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Turn your data into decisions.

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HOW IT WORKS

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Build your roster.

Set up a roster from scratch or import your team over from a 3rd party service. Invite your players into the software for them to track their progress.

Create a sandbox.

Prepare and customize your datasets, and then record measurements straight from your phone using outcomes as simple as Success or Failure.

Analyze the results.

Your data entry feeds into the system real-time, calculating team rankings and building characteristics for your players.

 

Case Studies

Our methods lend themselves easily to industry standards for game data.  Using publicly available "event" data from WyScout*, we have analyzed scores of players over hundreds of competitions in their respective abilities to defend, create, and score.  A few of our favorite sample analyses appear here.

Key Performance Indicators

*Pappalardo et al., (2019) A public data set of spatio-temporal match events in soccer competitions, Nature Scientific Data 6:236, https://www.nature.com/articles/s41597-019-0247-7

What are the individual player performance measures that predict the strength of a team?  Ten coaches will likely give ten different answers to this question.  At DSA, we seek to objectively provide answers by tying our measures of player performance to our measures of team strength.  Application of our analysis to EPL game data from the 2015-2018 time period produced a well-defined (predictive!) relationship between these two measures.  Such analyses are critical to understanding which traits to focus on in player selections and which aspects of the game require attention in training.

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2018 World Cup

The 2018 World Cup featured a number of front line stars.  Kylian Mbappe had a fantastic competition for eventual winner, France.  The top of our “forward” rankings contain a number of other greats, with Diego Costa, Romelu Lukaku and Edinson Cavani rounding out the top 5.  Our performance rankings also placed Lionel Messi in the bottom half of forwards during this tournament, reflecting his well-known struggles in International Competitions during that time.

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LaLiga Defender Analysis

Bottom 20

Top 20

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Our analysis of La Liga data (2015-2018) contained a number of interesting results.  Here, we focus on defenders and demonstrate how different analyses can yield different results.  First, we ranked all defenders in the league on their ability to win challenges and successfully anticipate passes (left).  Sergio Ramos leads all 558 defenders for whom data were recorded.  Notable players in the bottom 20 include Real Madrid left back Odriozola.

However, we also considered the ability of defenders to generate opportunities, goals, and assists (right).  Here Odriozola shines, finishing first and just ahead of Barcelona playmaker Jordi Alba.  In fact, in looking at the complete list of rankings one can see the tradeoffs coaches and managers must make every game in balancing conceding vs. generating opportunities!

Filipe Luis is the only defender to appear in the top 25 for both his ability to win challenges and generate offense.  During the time covered by this data set, Filipe was a fixture in the back with Atletico Madrid.  Not surprisingly, he played a crucial role for the squad in both league play and Champions League competition, earning ”best defender” in the Spanish League in 2013.

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Premier League Player Valuation
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We also have analyzed player performance relative to compensation.  Looking at EPL midfielders during the 2015-2018 time period, we created a composite ranking of defensive performance, playmaking ability, and shooting & goal scoring.  Examining player composite rankings against their average salary during this time period yielded some interesting results.  In terms of all-around performance, both Deli Alli and Alex Iwobi would seem to be underpaid.  Sure enough, both saw their annual salary increase significantly in the ensuing years.  The big names in EPL do indeed bubble to the top in terms of ability and compensation.  Some, however, like James Milner are commanding larger salaries than we believe their ability dictates.  By giving managers a quantitative means of assessing player value , DSA can help find those hidden gems as well as avoid the overvalued signings. 

An analysis of Premier League forwards produced some other interesting results.  At the end of his career, Wayne Rooney was one of the lower performing forwards, particularly with respect to his hefty salary.  A number of prominent strikers, including Lukaku, Salah, and Kane were clearly undervalued at the time; all subsequently signed much larger deals bringing their salaries in-line with their performance.  It is also interesting to note that the performance of forwards in the EPL is more strongly correlated with salary than for the midfielders above.  This points to the difficulty in quantitatively evaluating players in the "beautiful game", particularly at midfield!  The modeling and prediction tools we have developed at DSA permit straightforward solutions to this challenge and produce results that are easy to interpret.  

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Partnerships & Use Cases

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