Professional teams have been using data to aid the opposition analysis process for a number of years now. As data becomes cheaper, more accurate, and knowledge of how it can impact pre-game preparations increases, we’re seeing amateur, semi-professional and college teams begin to use it too.
There’s many advantages to using data in the scouting process. These include:
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- Many games can be incorporated into a report in relatively little time compared to watching them.
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- Every event is recorded, which would be impossible for the human brain to remember.
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- There is no bias in the numbers
On the other hand data does have its shortcomings, and if used incorrectly can be detrimental to a team’s preparations. Almost all match data lacks and subjectivity and context around some events.
For example, lots of data providers don’t include any information about the number of defenders inside the box at the time of an incomplete cross. And without watching video or having tracking data and a world-class data model, nobody can say confidently whether the decision to cross was a good one.
Standard event data (passes, shots etc.) only shows what happens to the ball, and doesn’t not record events that don’t happen. For example, it won’t tell you if a fullback doesn’t track his runner on a cross to the back post, or if a player loses his man on a corner kick.
When we use data it’s important to constructively ask critical questions and attempt to provide context to statistics. For example, if a team crosses more than most others we may want to include this in a report to our Head Coach. But if in fact one player on this team leads the league in crossing, and the rest cross at an average rate, this could make a huge difference to our preparations, especially if this player is injured or suspended.
In this instance a good base of soccer-specific statistical knowledge is useful, but adding some realism and critical thinking about the realities of being a Coach will really boost the impact of the data.
The upcoming matches feature two markedly different styles:
NYCFC: Known for their cautious approach, NYCFC favors high-volume, low-risk passing to generate scoring opportunities. They rely on maintaining possession and emphasize passing accuracy over long passes or crosses. While they excel in retaining the ball and producing quality shots, they might find it challenging if forced to adopt a more direct style of play.
Houston: In contrast, Houston tends to focus on aggressive forward passes, especially in the final third, and frequently uses crosses to create scoring chances. They are particularly effective in aerial duels, aiming to generate numerous opportunities through headers. However, they don’t prioritize high possession or pass completion rates as much as some other teams. Defensively, it may be necessary to bolster our numbers in goal-scoring zones to counter their frequent shots from close range.
Utilizing data alongside other analytical tools provides valuable insights. While data might not capture all in-game subtleties, it offers a solid foundation for understanding a team’s attacking philosophy.
The data analysis task highlight how the different team build and how important and subjective the data can be but also how if married with clips can be a great aid as looking at Sporting Kansas City, the data highlight they are a team who will look to dictated the tempo of the game by having the some of heighest numbers interms of in possession team stats with them being in the top 5 in both total passes completed and passing % although when cross colrating they are in the bottom 5 in terms of forward passes. By looking at the data it highlights that in fact as a team they play more square and backwards passes rather than playing dangerous forward pass. I think data can highlight area’s of team style of play and can back up the video analysis. However i think data can be very subjective and highlight trends in teams but they change their style of play from game to game. For example, maybe if Kansas City’s changed their team each week or a individual became injured would this affect the data and how they play .
The data analysis task highlight how the different team build and how important and subjective the data can be but also how if married with clips can be a great aid as looking at Sporting Kansas City, the data highlight they are a team who will look to dictated the tempo of the game by having the some of heighest numbers interms of in possession team stats with them being in the top 5 in both total passes completed and passing % although when cross colrating they are in the bottom 5 in terms of forward passes. By looking at the data it highlights that in fact as a team they play more square and backwards passes rather than playing dangerous forward pass. I think data can highlight area’s of team style of play and can back up the video analysis. However i think data can be very subjective and highlight trends in teams but they change their style of play from game to game. For example, maybe if Kansas City’s changed their team each week or a individual became injured would this affect the data and how they play .
One of the most interesting teams I found while doing my data analysis was about sporting KC, from first glance the data reflects that they are a heavy in possession team as they are top 5 in both total passes and passing % but actually are bottom 5 in forward passes. We can use the data to show that they play a lot of square and backwards passes and maybe lack an urge to connect a dangerous forward pass. I think data can help a lot in the analysis of the game of soccer, however I think it can also do some harm to use as statistics in general cannot solely be used to determine a style of play. For example maybe Kansas City’s starting 8 who plays the most forward passes on the team was hurt for most of the year.
NEW YORK attacks in a possession game, maintaining possession until there are open movements on the flanks, while Houston tries to attack the goal directly with many long passes, looking for depth and trying the 2nd ball
Very contrasting styles in the next two matches:
NYCFC – Risk-averse in the possession, using high-volume, low-risk passes to create scoring opportunities, with a minimal emphasis on long passes, including crosses and finishes. They are very proficient with their passing retention and shot quality, but could struggle if forced to play direct.
Houston – Will look to play forward passes often, particularly in the final third with crosses. They are very proficient in the air and will aim to create a high volume of chances via headers, but should not aim to have large amounts of possession or complete a high percentage of their passes in comparison to many of our other opponents. Defensively, we may need to prepare to have more defensive numbers in goal-scoring areas to counter their high number of shots from close to goal.
Data is a great metric to use alongside other analytic tools. While it make lack certain nuance in regards to common in-game actions, it provides a great framework for understanding a team’s philosophy in their attack.
NEWYORK attack patiently, keeping possesion until the opening arrives while Houston attempt to attack the goal directly with lots of long passes
When playing New York City:
– expect them to possess the ball
– expect them to cross less than most opponents and
– mostly retain possession in the final third to work the ball into the box
When playing Houston
– expect them to be direct in possession,
– expect them play long balls
– expect them to cross the ball often and attempt shots from headers
New york city is a possesion based side that patiently wotk the ball into opposition final third
Houston Dynamo is a direct side that look to create chances through crosses and posses aerial threat.
Data is increasingly used in football to analyze player performance, team strategy, and game outcomes. Teams use data to identify player strengths and weaknesses, track player progress, and make informed decisions about player recruitment and development. Data analysis can also help teams optimize their game strategies by identifying patterns and trends in player and team performance.
Here New york fc play a possession game with high range of passes and building game by shots passes and attacking inside the box giving them option for more shots on target.
Houston playing style is counter attacking with less passes and relying on more crosses and indirect passes having a high range of head goals.
The data shows the most passing teams, the most cross-players, the most possession, or depending on direct play…
Data is really very important
Houston: possesses the ball more, less crosses
New York: more direct in their style of play, more crosses
Houston: possesses the ball more
New York: more direct in their style of play
Houston Dynamo: Favors playing long, direct passes; their strength is in crosses and box-to-box headers. They are among the lowest five in the league for total passes because this style does not call for many passes.
New York City: They keep possession and play a lot of shots, most of which are on target, with a high proportion of ball retention in the attacking third. They play indirect short passes and aim to move opponents about with these passes.
Data combined with Excel or any other Database software or statistical software can be used to compare and find high or low performers . you can also see patterns or find strenghts and weakness of teams or individuals
Data is an Important part of Management to make work easier
Data is becoming very important in modern Football and making the managers job easier for example looking at Bretford and Brighton they are well know for using data and its shown by both of their league positions and recruitment that it is working efficiently
Data has become very important in modern football because it provides important information with much easy and it saves time and resources
Data has become a necessary tool in Football because gives us the opportunity to read and understand model games and playing styles. We must be accurate and ensure that we pick the right data according to our needs to play against our opponents. Only the more relevant is the key to successfully using data, and it can attach with video analysis.
Using data in analyzing the opposition attack will likely reinforce the key principles or trends that are displayed in the game. In addition, the data will provide a complex level of detail with minimal effort and time. The data alone cannot provide the solutions. Data and video analysis are simultaneously needed in order to provide the best picture possible. I think it is also important to not let the data over complicate the report to the coach or the players. Their time and capacity is relatively limited. Therefore, the analysis report should only include the most important pieces. Too much data could cause confusion and become a huge distraction.