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.
Data can be used at all stages of the recruitment process, from the initial shortlisting, down to more granular player assessments, to support qualitative live and video scouting and background personality checks. Yesterday, we showed how data can be used in the early stages of the recruitment process to create a shortlist of forwards that might be worth further scouting. Today we’re going to do the same with centre backs, starting with Burnley’s James Tarkowski.
It’s well known that Burnley have a particular – and effective – approach to defending when out of possession. If their opposition has possession deep in their own half, particularly from goal kicks, Burnley will play a high line and look to force their opponents to play the ball long, where they know their centre backs will more often than not win their duels near the halfway line. This is shown in their Defensive Distance: the average distance from their own goal that a team makes a defensive action.
However, should the opposition break the initial press and start to progress into the middle third and beyond, Burnley look to drop in and decrease the space between the lines, keeping a compact shape and defending any balls that come into the box. Their prioritising of their shape over engaging the opposition is reflected in their Aggression % – the percentage of opposition pass receipts that are pressured, tackled, or fouled within two seconds – of 16%, the lowest in the league.
For Tarkowski, this means that his main responsibilities as a Burnley centre back can be condensed into: being strong in aerial duels, being strong in ground duels, and defending his penalty box well, with little-to-no expectation on him to be an effective player in possession. Given Tarkowski’s importance to Burnley, and the fact he’s been linked with moves away in previous windows, it’d be sensible for Burnley to be planning and searching for potential successors already. Let’s use StatsBomb IQ to help us do this.
1) Create And Edit A Radar Template
The first thing to do is adjust the standard Centre Back radar template to include metrics that best reflect Tarkowski’s player profile and give us the best chance of finding a player that could replace him.
As mentioned, we’re not expecting Tarkowski or his replacement to be highly effective in possession, so we’ll remove Passing %, Pressured Long Balls, Unpressured Long Balls and xGBuildup from the standard radar template. In their place, we’ll add:
- Average Defensive Action Distance: The average distance from the goal line that the player successfully makes a defensive action
- Clearances: Number of clearances made by a player
- Blocks/Shot: Blocks made per shot faced
We can see that Tarkowski is an excellent performer in aerial duels (in the 99th percentile for Aerial Wins and 91st percentile for Aerial Win %), ground duels (72nd percentile for Tackle / Dribbled Past %*), and defending his box (88th percentile for Blocks / Shot and 83rd percentile for Clearances). *Tackle / Dribbled Past %: Percentage of time a player makes a tackle when going into a duel vs getting dribbled past.
When we’re happy that the radar reflects the profile of player we’ll be searching for, we can save the template for repeated future use. But how can we use this information to find a potential replacement?
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 Burnley tend to focus on a specific and limited number of markets when recruiting new players, mostly domestically. For the purposes of this exercise, we’re going to look at players from:
- Season: 2020/21
- Minutes Played: >=1200
- Competition: Big 5 European + English Championship
- Age: U-29
The top five most similar players make for interesting reading. Stoke’s Harry Souttar has been praised for his performances in his first full season at Championship level and, at 22 years old, represents a centre back that could be of longer-term interest to a team that defends like Burnley. If Burnley were happy to look abroad, then not far below the top five players returned is Felix Uduokhai of Augsburg in the Bundesliga. Uduokhai not only has a similar profile to Tarkowski, but Augsburg also show up as a team that defends in a somewhat similar style to Burnley when comparing Burnley’s defensive style to Big 5 + Championship teams in StatsBomb’s Similar Team Search. At 23 years old, he could be another worth further investigation.
The list returned is 99 players long which can be exported for more detailed filtering, scouting and analysis.
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:
- >=70% Aerial Win %
- >= 3.0 Aerial Wins per 90 minutes
- <= 28.2 Average Defensive Distance (to find players used to defending in deeper areas)
- >= 0.05 Blocks/Shot
- >= 70% Tack/Dribbled Past %
… returns a shortlist of 12 players 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 centre back to demonstrate this.
Magdalena Ericsson has just come off the back of a title-winning and Champions League silver medal winning season with Chelsea. Ericsson’s very capable on the ball, playing a crucial role in the early stages of build-up for Chelsea as well as possessing ability to progress the play herself through incisive passing or ball carrying.
Assuming on-ball ability 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 Fouls, Pressures, Pressured Long Balls, Unpressured Long Balls, pAdj Tackles and pAdj Interceptions. In their place we’ll add:
- pAdj Tackles & Interceptions: Number of tackles and interceptions adjusted proportionally to the possession volume of a team
- Open Play Passes: Number of attempted passes in open play
- Being Pressured Change in Pass%: How does passing % change when under pressure? This is calculated as Pressured Pass % minus Pass %
- Deep Progressions: Passes and dribbles/carries into the opposition final third
- Carries: A player controls the ball at their feet while moving or standing still
- Carry %: Percentage of a player’s Carries that were successful
- Carry Length: Average Carry length.
The new radar clearly highlights Ericsson’s profile in possession: one that is heavily involved in the build-up (72 Passes In Open Play per 90) and moving the play forwards (7.9 Deep Progressions per 90). Using this template in the Similar Play Search with the following filters:
- Age: U-26
- Minutes Played: min. 900
- Season: 2020/21
- Competition: Big 5 European Leagues
… 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:
- >= 75% Tackled / Dribbled Past %
- >= 60% Aerial Win %
- >= 45 Open Play Passes per 90 minutes
- >= 2.8 Deep Progressions per 90 minutes
… returns a list of 10 players, ones we can be confident will be a reasonably close fit to the ball-playing centre back profile we’re looking for and worthy of further analysis and scouting, with the opportunity to add, remove or adjust additional filters to create a wider or more specific shortlist of players. 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 alongside live and video qualitative scouting and background personality and availability checks. Next week we’ll look at another position on the pitch to further demonstrate how StatsBomb IQ can be customised to fit the precise profile of player you’re looking for.
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.