If you wish to access the video or the poster (summarized) click on the links below:
OptaPro Link: Youtube
Poster Link: Drop Box
Key Takeaways
This project provides a framework for using a rules-based approach to then build machine learning models. This strategy could be easily incorporated in pre-match and post-match analysis. More importantly, due to the effectiveness of the approach, performance analysts can view similar scenarios during live games and inform coaches sitting in the dugout.
Similar effective approaches are used by data teams at FC Barcelona and Toronto FC amongst other elite clubs. This project embraced coach recommendations through an iterative process that helps achieve the quintessential “coach-buy in” and thus challenges coaching practise. This is crucial because it bridges the gap between the two worlds of football and analytics.
The Challenge
I presented a novel approach to identifying low-block scenarios and effective strategies used against a low-block defence at the Opta 2020 Forum. So, what is a low-block? A common term like “Parking-the-bus” has floated around but to define it more aptly:
“ The low block is a defensive tactic in which a team defends from a very deep position on the pitch, meaning they then restrict the amount of space the opposition has to exploit. As a result, opposing teams tend to find it much more difficult to score due to the lack of room to operate in central areas of the pitch” — Football Scotland
This study was based on a club-led research question posed by FC Nordsjaelland, a Danish Super Liga Club. The club is directly linked to the ‘Right to Dream Academy’ (RDA) in Ghana, instrumental in recruiting young football talent. The strategic objective of the club is to spend less money on recruitment at a senior level whilst giving opportunities to young talent. Over the past 12 years, The Right to Dream Academy has produced talented individuals who have taken up scholarships worth $45 million through the US collegiate system. The average age seen of the team is about 22 years and the opposition team in the Danish Superliga almost always play in a low-block against FC Nordjaelland. Opposition teams play on artificial pitches in the winter months and tend to defend deeper due to the FC Nordjaelland style of passing play is high as compared to the league average.
Defining the problem: Real-world scenario
The head coach of FC Nordjallend, Flemming Pedersen, idolizes football with a pro-active possession-based approach. During the winter months, teams who play against FC Nordjallend most often defend deeper towards their own goal. The head coach seeks to identify and evaluate strategies that are influential to the game model when FC Nordjallend face oppositions well positioned in a low-block scenario.
“We have a gut feel for how effective our strategies are at breaking a low-block, but we have no hard evidence to challenge our assumptions. Using data to evaluate how we implement our game model is its most interesting use case” — Flemming Pedersen, Head Coach
Optical Tracking & Event Data
Event data primarily captures on-the-ball actions across the pitch and is supplied by Opta-Stats Perform. It predominantly covers all the actions in a game with respect to the ball location. The optical tracking data is positional data of players and the ball captured during a game at 25 frames per second. Optical tracking data is crucial to identify the movement of players on the football pitch which is clearly illustrated by the famous Johan Cruyff:
”When you play a match, it is statistically proven that players actually have the ball 3 minutes on average … So, the most important thing is: what do you do during those 87 minutes when you do not have the ball. That is what determines whether you’re a good player or not.” — Johan Cruyff
This project used a combination of optical tracking and event data.
Temporally geometrical centroids
Temporally geometrical centroids were identified as an intuitive approach to identify teams’ positions when faced with an attack by the opposition. The premise here suggests that if a team is centrally located deep towards their own goal, their combined centroid locations would give a better 1-dimensional representation. Ideally, using a point dimension to represent the location of a team is intuitive and can be used as a preliminary assumption to add on further rules to identify a low-block region.
The Lines represent a Low-Block Zone across a pitch, Green dot represents the centroids of the defensive team, Purple dot (a bit irrelevant in this context) represents the centroids of the attacking team.
Identifying when the team has broken a low-block
The next step was to identify when these low-block situations were broken by the opposition.
Capturing Offensive Strategies against a Low-Block
Once the low block was identified against the opposition, the project looked at identifying strategies used by FC Nordjallend against these situations. The strategies were broadly defined by coaches which were translated through data.
The Strategies used were:
Creating overloads in wide areas of the pitch: Encourage players to draw in defenders and analyse the right moment to break the opposition with runs into the half-space
2. Wide Pocket to Half-Space Run: Possession in wide areas, identifying gaps in defence within the half-space and making assertive runs behind the defence into the half-space
3. Fast Switches to Wide Zones: Importance of moving the ball quicker into wide areas when central areas are overloaded
4. Decisive off-ball runs at the defensive line: Assertive runs to be attempted when a ball is played behind the defensive line
5. Receiving in central pockets: Players are to receive in central pockets with the ability to play forwards. Numerous run patterns towards the defensive line by other players are made simultaneously
Putting it together to analyse results:
These summarized results were based on Low-block time sequences in games over 2018/2019 Danish Superliga Season. Results may vary when facing fewer/higher low blocks depending on the level of opposition.
Results
1) Amongst the strategies, off-the-ball runs was the highest adopted strategy followed by receiving in central pockets
2) Adopting half-space runs in comparison to wide overloads averaged three times a higher probability of a shot given a break through
3) Wide Pocket to Half-space runs averaged a higher xG per strategy attempt amongst all strategies.
4) Receiving in central pockets led to the lowest xG per strategy attempt given it's was the second most adopted strategy
Empirical Findings
Product Link to Performance Analysis
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