hoop.pptx
byjhasaras1610
 
Basketball matches outcomes prediction using data mining
byArioo Barzan
 
2012 UEFA Euro Efficiency Evaluation Based On Market Expectations
byNathan Mathis
 
Lineup Efficiency
byMatt Hanamoto
 
NBA playoff prediction Model.pptx
byrishikeshravi30
 
7 Ways Sports Teams Win With Sports Analytics
byTableau Software
 
Winning in Basketball with Data and Machine Learning
byKonstantinos Pelechrinis
 
Olympics Sports Data Analysis
byIRJET Journal
 
The Flexible “Motion Offense” as a Core Basketball Strategy
byCraig Raucher New York
 
RLOEB PPT — SportVU
byRobbie Loeb
 
2018_04_Zuccolotto_Manisera_basketball_data_science.pdf
bypedro218150
 
Performance Analysis Guide - AFC Kempston
byMichael Malone
 
Data science in baseball
byKartikKakani1
 
Ai final module (1)
byDevansh Chawla
 
Week 2: Introduction to Sports analytics
byVINAY SINGH
 
The Importance of Being Open: What Player Tracking Data Can Say About NBA Fie...
bySloan Sports Conference
 
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
byEdge AI and Vision Alliance
 
Basketball signals mech-2014
byZahid Dewan
 
Phoenix Suns SportVU
byRyan Milne
 
Phoenix Suns SportVU
byRyan Milne
 
Motion Analysis of a Basketball Being Shot Towards the Hoop.pptx
byjhasaras1610
 

More from University of Salerno

PDF
Modelling traffic flows with gravity models and mobile phone large data
byUniversity of Salerno
 
PDF
Regression models for panel data
byUniversity of Salerno
 
PDF
Carpita metulini 111220_dssr_bari_version2
byUniversity of Salerno
 
PDF
A strategy for the matching of mobile phone signals with census data
byUniversity of Salerno
 
PDF
Human activity spatio-temporal indicators using mobile phone data
byUniversity of Salerno
 
PDF
Poster venezia
byUniversity of Salerno
 
PDF
Big Data Analytics for Smart Cities
byUniversity of Salerno
 
PDF
Meeting progetto ode_sm_rm
byUniversity of Salerno
 
PDF
A Spatial Filtering Zero-Inflated approach to the estimation of the Gravity M...
byUniversity of Salerno
 
PPT
The Water Suitcase of Migrants: Assessing Virtual Water Fluxes Associated to ...
byUniversity of Salerno
 
PPT
The Global Virtual Water Network
byUniversity of Salerno
 
PDF
The Worldwide Network of Virtual Water with Kriskogram
byUniversity of Salerno
 
PDF
Ad b 1702_metu_v2
byUniversity of Salerno
 
PDF
Statistics lab 1
byUniversity of Salerno
 
PDF
Talk 2
byUniversity of Salerno
 
PDF
Talk 3
byUniversity of Salerno
 
PDF
Talk 4
byUniversity of Salerno
 
PDF
Talk 5
byUniversity of Salerno
 
PDF
Introduction to Bootstrap and elements of Markov Chains
byUniversity of Salerno
 
PDF
The linear regression model: Theory and Application
byUniversity of Salerno
 
Modelling traffic flows with gravity models and mobile phone large data
byUniversity of Salerno
 
Regression models for panel data
byUniversity of Salerno
 
Carpita metulini 111220_dssr_bari_version2
byUniversity of Salerno
 
A strategy for the matching of mobile phone signals with census data
byUniversity of Salerno
 
Human activity spatio-temporal indicators using mobile phone data
byUniversity of Salerno
 
Poster venezia
byUniversity of Salerno
 
Big Data Analytics for Smart Cities
byUniversity of Salerno
 
Meeting progetto ode_sm_rm
byUniversity of Salerno
 
A Spatial Filtering Zero-Inflated approach to the estimation of the Gravity M...
byUniversity of Salerno
 
The Water Suitcase of Migrants: Assessing Virtual Water Fluxes Associated to ...
byUniversity of Salerno
 
The Global Virtual Water Network
byUniversity of Salerno
 
The Worldwide Network of Virtual Water with Kriskogram
byUniversity of Salerno
 
Ad b 1702_metu_v2
byUniversity of Salerno
 
Statistics lab 1
byUniversity of Salerno
 
Talk 2
byUniversity of Salerno
 
Talk 3
byUniversity of Salerno
 
Talk 4
byUniversity of Salerno
 
Talk 5
byUniversity of Salerno
 
Introduction to Bootstrap and elements of Markov Chains
byUniversity of Salerno
 
The linear regression model: Theory and Application
byUniversity of Salerno
 

Players Movements and Team Performance

  • 1.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Players Movements and Team Shooting Performance: a Data Mining approach for Basketball. Rodolfo Metulini University of Brescia - Department of Economics and Management Palermo - June 20th, 2018
  • 2.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Table of contents 1 Overview 2 Data & Methods 3 Analysis & Results 4 Future developments 5 Acknowledgm. & References
  • 3.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Aims To study the interaction between players in the court, in relation to team performance Analysts want to explain movements in reaction to a variety of factors and in relation to team performance • Goal I: to segment the game into homogeneous phases in terms of players’ spatial relations, to retrieve significant moments of the game • Goal II: to characterize game phases in terms of team (shooting) performance
  • 4.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Global Positioning Systems (GPS) • Object trajectories capture the movement of players or the ball • Trajectories are captured using optical- or device-tracking and processing systems • The adoption of this technology and the availability of data is driven by various factors, particularly commercial and technical
  • 5.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Play-by-play Play-by-play (or ‘event-log”) reports a sequence of relevant events that occur during a match • Players’ events (shots, fouls) • Technical events (time-outs, start/end of the period) • Web scraping techniques (user-friendly R and Phyton routines) • Video analysis
  • 6.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Cluster Analysis in Sport Literature • Sampaio & Janeira (2003) investigate the discriminatory power of game statistics between winning and losing teams in the Portuguese Professional Basketball League • Carpita et al. (2013,2015) identify the drivers that most affect the probability to win a football match • Ross (2007) segment team sport spectators identifying potential similarities according to demographic variables • Gonccalves (2018), using GPS data, applies a two-step cluster to classify the regularity in team-mates dyads positioning.
  • 7.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Cluster analysis on time instants Metulini, Manisera & Zuccolotto (2017) split an amatorial basketball game in a number of separate time-periods, each identifying homogeneous spatial relations among players in the court Improvements in 2018 paper: • analysis extended to multiple matches, • use of professional basketball games, • use of the active moments of the games only (applying a filtering procedure on the initial dataset), • introduction of transition moments. • We characterize clusters in terms of team performance
  • 8.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Our dataset(s) • We have tracked data from three games played by Italian professional basketball teams, at the Italian Basketball Cup Final Eight. Data provided by MYagonism MYa • Each player worn a microchip, collecting the position (1 cm2 pixels), velocity and acceleration in the x-axis (court length), y-axis (court width), and z-axis (height) • The initial dataset considers the full game length, for all the ms in which the system captured at least one player. We cleaned it by dropping inactive moments, according to our filtering procedure • The final dataset for team 1 counts for 206,332 rows, team 2 counts for 232,544 rows, while team 3 counts for 201,651 rows (Frequency: 80/90 Hz)
  • 9.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Filtering Procedure From: Metulini, R., Filtering procedures for sensor data in basketball. Statistics & Applications. 2017-2 Full data matrix X (nrow = T); ↓ 1-A Remove row if players on the court = 5 ↓ 1-B Remove row if a player is on the free throw circle ↓ 1-C Remove row if players veloc- ity < h2 for h3 consecutive seconds ↓ Reduced data matrix (nrow = T’ ≤ T) ↓ 2-A Assign offense or defense labels ↓ 2-B Assign actions’ sorting ↓ Reduced data matrix with actions’ labelling and sorting
  • 10.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Approach - I • We consider 5 total lineups from 3 different games • Separately to each one, we apply a k-means cluster analysis to group objects • We group time instants • The similarity is expressed in terms of distance between players’ dyads. • We, consistently along different lineups, find k = 6 based on the value of the between deviance (BD) / total deviance (TD) ratio for different numbers of clusters
  • 11.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Profiles plot 05101520 C1 13.31% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C2 19.76% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C3 3.4% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C4 29.8% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C5 6.41% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 05101520 C6 27.31% dsred.d13 dsred.d16 dsred.d17 dsred.d18 dsred.d36 dsred.d37 dsred.d38 dsred.d67 dsred.d68 dsred.d78 Figure: Profile plots representing, for each of the 6 clusters, the average distance among players’ dyads. Lineup 1 in CS1. Profiles plot for other case studies Go to
  • 12.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Multidimensional scaling −10 −5 0 5 10 −4−2024 C1 13.31% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C2 19.76% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C3 3.4% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C4 29.8% Dimension 1 Dimension2 13 6 7 8 −10 −5 0 5 10 −4−2024 C5 6.41% Dimension 1 Dimension2 1 3 6 7 8 −10 −5 0 5 10 −4−2024 C6 27.31% Dimension 1 Dimension2 1 3 6 78 Figure: Map representing, for each of the 6 clusters, the average position of the five players in the two dimensions. Lineup 1 in CS1. Multidimensional scaling for other case studies Go to
  • 13.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Transition matrix Cluster 1 2 3 4 5 6 TR 8.41 21.76 82.11 7.08 54.49 10.53 D 22.74 10.28 6.6 70.48 23.98 17.95 O 68.85 67.97 11.29 22.45 21.53 71.52 Table: Percentages of time instants classified in Transition (TR) Defense (D) or Offense (O), for each cluster. Lineup 1 in CS1. Cluster label C1 C2 C3 C4 C5 C6 C1 - 11.27 10 8.45 15 10.34 C2 31.03 - 10 23.94 15 35.34 C3 0.00 1.41 - 0.00 0 7.76 C4 34.48 21.13 0 - 25 35.34 C5 3.45 4.23 0 4.23 - 11.21 C6 31.03 61.97 80 63.38 45 - Table: Transition matrix reporting the relative frequency subsequent moments (t, t + 1) report a switch from a group to a different one. Lineup 1 in CS1.
  • 14.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Team Shooting Performance - Video Analysis Play-by-play data are not freely available online for this tournament We retrieve shoots by doing a video analysis: • We install an app in the smartphone to take trace of time (aTimeLogger) • We open the video of the match, which is available online • We trace made/missed shoots with aTime Logger while running the video • The App create a .txt report with the shooting events and related ms, which can be attach to the final dataset
  • 15.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Team Shooting Performance Cluster label Made Missed Total % C1 2 2 4 50% C2 0 2 2 0% C3 0 0 0 - C4 0 0 0 - C5 0 1 1 0% C6 5 3 8 62.5% Lineup 1 7 8 15 46.7% Table: Team Shooting performance. Lineup 1 in CS1. Duration: 8m:21s
  • 16.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Examples An example of offensive play which ends in Cluster C6 An example of a transition-to-offensive play which ends in Cluster C6
  • 17.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Highlights • Spacing matters • The team (shot-) performs better when a player is free to shot • The team goes in search of the best pattern for a good shot (average length in a cluster: 2 seconds)
  • 18.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Future research • Examining the effect of external factors, such as coach advices (playbooks, tactics) • Including ball trajectories • ...
  • 19.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References Acknowledgements Big & Open Data Innovation (BODaI) laboratory BODaI Big Data analytics in Sports (BDS) laboratory BDS Paola Zuccolotto, Marica Manisera (University of Brescia) and Tullio Facchinetti (University of Pavia)
  • 20.
    Movements and Performance Metulini Overview Data & Methods Analysis & Results Future developments Acknowledgm. &References References 1. Sampaio, J., Janeira, M.: Statistical analyses of basketball team performance: understanding teams wins and losses according to a dierent index of ball possessions. International Journal of Performance Analysis in Sport 3.1 (2003): 40-49. 2. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P. (2013). Football mining with r. Data Mining Applications with R. 3. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P. (2015). Discovering the drivers of football match outcomes with data mining. Quality Technology & Quantitative Management 4. Ross, S. D.: Segmenting sport fans using brand associations: A cluster analysis. Sport Marketing Quarterly, 16.1 (2007): 15. 5. Gonalves, B. S. V.: Collective movement behaviour in association football. UTAD Universidade de Tras-os-Montes e Alto Douro (2018) 6. Metulini, R., Marisera, M., Zuccolotto, P.: Space-Time Analysis of Movements in Basketball using Sensor Data. Statistics and Data Science: new challenges, new generations SIS2017 proceeding. Firenze Uiversity Press. eISBN: 978-88-6453-521-0 (2017). 7. Metulini, R.: Filtering procedures for sensor data in basketball. Statistics&Applications 2 (2017).
  • 21.
    Movements and Performance Metulini More 0510152025 C1 11.32% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C2 24.45% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C314.42% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C4 6.21% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C5 40.4% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 0510152025 C6 3.2% dsred.d14 dsred.d15 dsred.d17 dsred.d110 dsred.d45 dsred.d47 dsred.d410 dsred.d57 dsred.d510 dsred.d710 (a) lineup 2, CS1 05101520 C1 2.17% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C2 34.52% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C3 8.4% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C4 10.59% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C5 9.18% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 05101520 C6 35.14% dsred.d12 dsred.d14 dsred.d15 dsred.d16 dsred.d24 dsred.d25 dsred.d26 dsred.d45 dsred.d46 dsred.d56 (b) lineup 1, CS2 051015 C1 5.46% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C2 20.5% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C3 4.3% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C4 12.65% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C5 36.11% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 051015 C6 20.98% dsred.d12 dsred.d15 dsred.d16 dsred.d18 dsred.d25 dsred.d26 dsred.d28 dsred.d56 dsred.d58 dsred.d68 (c) lineup 2, CS2 05101520 C1 26.55% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C2 4.49% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C3 3.85% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C4 20.92% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C5 37.8% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 05101520 C6 6.39% dsred.d25 dsred.d26 dsred.d29 dsred.d210 dsred.d56 dsred.d59 dsred.d510 dsred.d69 dsred.d610 dsred.d910 (d) lineup 1, CS3 Back to Lineup1, CS1
  • 22.
    Movements and Performance Metulini More −5 0 510 −40246 C1 11.32% Dimension 1 Dimension2 1 4 57 10 −5 0 5 10 −40246 C2 24.45% Dimension 1 Dimension2 1 4 5 7 10 −5 0 5 10 −40246 C3 14.42% Dimension 1 Dimension2 1 4 5 7 10 −5 0 5 10 −40246 C4 6.21% Dimension 1 Dimension2 1 4 5 7 10 −5 0 5 10 −40246 C5 40.4% Dimension 1 Dimension2 14 5 710 −5 0 5 10 −40246 C6 3.2% Dimension 1 Dimension2 1 4 5 7 10 (e) lineup 2, CS1 −10 −5 0 5 −4024 C1 2.17% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C2 34.52% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C3 8.4% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C4 10.59% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C5 9.18% Dimension 1 Dimension2 1 2 4 5 6 −10 −5 0 5 −4024 C6 35.14% Dimension 1 Dimension2 124 5 6 (f) lineup 1, CS2 −10 −5 0 5 −4024 C1 5.46% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C2 20.5% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5−4024 C3 4.3% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C4 12.65% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C5 36.11% Dimension 1 Dimension2 1 2 5 6 8 −10 −5 0 5 −4024 C6 20.98% Dimension 1 Dimension2 1 2 5 6 8 (g) lineup 2, CS2 −5 0 5 10 −4024 C1 26.55% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C2 4.49% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C3 3.85% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C4 20.92% Dimension 1 Dimension2 2 5 6 9 10 −5 0 5 10 −4024 C5 37.8% Dimension 1 Dimension2 25 69 10 −5 0 5 10 −4024 C6 6.39% Dimension 1 Dimension2 2 5 6 9 10 (h) lineup 1, CS3 Back to Lineup1, CS1