Right now, dozens of clubs around the world will be using StatsBomb IQ to aid their player recruitment planning and shortlisting ahead of the summer transfer window. IQ is designed by analysts, for analysts, with the goal of making data-driven insights easily accessible and digestible. Most importantly, it saves valuable time and resources for the time-poor analyst and is flexible and customisable to each user’s specific needs.
StatsBomb Director of Football James Yorke provided the commentary for this walkthrough video of how IQ can be used for data scouting and shortlist creation. If you’d like to consume the walkthrough in written form and see further example profiles of players, read on.
Data can be used at all stages of the recruitment process, from the initial shortlisting all the way down to the final granular player assessments. Last week, we looked at how data can be used to create shortlists of forwards and centre backs that might be good stylistic fits for clubs looking for a particular profile of player. In this article, it’s the turn of one of the more role-diverse positions on the pitch: full backs. Let’s focus on a player who’s become synonymous with the position in recent seasons, Trent Alexander-Arnold.
Alexander-Arnold’s performances at full back for Liverpool have seen him become a key player in their recent domestic and European successes. We’re all familiar with the ultra-attacking approach he takes to the position – asked to supply all the width on Liverpool’s right flank and be a creative outlet both in build-up and in chance creation. Combined with Andy Robertson, Liverpool were one of the most frequent and dangerous crossing teams in the Premier League last season.
Having a player that can perform an attacking role to such a high standard is something clubs around the world will prioritise in today’s modern game. So let’s show you how StatsBomb IQ can be used to support this process in identifying and shortlisting players of this profile.
1) Create And Edit A Radar Template
The first thing to do when using IQ to identify players is to select the metrics that best reflect the role you’re recruiting for. In this case, we’re going to adjust the current full back radar to add key metrics that are closely associated with Alexander-Arnold’s style of play.
We’re looking to highlight the most synonymous parts of Alexander-Arnold’s game – chance creation, competence in possession, width in attack, and ability to defend in a high line, so it makes sense to add:
- Average Defensive Action Distance: The average distance from the goal line that the player successfully makes a defensive action
- Carry Length: Average Carry Length
- Being Pressured Change in Pass%: How does passing % change when under pressure? This is calculated as Pressured Pass % minus Pass %
- Successful Crosses: Completed Crosses
- Open Play xG Assisted: xG Assisted from open play
- Open Play Key Passes: Passes that create shots for teammates, from open play only
We can see a radar that more closely resembles what we’d associate with Alexander-Arnold’s style of play. He’s performed to a very high standard in the chance creation metrics we selected (96th percentile for Open Play xG Assisted, for example), with stylistic indicators such as Carry Length and Average Defensive Action Distance providing further illustration of his player profile. When we’re happy that the radar we’ve created reflects and demonstrates the profile of player we’re looking for, we can save the template for future and repeated use.
But how can we use this information to find potential competition or players of a similar profile?
2) Use StatsBomb’s Similar Player Search Tool
The first thing to do in Similar Player Search is to set the filters for potential replacements. StatsBomb cover 80+ competitions worldwide, but Liverpool tend to focus on recruiting from the very top of the market. For the purposes of this exercise, we’re going to set the following filters to search within:
- Season: 2020/21
- Minutes Played: >=1200
- Competition: Big 5 European
- Age: U-25
The returned list throws up some interesting names, some more realistic than others. It’s no surprise to see Inter Milan’s Achraf Hakimi flagged as a very similar profile of full back to Trent Alexander-Arnold – Hakimi put up 0.48 goals + assists per 90 from right wingback for I Nerazzurri in their Serie A title-winning season and in this campaign has boosted his reputation further as one of the world’s best right-sided defenders. Priot to any qualitative scouting, the presence of Joakim Mæhle is a curious one as Atalanta profile as one of the most similar teams to Liverpool in StatsBomb’s Similar Team Search tool, suggesting Mæhle might find the transition to Liverpool’s playing style easier than most if Liverpool were hypothetically looking to add competition for the right back position and if Mæhle were hypothetically deemed to have the required quality to fulfil that role.
The list returned is 73 players long which can be exported for further filtering, analysis and scouting.
3) Use IQ Scout
The second thing we can do to find players and create scouting shortlists is to use IQ Scout. IQ Scout is the recently upgraded scouting and recruitment tool within the StatsBomb IQ platform. We can use IQ Scout to find more players that may not have been flagged in our Similar Player Search, using filters to bring the list of players down to a manageable and relevant number. The first thing to do in IQ Scout is to select the radar template we’ve just created so we can filter our shortlist based on those metrics. Setting a benchmark of:
- >= 3.75 Deep Progressions per 90 minutes
- >= 0.50 Open Play Key Passes per 90 minutes
- >= 0.50 Successful Crosses per 90 minutes
- >= 62% Tackled / Dribbled Past %
… returns a shortlist of 11 players (after excluding Alexander-Arnold) that we can be confident are worthy of further investigation and filtering.
Loosening or altering the filters brings up a different set of names, as does adding more leagues to the search, allowing you to widen or reduce the pool of players before you export the shortlist which includes their performance data across every metric in the StatsBomb IQ Scout database.
Of course, IQ is flexible to each user’s demands and scouting criteria, so let’s take a quick look at an alternative profile of full back to demonstrate this.
Benjamin Pavard has just come off the back of another title-winning season at Bayern Munich and heads into the Euros with France looking to double up on their 2018 World Cup win. Pavard has provided a stable solidity to Bayern’s back four, counterbalancing the rampaging and aggressive Alphonso Davies on the opposite flank. Pavard’s key duties in the Bayern backline have been to protect the Bavarian’s from becoming exposed on the counter, allowing their more attacking talents to flourish, and provide a safe outlet in possession to recycle the ball to more adventurous teammates.
Assuming safety – both defensively and in-possession – is the most important attribute we want to search for in this exercise, we can create a new player template that is designed to highlight and emphasise this. We’ll remove Pressures, Deep Progressions, xGBuildup, and Successful Dribbles from the original full back template. In their place we’ll add:
- pAdj Pressures: Possession adjusted pressures
- Pressured Pass %: Proportion of pressured passes that were completed
- Dribbled Past: How often a player fails a challenge and is dribbled past
- Blocks/Shot: Blocks made per shot faced
The new radar clearly highlights Pavard’s defensively excellent performances for Bayern, regularly winning the ball back and protecting the Bayern goal and rarely giving the ball away. This provides a template and benchmark we can use to find players that perform defensive or “safe” actions to a similarly high level. Using this template in the Similar Play Search with the following filters:
- Age: U-24
- Minutes Played: min. 1200
- Season: 2020/21
- Competition: Big 5 European Leagues + Austrian Bundesliga, Belgian Pro League A, German Bundesliga 2., Netherlands Eredivisie, Portuguese Liga NOS, and Swiss Super League.
… returns a list of 100 players, with the five most similar players seen below:
Heading into IQ Scout and applying the same preliminary filters as before with the addition of:
- >= 50% Aerial Win %
- <= 1.20 Dribbled Past per 90 minutes
- >= 1.0 pAdj Interceptions per 90 minutes
- >= 1.5 pAdj Tackles per 90 minutes
- >= 65% Pressured Pass %
- >= 67% Tackle / Dribbled Past %
… returns a list of 12 players, ones we can be confident will be a reasonably close fit to the safety-first full back profile we’re looking for and worthy of further analysis and scouting. We can also create a wider or more specific shortlist of players by adjusting or changing the filters.
That’s just a glimpse of how StatsBomb IQ can be used for player recruitment and shortlist creation, prior to the deeper analysis we can perform within IQ once we’ve identified our targets. If you’re a football club or organisation and would like a full demo of how StatsBomb IQ and Data can help you achieve your objectives, get in touch with us today.