{"id":21118,"date":"2022-01-27T09:05:32","date_gmt":"2022-01-27T08:05:32","guid":{"rendered":"https:\/\/soccerment.com\/?p=21118"},"modified":"2024-06-14T12:36:22","modified_gmt":"2024-06-14T12:36:22","slug":"measuring-pressing-success-buildup-disruption-percentage-bdp","status":"publish","type":"post","link":"https:\/\/soccerment.com\/it\/measuring-pressing-success-buildup-disruption-percentage-bdp\/","title":{"rendered":"Measuring pressing success: Buildup Disruption Percentage (BDP)"},"content":{"rendered":"

Pressing is a fundamental part of many teams’ game plan in modern football. At the same time, it is difficult to measure it accurately using event data. While PPDA is certainly a very useful metric, in our view it would be advisable to complement it with different, indirect measurements of how teams are able to prevent their opponents from building up dangerous actions and progressing the ball up the pitch. To fill this gap, we introduce a new metric called Buildup Disruption Percentage<\/em>, or BDP, which quantifies the destructive effect of pressing on the opponent’s pass completion rate.<\/strong> In the current season, the top five clubs in the Top 5 Leagues on BDP are Torino, Hellas Verona, FC K\u00f6ln, Celta Vigo and – unsurprisingly – Klopp’s Liverpool. <\/strong><\/p>\n\n\n\n

Note: Updated on 31st January 2022 – Scroll to the bottom<\/em><\/p>\n\n\n\n

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Introduction: how do we calculate BDP?<\/h2>\n\n\n\n

It is difficult to measure pressing accurately using event data, which only tracks on-ball events, since pressing is mainly an off-ball action, which sometimes results in an attempted defensive action on the ball, but not always. <\/p>\n\n\n\n

Existing metrics, such as PPDA (Passes Per Defensive Action), already provide a useful and well-tested tool in measuring pressure. However, given the challenges outlined above, it would be advisable to complement it with different, indirect measurements of how teams are able to prevent their opponents from building up dangerous actions and progressing the ball up the pitch, as suggested by Italy’s Head of Match Analysis, Antonio Gagliardi<\/strong> (Twitter Profile<\/a>). <\/p>\n\n\n\n

To fill this gap, we introduce a new metric called Buildup Disruption Percentage<\/strong><\/em>, or “BDP”<\/strong>, which quantifies the destructive effect of pressing on the opponent’s pass completion rate.<\/p>\n\n\n\n

The concept behind this new metric is quite simple. If a team executing aggressive pressing is successful, we should expect their opponents to be able to complete fewer passes than they normally would, due to the disruptive action of the opposition press. By computing the team’s pass completion rate for each match, we can compare it with the team’s average rate, and compute a percentage difference. If we then switch the point of view and look at the opposing team, average these differences weighting them by the opponent’s average pass accuracy and change the sign, this tells us how successful their pressing is in disrupting their opponents’ buildup phase. We therefore refer to this quantity as Buildup Disruption Percentage<\/em><\/strong>, or BDP<\/em><\/strong>. Note that we switched the sign of the metric, since it is assigned to the pressing team, so that positive BDP values indicate aggressive and effective pressing, while negative values indicate a lack of pressure or ineffective pressing.<\/p>\n\n\n\n

There are some caveats to consider when reading BDP. Naturally, there are other factors besides the opponent team’s pressing which may lead a team to have a lower (or higher) pass completion rate in a specific match, such as player availability, due to injuries and suspensions, athletic form and psychological momentum. These additional variables surely introduce some additional variance in the BDP, but by taking the average across all matches played, they should even out as the number of matches increases.<\/p>\n\n\n\n

This new metric is a good example of how we can go around the limitations of event data to recover off-the-ball information without tracking data, by making an indirect measurement.<\/p>\n\n\n\n

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Which teams have the most disruptive pressing in Europe?<\/h2>\n\n\n\n

If we apply this model to all teams in the top 5 European leagues, we find some surprising results at the top of the ranking. The team with the most destructive pressing is Torino (10.8%), followed by fellow Serie A club Verona (10.5%), and FC K\u00f6ln (10.1%). The highest ranking top team is Liverpool (8.7%) in 5th place, with well-known pressing teams such as Marcelo Bielsa’s Leeds<\/a> (7.7%), Atalanta<\/a> (6.9%) and RB Leipzig<\/a> (6.8%) also figuring in the top 10.<\/p>\n\n\n\n

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We can compare BDP with PPDA, with the caveat that we should consider BDP only in the pressing team’s opponents’ half, since PPDA is computed using events in the highest 40% of the pitch. Conceptually, since PPDA takes into account passes and defensive events regardless of their outcome, it mostly focuses on pressing intent<\/strong> rather than its outcome. In this sense, by measuring indirectly the effect of pressing on the opponent team’s buildup, BDP is more geared towards measuring pressing success<\/strong>. <\/p>\n\n\n\n

The relationship between the two metrics is shown in the scatterplot below. As expected, there is a well-defined anti-correlation between BDP and PPDA<\/strong> (smaller values of the latter indicate more pressing). The dispersion in BDP for the same PPDA value is a measure of how successful a team’s pressing is compared to their effort.<\/p>\n\n\n\n

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We can see that the top five teams for general BDP significantly outperform the linear relationship with PPDA<\/strong>. This could have two main interpretations: these teams’ pressing is particularly efficient, and\/or a significant part of the pressing workload is given by careful positioning off the ball, without resulting in explicit defensive events which PPDA is sensitive to. Verona’s BDP numbers are especially noteworthy: Tudor’s team has a similar PPDA to Manchester City<\/a>, but while the EPL champions have a BDP in the attacking half of 1.0%, the Italians record the highest value with 8.6%.<\/p>\n\n\n\n

It is also interesting to look at the relationship between BDP and fouls made per game: this is shown in the scatterplot below. While one might expect to see a correlation between the two, this is actually not the case. The most disruptive pressers, Torino, also commit the most fouls per game, but this does not extend into a global correlation for the rest of the teams. This is exemplified by teams such as Bayern<\/a>, Manchester City and Chelsea<\/a>, who are in the top half of the distribution for BDP, but find themselves at the low end of the distribution for fouls per game. Furthermore, some teams show an opposite trend, with a high number of fouls per game but low BDP: this is the case for Valencia<\/a>, Getafe<\/a> and Levante<\/a>, among others.<\/p>\n\n\n\n

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