Moneyball 2.0 Soccer edition – StarTalk Radio

Notes taken from StarTalk Radio’s podcast about how data and analytics is affecting the soccer industry worldwide.

Dan Altman – North Yard Analytics

Leading in soccer analytics field: Dan Altman – north yard analytics.

There are 2 main kinds of data used:

  1. match data from what happens on the ball (tackle, pass, shot)
  2. tracking data

This data is normally provided by different companies and needs to be bought.

Tracking data is significantly larger than match data.

This is only really possible in modern era with computaional capabilities to analyze it.

Match data is mainly used for scouting.

Tracking data is used internally for improvement among your own team.

There is significant diversity in what coaches want to hear. For example a plot of goal scored from corners. Or expected goal networks for all of our opponents.

Goals scored from corners can help coaches decide whether to do man marking or zonal marking on corners. Once this decision is made this data can be used to make recruitment decisions.

This data can be used to predict player performance in different leagues. It was used to decide if DC United should sign Wayne Rooney.


Has Rooney still got enough in the tank or is he past his best with little to offer? Analysis revealed that while Rooney was becoming less effective in the premier league he should still have a significant impact in the MLS.

Dan has a model for winning.

Every action gets a value in that model. (mechanistic model)

Adding up all a players actions to understand his expected contribution. to winning.

MLS is at roughly the same standard of football as league 1 or league 2

Rate of improvement of Premier League is accelerating rapidly.

There is such thing as an Agnostic model. This is looking at an overall contribution without saying how they did.

Intangible contributions.

Overall contribution - mechanistic model = intangible contributions.

Using data is complimentary to the guy on the sideline.

Data and scouts both make mistakes, normally not wrong in the same way. They work best when working together.

Juventus – makes recruitment decisions as a team. Signings have to be a benefit to Juventus. A benefit while they are here and a benefit when they leave.

The process a team uses to make decisions is more effective than simply doing data analysis

When a player (George aweh) goes on loan can give a owers manual for the player including:

  1. which situation he is most successful in.
  2. where he created the most chances
  3. where had most quality touches

Agents can and do use this information in contract negotiation.

Coaches can act as interpreters or translators to get the insights of this data across to players.

One of the best ways to monetize analytics within football is to buy a football club and get it promoted to a higher league.

Howard Hamilton from Soccermetrics

A data analytics firm that performs quatitative analysis on data that the football insustry generates.

  1. Team performance
  2. Player performance
  3. Valuations
  4. Operations eg ticket sales

Sensitivity of ticket sales to the current record of a team. some interest from Stubhub

Benchmarking analysis.
payroll it takes to win a match.

Is soccer moneyball 2.0?

Brentford have employed a moneyball approach.

There is still a lot of cultural resistance to analytics in football.

Analysis of effective playing time with certain officials.

The officials don’t really effect the playing time, its the team who effects this.

Officials effect time between fouls. more of an effect on flow.

Passing networks – eigenvector centrality.

A passing network can be represented as a matrix. This can then be translated into a network graph. Each player is a node and the passes become edges. Centrality can then tell us the relative importance of each player within each passing network.

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