In sports a team’s goal is to be successful. As with many sports in soccer success is winning as many games as possible to make it to post season and eventually win the championship.
How does an organization build a championship winning team? There are a lot of factors that can make an impact and data can help influence recruiting decisions.
Putting ourselves in the shoes of a recruiter, we’re looking to put together a new star team in the EPL(English Premier League). One of the best ways to learn is from looking at history. We are lucky enough to have data on the players currently in the league.
The objective is to find what would make an ideal recruit for our team (data only). We want to find the optimal player profile that will help us have a successful season.
Location is important and this dashboard can tell us a lot about where to look for players:
https://www.tableau.com/solutions/workbook/create-optimal-game-strategies-based-past-results
according to the dashboard:
- Most players have spent time in other countries, but most have spent >50k days playing in Europe, conclusion is that there is experience in European leagues or the EPL
- EPL recruitment is concentrated in EU, but also pulls from other countries WW
- When looking at club breakdown, most patterns look similar with a cluster in EU and variance in the outliers (players from the US, Spain, South America). It is hard to correlate to current standings.
- Birth country shows that the EU isn’t the only location dominating the top – players are born in Senegal, Brazil, Argentina, and Nigeria (even though no players were directly recruited from Africa)
- The author digs further into Africa, revealing that a significant number of players born in Africa play in the EPL, regardless of their recruiting or development country, highlighting that people from Africa often come to Europe from France are developed and are recruited from Europe
Based on common trends it is pretty conclusive that focus would be on recruiting players that have done development work EU, but with South American, African, or European backgrounds.
This dashboard covers location, but it is not enough to tell us what makes the perfect player.
It does not include individual player performance or what combination of these two builds success (if someone figured that out, recruiters wouldn’t be needed). The NCAA makes some recommendations on what body fat and other characteristics a higher level soccer player should have (among other sports):
Pros
- Many different types of visuals are used
- The “story” aspect of Tableau is used to direct attention to the point the author is trying to make
- A lot of good data on what teams are already doing
- Compares different recruiting patterns, but doesn’t show how that impacts
- The visuals are all straightforward. It is not confusing to understand any page of the dashboard.
Cons
- Doesn’t provide the ability to necessarily explore options outside of what people are already doing, an observation of what current teams are already doing (IE what happens if I expand to recruiting in Antarctica)
- Can’t focus in on a specific team, don’t have enough info to use the data to see how expanding to new markets for recruiting has impacted performance (IE Everton recruiting in South America vs AFC Bournemouth staying closer to EU)
- Don’t put rankings, team performance
How would I change it?
- Presuming the goal is to provide insight that one can take action on, more data points need to be added in addition to location. Location is only an observation, adding player characteristics, team characteristics (resources), and performance can add more background and context to explain patterns and correlation about the players/teams and performance over time that could be made into recommendations.
How else could I use this data?
- If I am looking to someday become a player in the EPL, based on geography I could make strategic decisions on where to play (or to do development) in order to increase my chances on getting into the league (purely based on geographic indicators from this data)
- If significant correlations are seen between players from (born) in certain countries and performance in the EPL, development investment could be made in those countries (IE if players born in Africa are high performing, how do we optimize)