Framework for Forecasting Professional Soccer Player Career Paths

12
2015 OptaPro Analytics Forum Framework for a Player Career Forecast Model Between Multiple Leagues Howard Hamilton Founder, Soccermetrics Research

Transcript of Framework for Forecasting Professional Soccer Player Career Paths

Page 1: Framework for Forecasting Professional Soccer Player Career Paths

2015 OptaPro Analytics Forum

Framework for a Player Career Forecast Model

Between Multiple Leagues

Howard HamiltonFounder, Soccermetrics Research

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Developed a career statistical forecasting modelling framework for football players, automated by applying machine-learning techniques.

Inputs 1. Season statistical performance2. Physical / playing

characteristics

Outputs1. Identify peer group of players with comparable

performance2. Forecast future statistical performance over a limited

horizon3. Translate performance in one domestic league

competition to performance in another

Expected Interest Clubs Media Betting Fantasy

Early Stage: Framework > Results

Main Points

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Baseball1. Similarity Scores (Bill James, 1980s)

2. Vladimir Forecasting System (Gary Huckabay, 1990s)

3. PECOTA (Nate Silver/Baseball Prospectus, 2003)

PECOTA-inspired forecasting models in other sports1. SCHOENE (Kevin Pelton/Basketball Prospectus/ESPN, mid 2000s)

2. KUBIAK (Aaron Schatz/Football Outsiders, mid 2000s)

3. VUKOTA (Puck Prospectus, 2010)

Individual / team projection models in football1. Aaron Nielsen (ENB Sports)

• One-year projection of individual/team performance

2. Pérez Sánchez et al (2013)• Estimating goal-scoring performance in Spanish league

Forecasting Statistical Performance in Sport

Prior Art

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Data scarcity• Range of seasons• Statistical categories collected• League variations

Characteristics of domestic leagues• Differences in aging curves between leagues• Would a 'universal' aging curve work? Not sure...• Statistical translations between leagues• Some leagues are very connected, others less so

Challenges

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Data Source: ENB Soccer Database• 60,000+ players, • 75 domestic league competitions, • 500+ clubs

Individual season statistics• 1992-93 to 2011-12 (European)• 1992 to 2012 (American/Scandinavian/Japanese)

Database Analysis

All players• Season• Team• Competition• Appearances• Subs• Minutes• Yellows / reds

Field players• Goals• Assists• Shots• Fouls

Goalkeepers• Goals allowed• Clean sheets• Shots faced• Wins• Draws• Losses

Modeling Components

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Normalize statistical categoriesConvert statistical values of players in same competition and season • to “standard score”• Places statistical performances on one standard distribution• This is what allows us to compare players

Identify K comparable players (“nearest neighbors”)• Consider players of same age and position• Calculate similarity score between statistical records• Comparable players: Score about 0.90 - 0.95• Relax threshold for “unique” players

Forecast future performance with historical performance of comparable playersUsing regression techniques • Adjust for aging and regression to mean• Convert to statistics for league competition of interest

(x-)/

K-NN

Model Description

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Player League Season Similarity

Osvaldo Val Baiano Brazil Serie B 2007 0.961

Wayne Rooney English Premier League 2011-2012 0.957

Oscar Cardozo Portugal Primeira Liga 2009-2010 0.954

Maciej Zurawski Poland Ekstraklasa 2002-2003 0.939

Carlos Tevez English Premier League 2010-2011 0.926

Javi Moreno Spanish Primera 2000-2001 0.925

Katlego Mphela South Africa PSL 2010-2011 0.913

Matt Tubbs England Conference 2010-2011 0.913

Kris Boyd Scotland Premier League 2009-2010 0.905

Goncalves Jonas Brazil Serie A 2010 0.904

Rickie Lambert England League One 2008-2009 0.901

Mario Bermejo Spanish Segunda 2004-2005 0.897

Alan Shearer English Premier League 1996-1997 0.877

Kevin Phillips English Premier League 1999-2000 0.863

Photo by Simon Harriyott

Cristiano Ronaldo: Forward, aged 27 (Spanish Primera 2011/12)

Active Player.

Scored 46 goals in 2011/12La Liga season.

Nearest Neighbor Results

Nearest Neighbor groups leading goalscorers at Ronaldo's age

0.96 similarity metric – few players had a season as dominant

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Marvin Bejarano: Defender, aged 21 (Bolivia Liga Profesional 2008)

Player League Season Similarity

Fernando Tobio Argentina Primera 2009-2010 0.996

Charlie Wassmer England League Two 2011-2012 0.990

Oswaldo Alanis Mexico Primera 2009-2010 0.985

Jan Vertonghen Netherlands Eredivisie 2007-2008 0.984

Paul Papp Romania Liga I 2009-2010 0.957

Santiago Vergini Paraguay Primera 2009 0.957

Mauricio Casierra Colombia Primera 2006 0.957

Rafael Delgado Argentina Nacional B 2010-2011 0.955

Konstantin Engel Germany 2 Bundesliga 2008-2009 0.954

Jae Sung Lee South Korea K-League 2009 0.953

Koybasi Ismail Turkey Super Lig 2009-2010 0.953

Luke O'Brien England League Two 2008-2009 0.951

Hector Quinones Colombia Primera 2012 0.950

Mate Ghvinianidze Germany 2 Bundesliga 2006-2007 0.950

Franz Schiemer Austria 1 Bundesliga 2006-2007 0.947

Active Player.

Has played for one club over his career.

5 caps for Bolivia.

0.996 similarity metric – very comparable, but limited defensive data

Nearest Neighbor Results

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Iker Casillas: Goalkeeper, aged 26 (Spanish Primera, 2006-2007)

Active Player.

Has played for one club over his career.

450+ appearances at Real Madrid,160 caps for Spain.

Interesting that Gianluigi Buffon is closest comparable at 26 y/o

Nearest Neighbor Results

Player League Season Similarity

Gianluigi Buffon Italy Serie A 2003-2004 0.994

Mark Crossley English Premier League 1994-1995 0.992

Dionissis Chiotis Greece Super League 2002-2003 0.990

Steve Mandanda France Ligue 1 2010-2011 0.989

Marco Wolfli Switzerland Super League 2007-2008 0.989

Shay Given English Premier League 2001-2002 0.986

Guillermo Ochoa Mexico Primera 2010-2011 0.986

Eduardo Martini Brazil Serie A 2004 0.985

Morgan de Sanctis Italy Serie A 2002-2003 0.984

Hiroki Iikura Japan J1-League 2011 0.982

Cesar Lainez Spanish Segunda 2002-2003 0.981

Marcelo Grohe Brazil Serie A 2012 0.981

Hitoshi Sogahata Japan J1-League 2005 0.980

Henri Sillanpaa Finland Veikkausliiga 2004 0.980

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Projecting career performance is difficult• Next steps:

● Use nearest neighbors to forecast future performance● Quantify adjustments for age, league quality, position● Create multiple career forecast paths with probabilities

• Limited horizons important (2-3 years)• Probabilistic projections sensible, not necessarily useful

• Accuracy vs. clarity• Diverse range of statistical categories necessary –

• Attacking and defending contributions and impact• Advanced metrics

Data normalization is a necessity!

Club projections are logical stepNeed to enforce a “conservation of goals” in the universe of data in our

system, i.e:

Total goals scored == total goals conceded

Photo by Simon Harriyott

Conclusions

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Customization• Integrate with financial/medical databases, scouting data• Greatest utility at football operations/sporting director level

Biggest challenge: Data!Not just data on all players in league, but players • in all other leagues of interest• Some statistical categories not available in some leagues• As always, data collection and analysis problems are non-trivial

Photo by JD Hancock

Knowledge Transfer

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Thank You!Special Thanks To:

OptaPro (Invitation to Forum)Aaron Nielsen (ENB Database access)

Simon Harriyott (Presentation at Forum)

For more information contactSoccermetrics Research

[email protected]

@soccermetrics