{"id":1326,"date":"2025-12-17T20:07:38","date_gmt":"2025-12-17T17:07:38","guid":{"rendered":"http:\/\/api-sport.pro\/?p=1326"},"modified":"2025-12-17T20:07:38","modified_gmt":"2025-12-17T17:07:38","slug":"what-is-ppda-and-how-does-this-metric-help-in-press-analysis","status":"publish","type":"post","link":"https:\/\/api-sport.pro\/es\/what-is-ppda-and-how-does-this-metric-help-in-press-analysis\/","title":{"rendered":"What is PPDA and how does this metric help in press analysis?"},"content":{"rendered":"<div class=\"table-of-contents\">\n<div class=\"table-of-contents-title\">Contenidos<\/div>\n<ul class=\"table-of-contents-ul\">\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-1\">PPDA in football what is this statistic in simple terms<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-2\">How is the PPDA metric calculated formula and examples<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-3\">What PPDA values are considered high and low for pressing analysis<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-4\">Which football APIs provide the PPDA metric and advanced pressing statistics<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-5\">How to obtain a team\u2019s PPDA data through sports event API step by step<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-6\">How to use PPDA from the API for tactical match analysis and pressing quality<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-7\">Integration of the PPDA indicator from the API into analytical reports and dashboards<\/a><\/li>\n<\/ul>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-1\">PPDA in football what is this statistic in simple terms<\/h2>\n<p>PPDA (Passes Allowed Per Defensive Action) in football is a metric that shows how many passes the opponent is allowed to make before the team performs one active defensive action in the pressing zone. Simply put, it measures how aggressively and organized the team presses the opponent without the ball. The lower the PPDA value, the more often the team engages in tackles, interceptions, and fouls, not allowing the opponent to play the ball freely. A high PPDA, on the contrary, indicates passive defense and a large number of free passes for the opponent.<\/p>\n<p>In the classic interpretation, PPDA is calculated based on actions that occur in the midfield and attacking zone of the pressing team (approximately 60 % closer to the opponent\u2019s goal). It takes into account the opponent\u2019s passes in this part of the field and divides them by the number of defensive actions of the pressing team: tackles, attempts to tackle, interceptions, fouls aimed at tackling, and other challenges for the ball. This allows the metric to accurately describe not only the volume of work without the ball but also stylistic features: high, medium, or low block, activity of the first lines of pressure.<\/p>\n<p>Today, PPDA is widely used by scouts, club analysts, media, and betting companies because it lends itself well to automatic calculation based on match event statistics. It is enough to have detailed data on passes and defensive actions to build your own pressing models. Such data is provided by the sports event API. <a href=\"http:\/\/api-sport.pro\/es\/\">api-sport.pro<\/a>For each football match, you receive a structure with extended statistics, from which you can independently calculate PPDA for both teams and integrate this indicator into your services, dashboards, or forecasting systems.<\/p>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-2\">How is the PPDA metric calculated formula and examples<\/h2>\n<p>The basic formula for PPDA looks like this: PPDA = P \/ DA, where P is the number of opponent\u2019s passes in your pressing zone, and DA (Defensive Actions) is the number of your defensive actions in the same zone. Defensive actions usually include tackles, attempts to tackle, interceptions, pressing duels, and fouls committed while trying to regain possession of the ball. In professional models, the numerator includes the opponent\u2019s passes in their defensive and central thirds of the field, while the denominator includes all actions of your team without the ball aimed at regaining possession.<\/p>\n<p>Let\u2019s consider a simplified example. The opponent made 110 passes in their and the middle zones during the first half, while your team made 28 defensive actions (tackles, interceptions, fouls while attempting to tackle). Then your team\u2019s PPDA for the half will be 110 \/ 28 \u2248 3.93. This is a very low value: you allow an average of less than four passes before actively engaging in the fight, which characterizes extremely high pressing. If the opponent made the same 110 passes, but there were only 10 defensive actions, PPDA = 11 \u2014 this indicator is closer to the average or even low level of pressure.<\/p>\n<p>When working with sports event APIs, for example, with a football API service <a href=\"http:\/\/api-sport.pro\/es\/\">api-sport.pro<\/a>, it is convenient to take the numerator and denominator of the formula from the extended match statistics. In the object <strong>estad\u00edsticasDelPartido<\/strong> you will find the total passes of the opponent (key <em>pases<\/em>), as well as values for tackles (<em>totalTackle<\/em>), interceptions (<em>interceptionWon<\/em>), duels and fouls (<em>faltas<\/em>). By summing the defensive actions for the required team and dividing the number of opponent\u2019s passes by the obtained sum, you get the PPDA for the match or for a specific half (periods ALL, 1ST, 2ND are presented in the data), after which you can store and analyze this indicator in your own analytical system.<\/p>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-3\">What PPDA values are considered high and low for pressing analysis<\/h2>\n<p>The values of PPDA should always be interpreted in context: leagues, team style, game speed, and match samples. However, in the practice of football analytics, approximate ranges have been established. Values up to 6-7 are conditionally considered very low PPDA (i.e., extremely high pressing). These are teams that practically do not allow the opponent to breathe, actively involve forwards and midfielders in the pressure, and try to regain possession as quickly as possible after losing the ball. The range of 7-10 is usually referred to as high but more balanced pressing.<\/p>\n<p>Indicators around 10-14 are considered average: the team presses episodically, choosing moments when it has a numerical advantage or advantageous positioning, while consciously sitting back at times, covering dangerous areas. Values above 14-15 indicate low or moderate pressing. This can be a conscious strategy of playing as the second team, betting on compactness in a low block and counterattacks, or it may indicate problems with physicality or coordination of pressing between lines. It is important to remember that a \u00abgood\u00bb PPDA is not always minimal; it should correspond to the game plan that the team implements.<\/p>\n<p>Analysts using data from sports event APIs usually look not at a single PPDA value but at the dynamics: the average figure for the season, differences at home and away, dependence on the status of the favorite or outsider. Based on data obtained through the API, matches can be grouped by coach, formation, phase of the season, and one can see how the intensity of pressure changes. This approach allows for a more accurate assessment of the trend: whether the team has indeed started pressing more or if a spike in one match is related to a specific opponent and the scenario of the match (quick goal, red card, etc.).<\/p>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-4\">Which football APIs provide the PPDA metric and advanced pressing statistics<\/h2>\n<p>Football APIs can be conditionally divided into two types: services that immediately provide ready-made advanced metrics (PPDA, high recovery metrics, zone pressing, etc.), and sources that provide detailed event statistics, based on which such indicators are calculated independently. The first option is convenient for speed of implementation but is rigidly tied to someone else\u2019s calculation methodology and is usually more expensive. The second option offers flexibility: you define the pressing zone, set of defensive actions, and can adapt PPDA to your analytical requirements.<\/p>\n<p>La plataforma <a href=\"http:\/\/api-sport.pro\/es\/\">api-sport.pro<\/a> refers to the second, more universal type. Within the football API, you receive an extended object for each match. <strong>estad\u00edsticasDelPartido<\/strong> with a breakdown by key groups: passes (including accurate, long, in the final third), recoveries, interceptions, duels, fouls, clearances, saves, and much more. This data is sufficient to calculate PPDA, intensity of recoveries by halves and opponents, build your own pressing models, and combine them with other analytics, such as possession or number of shots.<\/p>\n<p>Below is an example of a simple API request that retrieves data for a specific football match along with extended statistics. This is enough to calculate PPDA for both teams on your side and store it in a database or use it directly in the application:<\/p>\n<pre class=\"brush: bash; title: ; notranslate\" title=\"\" data-no-translation=\"\">curl -X GET &quot;https:\/\/api.api-sport.ru\/v2\/football\/matches\/14570728&quot; \\\n  -H &quot;Authorization: YOUR_API_KEY&quot;<\/pre>\n<p>In the response, you will find an object <strong>estad\u00edsticasDelPartido<\/strong> with an array of periods (ALL, 1ST, 2ND). In each period, the statistics are broken down into groups (<em>Resumen del partido<\/em>, <em>Defensa<\/em>, <em>Pases<\/em> etc.), where each indicator has values for the home and away teams (<strong>costoLocal<\/strong>, <strong>costoVisitante<\/strong>1. ). Based on these fields, you can build your own PPDA calculation module, and with the emergence of WebSocket connections and AI add-ons on the api-sport.ru service side \u2014 update pressing metrics in almost real-time without complex infrastructure modifications.<\/p>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-5\">How to obtain a team\u2019s PPDA data through sports event API step by step<\/h2>\n<p>2. To calculate PPDA through the sports events API, just a few sequential steps are needed. First, you register on the platform and obtain an access key to the API in <a href=\"https:\/\/app.api-sport.ru\">la cuenta personal.<\/a>. 3. . Then you define the match you are interested in: it can be a specific game found by ID, or a list of matches by date, tournament, and team. After that, you request the details of the needed match with extended statistics and on your server or application, calculate PPDA based on the provided numerical fields.<\/p>\n<p>4. Practically, it looks like this. Step 1 \u2014 get a list of matches, for example, for a specific date and tournament, through the method <strong>\/v2\/f\u00fatbol\/partidos<\/strong>, 5. , using filters <em>fecha<\/em> \u0438 <em>torneo_id<\/em>. 6. . From the response, take the needed <strong>idPartido<\/strong>. 7. . Step 2 \u2014 request the details of this match using the method <strong>\/v2\/f\u00fatbol\/partidos\/{matchId}<\/strong> 8. and extract the object <strong>estad\u00edsticasDelPartido<\/strong>. 9. . Step 3 \u2014 find the indicators in the array with the period <em>TODO<\/em> indicators <em>pases<\/em> (opponent\u2019s passes) and your team\u2019s defensive actions: <em>totalTackle<\/em>, <em>interceptionWon<\/em>, <em>recuperaci\u00f3nDeBal\u00f3n<\/em>, <em>faltas<\/em>. Step 4 \u2014 sum up the defensive actions, divide the opponent\u2019s passes by the obtained sum, and save the calculated PPDA value.<\/p>\n<p>Below is an example of Python code that shows the basic logic for obtaining a match, extracting statistics, and calculating PPDA for the home team. In a real project, you can wrap this algorithm in a scheduler task, run through the season, and calculate PPDA for all of the team\u2019s games:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\" data-no-translation=\"\">import requests\nAPI_KEY = &quot;YOUR_API_KEY&quot;\nBASE_URL = &quot;https:\/\/api.api-sport.ru\/v2\/football&quot;\nmatch_id = 14570728\nresp = requests.get(\n    f&quot;{BASE_URL}\/matches\/{match_id}&quot;,\n    headers={&quot;Authorization&quot;: API_KEY},\n)\nmatch = resp.json()\nstats = match.get(&quot;matchStatistics&quot;, &#x5B;])\nall_period = next((p for p in stats if p.get(&quot;period&quot;) == &quot;ALL&quot;), None)\ndef get_value(key, side=&quot;home&quot;):\n    if not all_period:\n        return 0\n    for group in all_period.get(&quot;groups&quot;, &#x5B;]):\n        for item in group.get(&quot;statisticsItems&quot;, &#x5B;]):\n            if item.get(&quot;key&quot;) == key:\n                return item.get(f&quot;{side}Value&quot;, 0)\n    return 0\n# \u043f\u0435\u0440\u0435\u0434\u0430\u0447\u0438 \u0441\u043e\u043f\u0435\u0440\u043d\u0438\u043a\u0430 \u043f\u043e \u0432\u0441\u0435\u043c\u0443 \u043c\u0430\u0442\u0447\u0443\nopponent_passes_for_home = get_value(&quot;passes&quot;, side=&quot;away&quot;)\n# \u043e\u0431\u043e\u0440\u043e\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u0445\u043e\u0437\u044f\u0435\u0432\nhome_def_actions = (\n    get_value(&quot;totalTackle&quot;, side=&quot;home&quot;) +\n    get_value(&quot;interceptionWon&quot;, side=&quot;home&quot;) +\n    get_value(&quot;ballRecovery&quot;, side=&quot;home&quot;) +\n    get_value(&quot;fouls&quot;, side=&quot;home&quot;)\n)\nppda_home = opponent_passes_for_home \/ home_def_actions if home_def_actions else None\nprint(&quot;PPDA (home):&quot;, ppda_home)<\/pre>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-6\">How to use PPDA from the API for tactical match analysis and pressing quality<\/h2>\n<p>The PPDA metric calculated from the API data becomes a powerful tool for tactical analysis. In pre-match preparation, analysts look at the average PPDA of the team for the season and for specific segments: at home and away, against comparable opponents, with different formations (4-3-3, 3-4-3, etc.). If the sample of matches shows that the team consistently maintains a PPDA of 6-8, one can expect intense high pressing. Conversely, stable values above 14-15 will indicate to the coaching staff that the opponent prefers to defend low and cede initiative.<\/p>\n<p>During the match itself, the PPDA calculated from live statistics from the API helps track changes in the game plan. Through regular requests to the method <strong>\/v2\/f\u00fatbol\/partidos<\/strong> con estado <em>en progreso<\/em> you receive an updated object <strong>estad\u00edsticasDelPartido<\/strong> and can recalculate PPDA every few minutes. If after conceding a goal or taking the lead your metric changes sharply (for example, from 8 to 13), this is a signal: the team has reduced the intensity of pressure or shifted the block lower. In the future, the planned integration of WebSocket on the platform side will allow receiving updates on statistics without polling the API and building fully live tactical panels.<\/p>\n<p>For betting and risk management, the PPDA obtained through the API is especially valuable in conjunction with bookmaker odds. In the responses of the method <strong>\/v2\/f\u00fatbol\/partidos<\/strong> you can simultaneously receive both match statistics and markets <strong>oddsBase<\/strong>. This allows building signal models: if the favorite unexpectedly \u00ablets go\u00bb of the opponent (PPDA rises, pressure drops), but the bookmaker line has not yet adjusted, the system can highlight such games as potentially interesting for adjusting limits or odds. Below is an example of a simplified API request in JavaScript format:<\/p>\n<pre class=\"brush: jscript; title: ; notranslate\" title=\"\" data-no-translation=\"\">fetch(&quot;https:\/\/api.api-sport.ru\/v2\/football\/matches?status=inprogress&quot;, {\n  headers: { Authorization: &quot;YOUR_API_KEY&quot; },\n})\n  .then((r) =&gt; r.json())\n  .then((data) =&gt; {\n    data.matches.forEach((match) =&gt; {\n      \/\/ \u0437\u0434\u0435\u0441\u044c \u043c\u043e\u0436\u043d\u043e \u0438\u0437\u0432\u043b\u0435\u0447\u044c match.matchStatistics \u0438 oddsBase\n      \/\/ \u0438 \u043f\u043e\u0441\u0447\u0438\u0442\u0430\u0442\u044c PPDA + \u0441\u043e\u043f\u043e\u0441\u0442\u0430\u0432\u0438\u0442\u044c \u0441 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u0430\u043c\u0438\n    });\n  });<\/pre>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-7\">Integration of the PPDA indicator from the API into analytical reports and dashboards<\/h2>\n<p>In order for PPDA to become an integral part of your analytics, it needs to be integrated into the regular data flow: collection via API, calculation of the metric, storage in a repository, and visualization in reports or dashboards. A typical architecture looks like this: a background service or cron job regularly accesses the sports events API <a href=\"http:\/\/api-sport.pro\/es\/\">api-sport.pro<\/a>, retrieves fresh matches and their <strong>estad\u00edsticasDelPartido<\/strong>, calculates PPDA for each team and records the results in a database or analytical layer (Data Warehouse). Then the BI system or custom frontend builds graphs by seasons, coaches, tournaments, comparing the intensity of pressing and its dynamics.<\/p>\n<p>Technically, this can be implemented on any stack: Python, Node.js, PHP, Go \u2014 it is more important to properly organize the storage scheme. It is convenient to create a separate table or collection <em>team_pressing_metrics<\/em>, where for each team, match, and period the calculated PPDA will be stored, as well as related metrics (possession, shots, xG, if you count them separately). This approach simplifies the construction of complex reports: \u00abTeam PPDA when the score is 0:0\u00bb, \u00abpressing intensity in the first 15 minutes\u00bb, \u00abcomparison of pressure against top clubs and mid-table teams\u00bb, etc. With the introduction of WebSocket support, you will be able to update such dashboards almost online, without overloading the API with repeated requests.<\/p>\n<p>Below is an example of a conditional SQL query for a simple report on PPDA, calculated based on data obtained from the API. In a real project, the fields and table names may differ, but the idea remains the same: we use the calculated PPDA as a regular numeric field, which can be used to build filters, aggregations, and visualizations:<\/p>\n<pre class=\"brush: sql; title: ; notranslate\" title=\"\" data-no-translation=\"\">SELECT\n  season,\n  team_name,\n  AVG(ppda)       AS avg_ppda,\n  AVG(possession) AS avg_possession,\n  COUNT(*)        AS matches\nFROM team_pressing_metrics\nWHERE tournament_id = 7 -- \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u041b\u0438\u0433\u0430 \u0447\u0435\u043c\u043f\u0438\u043e\u043d\u043e\u0432\nGROUP BY season, team_name\nORDER BY avg_ppda ASC;<\/pre>\n<p>Thus, by setting up the retrieval of matches via API and the calculation of PPDA once, you turn raw statistics into a clear metrics language for coaches, analysts, and betting specialists. All of this is built on top of a single data source \u2014 the sports events and bookmaker odds API, which is constantly evolving: new sports are added, detailed statistics, live events, and in the future \u2014 AI modules for automatic detection of anomalies in pressing and other game patterns.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>The content of PPDA in football what is this statistic in simple words How is the PPDA metric calculated formula and examples What PPDA values are considered high and low for press analysis What football APIs provide the PPDA metric and advanced pressing statistics How to obtain team PPDA data through sports event API step by step How to use PPDA from API for tactical analysis [\u2026]<\/p>","protected":false},"author":1,"featured_media":1325,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","faq":"[{\"question\":\"\u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 PPDA \u0432 \u0444\u0443\u0442\u0431\u043e\u043b\u0435 \u043f\u0440\u043e\u0441\u0442\u044b\u043c\u0438 \u0441\u043b\u043e\u0432\u0430\u043c\u0438?\",\"answer\":\"PPDA (Passes Allowed Per Defensive Action) \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043f\u0435\u0440\u0435\u0434\u0430\u0447 \u0441\u043e\u043f\u0435\u0440\u043d\u0438\u043a \u0443\u0441\u043f\u0435\u0432\u0430\u0435\u0442 \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0434\u043e \u043e\u0434\u043d\u043e\u0433\u043e \u0432\u0430\u0448\u0435\u0433\u043e \u043e\u0431\u043e\u0440\u043e\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u0432 \u0437\u043e\u043d\u0435 \u043f\u0440\u0435\u0441\u0441\u0438\u043d\u0433\u0430. \u041d\u0438\u0437\u043a\u0438\u0439 PPDA \u043e\u0437\u043d\u0430\u0447\u0430\u0435\u0442 \u0438\u043d\u0442\u0435\u043d\u0441\u0438\u0432\u043d\u044b\u0439 \u0432\u044b\u0441\u043e\u043a\u0438\u0439 \u043f\u0440\u0435\u0441\u0441\u0438\u043d\u0433, \u0432\u044b\u0441\u043e\u043a\u0438\u0439 PPDA \u2014 \u0431\u043e\u043b\u0435\u0435 \u043f\u0430\u0441\u0441\u0438\u0432\u043d\u0443\u044e \u043e\u0431\u043e\u0440\u043e\u043d\u0443 \u0438 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u0441\u0432\u043e\u0431\u043e\u0434\u0443 \u0434\u043b\u044f \u0440\u043e\u0437\u044b\u0433\u0440\u044b\u0448\u0430 \u043c\u044f\u0447\u0430 \u0443 \u043e\u043f\u043f\u043e\u043d\u0435\u043d\u0442\u0430.\"},{\"question\":\"\u041a\u0430\u043a\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0443\u0436\u043d\u044b \u0438\u0437 API, \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0441\u0447\u0438\u0442\u0430\u0442\u044c PPDA?\",\"answer\":\"\u0414\u043b\u044f \u0440\u0430\u0441\u0447\u0451\u0442\u0430 PPDA \u043d\u0443\u0436\u043d\u044b \u0434\u0432\u0435 \u0433\u0440\u0443\u043f\u043f\u044b \u0434\u0430\u043d\u043d\u044b\u0445: \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0435\u0440\u0435\u0434\u0430\u0447 \u0441\u043e\u043f\u0435\u0440\u043d\u0438\u043a\u0430 \u0432 \u0437\u043e\u043d\u0435 \u0432\u0430\u0448\u0435\u0433\u043e \u043f\u0440\u0435\u0441\u0441\u0438\u043d\u0433\u0430 \u0438 \u0447\u0438\u0441\u043b\u043e \u0432\u0430\u0448\u0438\u0445 \u043e\u0431\u043e\u0440\u043e\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 \u0432 \u044d\u0442\u043e\u0439 \u0437\u043e\u043d\u0435 (\u043e\u0442\u0431\u043e\u0440\u044b, \u043f\u0435\u0440\u0435\u0445\u0432\u0430\u0442\u044b, \u0435\u0434\u0438\u043d\u043e\u0431\u043e\u0440\u0441\u0442\u0432\u0430, \u0444\u043e\u043b\u044b \u043f\u0440\u0438 \u043f\u043e\u043f\u044b\u0442\u043a\u0435 \u043e\u0442\u0431\u043e\u0440\u0430). \u0412 API api-sport.ru \u044d\u0442\u043e \u0431\u0435\u0440\u0451\u0442\u0441\u044f \u0438\u0437 \u043e\u0431\u044a\u0435\u043a\u0442\u0430 matchStatistics: \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438 passes, totalTackle, interceptionWon, ballRecovery, fouls \u0441 \u0440\u0430\u0437\u0431\u0438\u0432\u043a\u043e\u0439 \u043f\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u0430\u043c \u0438 \u043f\u0435\u0440\u0438\u043e\u0434\u0430\u043c \u043c\u0430\u0442\u0447\u0430.\"},{\"question\":\"\u041c\u043e\u0436\u043d\u043e \u043b\u0438 \u043f\u043e\u043b\u0443\u0447\u0430\u0442\u044c PPDA \u0432 \u0440\u0435\u0436\u0438\u043c\u0435 live \u0447\u0435\u0440\u0435\u0437 API?\",\"answer\":\"\u0413\u043e\u0442\u043e\u0432\u043e\u0435 \u043f\u043e\u043b\u0435 PPDA \u0432 \u0442\u0435\u043a\u0443\u0449\u0435\u0439 \u0432\u0435\u0440\u0441\u0438\u0438 API \u043e\u0431\u044b\u0447\u043d\u043e \u043d\u0435 \u043e\u0442\u0434\u0430\u0451\u0442\u0441\u044f, \u043d\u043e \u0435\u0433\u043e \u043b\u0435\u0433\u043a\u043e \u043f\u0435\u0440\u0435\u0441\u0447\u0438\u0442\u044b\u0432\u0430\u0442\u044c \u0432 live\u2011\u0440\u0435\u0436\u0438\u043c\u0435. \u0412\u044b \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e \u0437\u0430\u043f\u0440\u0430\u0448\u0438\u0432\u0430\u0435\u0442\u0435 \u043c\u0430\u0442\u0447\u0438 \u0441\u043e \u0441\u0442\u0430\u0442\u0443\u0441\u043e\u043c inprogress \u0447\u0435\u0440\u0435\u0437 \/v2\/football\/matches, \u0438\u0437\u0432\u043b\u0435\u043a\u0430\u0435\u0442\u0435 \u043e\u0431\u043d\u043e\u0432\u043b\u0451\u043d\u043d\u044b\u0439 matchStatistics \u0438 \u0437\u0430\u043d\u043e\u0432\u043e \u0441\u0447\u0438\u0442\u0430\u0435\u0442\u0435 PPDA. \u0422\u0430\u043a \u043c\u043e\u0436\u043d\u043e \u043e\u0442\u0441\u043b\u0435\u0436\u0438\u0432\u0430\u0442\u044c, \u043a\u0430\u043a \u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f \u0438\u043d\u0442\u0435\u043d\u0441\u0438\u0432\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u0435\u0441\u0441\u0438\u043d\u0433\u0430 \u0432 \u0442\u0435\u0447\u0435\u043d\u0438\u0435 \u043c\u0430\u0442\u0447\u0430. \u0412 \u043f\u0435\u0440\u0441\u043f\u0435\u043a\u0442\u0438\u0432\u0435 WebSocket\u2011\u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 \u0443\u043f\u0440\u043e\u0441\u0442\u0438\u0442 \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u0435 \u0442\u0430\u043a\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u043c \u0432\u0440\u0435\u043c\u0435\u043d\u0438.\"},{\"question\":\"\u041a\u0430\u043a\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f PPDA \u0441\u0447\u0438\u0442\u0430\u044e\u0442\u0441\u044f \u0432\u044b\u0441\u043e\u043a\u0438\u043c\u0438 \u0438 \u043d\u0438\u0437\u043a\u0438\u043c\u0438?\",\"answer\":\"\u041e\u0440\u0438\u0435\u043d\u0442\u0438\u0440\u043e\u0432\u043e\u0447\u043d\u043e PPDA \u0434\u043e 6\u20137 \u0441\u0447\u0438\u0442\u0430\u044e\u0442 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u043c \u043e\u0447\u0435\u043d\u044c \u0438\u043d\u0442\u0435\u043d\u0441\u0438\u0432\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0441\u0441\u0438\u043d\u0433\u0430, 7\u201310 \u2014 \u0432\u044b\u0441\u043e\u043a\u043e\u0433\u043e, 10\u201314 \u2014 \u0441\u0440\u0435\u0434\u043d\u0435\u0433\u043e, \u0441\u0432\u044b\u0448\u0435 14\u201315 \u2014 \u043d\u0438\u0437\u043a\u043e\u0433\u043e \u0434\u0430\u0432\u043b\u0435\u043d\u0438\u044f. \u041d\u043e \u044d\u0442\u0438 \u043f\u043e\u0440\u043e\u0433\u0438 \u0437\u0430\u0432\u0438\u0441\u044f\u0442 \u043e\u0442 \u043b\u0438\u0433\u0438, \u0441\u0442\u0438\u043b\u044f \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u0438 \u0432\u044b\u0431\u043e\u0440\u043a\u0438 \u043c\u0430\u0442\u0447\u0435\u0439, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0438 \u0441\u043c\u043e\u0442\u0440\u044f\u0442 \u0432 \u043f\u0435\u0440\u0432\u0443\u044e \u043e\u0447\u0435\u0440\u0435\u0434\u044c \u043d\u0430 \u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0443 PPDA \u043f\u043e \u0441\u0435\u0437\u043e\u043d\u0443 \u0438 \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 \u0441 \u0441\u043e\u0441\u0435\u0434\u044f\u043c\u0438 \u043f\u043e \u0442\u0443\u0440\u043d\u0438\u0440\u0443.\"},{\"question\":\"\u041a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c PPDA \u0438\u0437 API \u0432 \u0431\u0435\u0442\u0442\u0438\u043d\u0433\u0435 \u0438 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u043e\u0432?\",\"answer\":\"PPDA \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u043f\u043e\u043d\u044f\u0442\u044c, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u0430 \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0434\u0430\u0432\u0438\u0442 \u043d\u0430 \u0441\u043e\u043f\u0435\u0440\u043d\u0438\u043a\u0430 \u0432 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u043c \u043c\u0430\u0442\u0447\u0435, \u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043f\u043e \u0441\u0447\u0435\u0442\u0443 \u0438 \u0432\u043b\u0430\u0434\u0435\u043d\u0438\u044e \u043c\u044f\u0447\u043e\u043c. \u0427\u0435\u0440\u0435\u0437 API api-sport.ru \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u043e\u0434\u043d\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e \u043f\u043e\u043b\u0443\u0447\u0430\u0442\u044c \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0443 \u043c\u0430\u0442\u0447\u0430 (\u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0451\u0442\u0430 PPDA) \u0438 \u0440\u044b\u043d\u043a\u0438 oddsBase \u0441 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u0430\u043c\u0438. \u042d\u0442\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0441\u0442\u0440\u043e\u0438\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0440\u0435\u0430\u0433\u0438\u0440\u0443\u044e\u0442 \u043d\u0430 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u043f\u0440\u0435\u0441\u0441\u0438\u043d\u0433\u0430: \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043e\u0446\u0435\u043d\u043a\u0443 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0438 \u0433\u043e\u043b\u0430 \u0444\u0430\u0432\u043e\u0440\u0438\u0442\u0430, \u0435\u0441\u043b\u0438 \u0435\u0433\u043e PPDA \u0440\u0435\u0437\u043a\u043e \u0432\u044b\u0440\u043e\u0441 \u0438 \u0434\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u0441\u043b\u0430\u0431\u043b\u043e.\"},{\"question\":\"\u041d\u0443\u0436\u0435\u043d \u043b\u0438 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u0439 \u0441\u0435\u0440\u0432\u0438\u0441, \u0435\u0441\u043b\u0438 PPDA \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0441\u0430\u043c\u043e\u043c\u0443 \u043f\u043e API?\",\"answer\":\"\u041e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u0439 \u0441\u0435\u0440\u0432\u0438\u0441 \u043d\u0435 \u043d\u0443\u0436\u0435\u043d: \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0438\u043c\u0435\u0442\u044c \u0434\u043e\u0441\u0442\u0443\u043f \u043a \u0434\u0435\u0442\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0435 \u043c\u0430\u0442\u0447\u0435\u0439. API \u0441\u043f\u043e\u0440\u0442\u0438\u0432\u043d\u044b\u0445 \u0441\u043e\u0431\u044b\u0442\u0438\u0439 api-sport.ru \u043a\u0430\u043a \u0440\u0430\u0437 \u0438 \u0440\u0435\u0448\u0430\u0435\u0442 \u044d\u0442\u0443 \u0437\u0430\u0434\u0430\u0447\u0443 \u2014 \u0432\u044b \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442\u0435 \u0435\u0434\u0438\u043d\u044b\u0439 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u044b\u0439 JSON \u043f\u043e \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0443 \u0442\u0443\u0440\u043d\u0438\u0440\u043e\u0432 \u0438 \u0432\u0438\u0434\u043e\u0432 \u0441\u043f\u043e\u0440\u0442\u0430 \u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0441\u0430\u043c\u0438 \u0441\u0442\u0440\u043e\u0438\u0442\u044c \u043f\u043e\u0432\u0435\u0440\u0445 \u043d\u0435\u0433\u043e \u043b\u044e\u0431\u044b\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0438, \u0432\u043a\u043b\u044e\u0447\u0430\u044f PPDA, \u0431\u0435\u0437 \u043f\u0440\u0438\u0432\u044f\u0437\u043a\u0438 \u043a \u0447\u0443\u0436\u043e\u0439 \u043c\u0435\u0442\u043e\u0434\u043e\u043b\u043e\u0433\u0438\u0438 \u0438 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u044f\u043c \u0433\u043e\u0442\u043e\u0432\u044b\u0445 \u043e\u0442\u0447\u0451\u0442\u043e\u0432.\"}]","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1326","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"<title>PPDA in football and pressing analysis \u2014 API api-sport.ru<\/title>\n<meta name=\"description\" content=\"What is PPDA and how to calculate the intensity of pressing using data from the football API. 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