{"id":1344,"date":"2025-12-17T20:07:53","date_gmt":"2025-12-17T17:07:53","guid":{"rendered":"http:\/\/api-sport.pro\/?p=1344"},"modified":"2025-12-17T20:07:53","modified_gmt":"2025-12-17T17:07:53","slug":"what-metrics-are-important-for-evaluating-forwards-2","status":"publish","type":"post","link":"https:\/\/api-sport.pro\/es\/what-metrics-are-important-for-evaluating-forwards-2\/","title":{"rendered":"\u00bfQu\u00e9 m\u00e9tricas son importantes para evaluar a los delanteros?"},"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\">What metrics are important for evaluating a forward\u2019s effectiveness in football<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-2\">What data about forwards can be obtained through the sports statistics API<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-3\">How to calculate xG, xA, and other goal metrics for a forward through the API<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-4\">How to analyze a forward\u2019s shots, positions, and actions on the field using the API<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-5\">What advanced metrics for forwards are available in popular sports event APIs<\/a><\/li>\n<li class=\"table-of-contents-li\"><a class=\"table-of-contents-a\" href=\"#contents-6\">How to automate the calculation of forward metrics based on API data<\/a><\/li>\n<\/ul>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-1\">What metrics are important for evaluating a forward\u2019s effectiveness in football<\/h2>\n<p>Evaluating a forward has long gone beyond simple goal statistics. In modern analytics, three blocks of indicators are important: effectiveness, quality of chances, and involvement in the game. Basic metrics include goals, assists, shots on goal, shots on target, and the share of goals without penalties. They help to understand how consistently a player finishes attacks. Additionally, the conversion rate of shots, the share of shots from inside the box, and how often a forward finishes attacks after key passes from teammates are analyzed.<\/p>\n<p>The context of the quality of chances is no less important. Here, expected goals (xG) and expected assists (xA) metrics, the conversion rate of \u00abbig chances,\u00bb as well as the number of touches in the box, dribbles, and duels won in the final third help. According to the matchStatistics object in the responses from the Sport Events API, one can assess how much the team as a whole creates chances (totalShotsOnGoal, totalShotsInsideBox, bigChanceCreated, bigChanceScored, touchesInOppBox), and then overlay this on the individual numbers of the player obtained from their personal statistics in the lineup (lineup.players.statistics).<\/p>\n<p>The third layer is sustainability and usefulness over distance: shots and goals per 90 minutes, contribution to pressing, number of losses and offsides, participation in combinations leading to shots. These metrics allow distinguishing a forward who scored due to a short successful streak from a player with a consistently high level of play. The source of such data is specialized solutions based on the Sport Events API: through regular requests to the endpoints \/v2\/football\/matches and \/v2\/football\/matches\/{matchId}, you automatically collect match statistics, filter it by the desired player, and build your advanced models for evaluating the effectiveness of forwards.<\/p>\n<pre class=\"brush: jscript; title: ; notranslate\" title=\"\" data-no-translation=\"\">\n\/\/ \u041f\u0440\u0438\u043c\u0435\u0440: \u0437\u0430\u043f\u0440\u043e\u0441 \u0434\u0435\u0442\u0430\u043b\u0435\u0439 \u043c\u0430\u0442\u0447\u0430 \u0438 \u0431\u0430\u0437\u043e\u0432\u043e\u0439 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0430\u0442\u0430\u043a\u0443\u044e\u0449\u0435\u0439 \u043a\u043e\u043c\u0430\u043d\u0434\u044b\nfetch('https:\/\/api.api-sport.ru\/v2\/football\/matches\/14570728', {\n  headers: {\n    'Authorization': '\u0412\u0410\u0428_API_\u041a\u041b\u042e\u0427'\n  }\n})\n  .then(r =&gt; r.json())\n  .then(match =&gt; {\n    const stats = match.matchStatistics.find(s =&gt; s.period === 'ALL');\n    const shotsGroup = stats.groups.find(g =&gt; g.groupName === 'Shots');\n    const attackGroup = stats.groups.find(g =&gt; g.groupName === 'Attack');\n    \/\/ \u041f\u0440\u0438\u043c\u0435\u0440 \u0431\u0430\u0437\u043e\u0432\u044b\u0445 \u043c\u0435\u0442\u0440\u0438\u043a \u0434\u043b\u044f \u043e\u0446\u0435\u043d\u043a\u0438 \u0430\u0442\u0430\u043a\u0443\u044e\u0449\u0435\u0439 \u043c\u043e\u0449\u0438 \u043a\u043e\u043c\u0430\u043d\u0434\u044b\n    const totalShots = shotsGroup.statisticsItems.find(i =&gt; i.key === 'totalShotsOnGoal');\n    const shotsInsideBox = shotsGroup.statisticsItems.find(i =&gt; i.key === 'totalShotsInsideBox');\n    const bigChances = attackGroup.statisticsItems.find(i =&gt; i.key === 'bigChanceCreated');\n    console.log('\u0412\u0441\u0435\u0433\u043e \u0443\u0434\u0430\u0440\u043e\u0432:', totalShots.homeValue, totalShots.awayValue);\n    console.log('\u0423\u0434\u0430\u0440\u044b \u0438\u0437 \u0448\u0442\u0440\u0430\u0444\u043d\u043e\u0439:', shotsInsideBox.homeValue, shotsInsideBox.awayValue);\n    console.log('\u0421\u043e\u0437\u0434\u0430\u043d\u043e \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u0448\u0430\u043d\u0441\u043e\u0432:', bigChances.homeValue, bigChances.awayValue);\n  });\n<\/pre>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-2\">What data about forwards can be obtained through the sports statistics API<\/h2>\n<p>Through the Sport Events API, which underlies the service <a href=\"http:\/\/api-sport.pro\/es\/\">API de eventos deportivos<\/a>, You receive both profile data about forwards and their game statistics. The endpoint \/v2\/football\/players returns a list of players with key information: name, country, date of birth, height, preferred foot, position (including F for forwards), number, contract duration, and estimated market value (proposedMarketValueEUR). This data allows you to segment players by age, anthropometry, and style, selecting the necessary profiles for the club\u2019s game model or scouting tasks.<\/p>\n<p>For detailed analysis of forwards\u2019 performances, you combine player data with information about teams and matches. The endpoint \/v2\/football\/teams returns the team lineup, where each player has a statistics object with parameters for a specific match: minutes on the field, goals, assists, shots, shots on target, key passes, dribbles, duels, and other metrics. In conjunction with match data from \/v2\/football\/matches and \/v2\/football\/matches\/{matchId}, you get the full context: against which opponent the forward played, in which tournament, and under what score he converted chances.<\/p>\n<p>Thanks to this API, you can build custom data showcases for forwards: a top scorers ranking considering xG, lists of undervalued forwards with a high number of touches in the penalty area and shots on goal, and player selection for a specific championship by age and style. All data is provided in a convenient JSON format and easily integrates into the club\u2019s internal CRM, analytical dashboards, or odds modeling systems. The connection takes just a few minutes, and the access key can be obtained in <a href=\"https:\/\/app.api-sport.ru\">the API personal account.<\/a>.<\/p>\n<pre class=\"brush: jscript; title: ; notranslate\" title=\"\" data-no-translation=\"\">\n\/\/ \u041f\u0440\u0438\u043c\u0435\u0440: \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u0432\u0441\u0435\u0445 \u0438\u0433\u0440\u043e\u043a\u043e\u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u0438 \u0444\u0438\u043b\u044c\u0442\u0440\u0443\u0435\u043c \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0438\u0445\nconst TEAM_ID = 195801;\nfetch(`https:\/\/api.api-sport.ru\/v2\/football\/players?team_id=${TEAM_ID}`, {\n  headers: {\n    'Authorization': '\u0412\u0410\u0428_API_\u041a\u041b\u042e\u0427'\n  }\n})\n  .then(r =&gt; r.json())\n  .then(data =&gt; {\n    const forwards = data.players.filter(p =&gt; p.position === 'F');\n    forwards.forEach(player =&gt; {\n      console.log(`${player.name} (${player.country.name})`);\n      console.log('\u0412\u043e\u0437\u0440\u0430\u0441\u0442 (timestamp):', player.dateOfBirthTimestamp);\n      console.log('\u0420\u0430\u0431\u043e\u0447\u0430\u044f \u043d\u043e\u0433\u0430:', player.preferredFoot);\n      console.log('\u0420\u044b\u043d\u043e\u0447\u043d\u0430\u044f \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c, \u20ac:', player.proposedMarketValueEUR);\n      console.log('-------');\n    });\n  });\n<\/pre>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-3\">How to calculate xG, xA, and other goal metrics for a forward through the API<\/h2>\n<p>Expected goals (xG) and expected assists (xA) metrics allow you to evaluate not only the fact of scoring a goal but also the quality of created chances. In the classical approach, xG is calculated as the probability that a specific shot will become a goal, based on the characteristics of the episode: distance to the goal, angle, type of shot, situation (set piece, open play), pressure from defenders, etc. xA values reflect the probability that a pass will lead to a shot with a certain xG. These approaches are widely recognized in professional football analytics and are actively used by clubs and betting companies.<\/p>\n<p>The Sport Events API does not impose a specific xG\/xA model but provides raw data for its construction. From the liveEvents objects (endpoint \/v2\/football\/matches\/{matchId}\/events), you get a sequence of events: goals, penalties, substitutions, and other key moments, to which you can link the shooter and the assister. Additionally, match statistics from matchStatistics provide aggregated features on shots, \u00abbig chances,\u00bb touches in the penalty area, and attack structure. On this basis, you train your own xG\/xA model or use ready-made empirical coefficients (for example, typical studies show that an average shot from the penalty area has a higher expected xG than a long-range shot; the exact value depends on your model and sample).<\/p>\n<p>In practice, it often starts with a simplified assessment: assigning average xG weights to shots from different zones, while for xA, a combination of assists and created \u00abbig chances\u00bb is used. With the Sport Events API, you can save the context of the match, opponent, tournament, and bookmaker odds dynamics (oddsBase field) for each shot by the forward. This allows you to compare the actual and expected performance of the forward with market expectations and find overvalued or undervalued players for betting, fantasy, or transfer analytics.<\/p>\n<pre class=\"brush: jscript; title: ; notranslate\" title=\"\" data-no-translation=\"\">\n\/\/ \u0423\u043f\u0440\u043e\u0449\u0451\u043d\u043d\u044b\u0439 \u043f\u0440\u0438\u043c\u0435\u0440 \u0440\u0430\u0441\u0447\u0451\u0442\u0430 xG \u043f\u043e \u0430\u0433\u0440\u0435\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u043c \u0434\u0430\u043d\u043d\u044b\u043c \u043a\u043e\u043c\u0430\u043d\u0434\u044b\n\/\/ \u0412 \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u043c \u043f\u0440\u043e\u0435\u043a\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c xG \u043e\u0431\u0443\u0447\u0430\u044e\u0442 \u043d\u0430 \u043c\u0430\u0441\u0441\u0438\u0432\u0435 \u044d\u043f\u0438\u0437\u043e\u0434\u043e\u0432, \u0437\u0434\u0435\u0441\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0430\u0446\u0438\u044f \u0438\u0434\u0435\u0438\nfunction estimateTeamXG(shotsInsideBox, shotsOutsideBox) {\n  const xGInside = 0.15;   \/\/ \u043f\u0440\u0438\u043c\u0435\u0440 \u0443\u0441\u0440\u0435\u0434\u043d\u0451\u043d\u043d\u043e\u0433\u043e \u0432\u0435\u0441\u0430, \u0437\u0430\u0432\u0438\u0441\u0438\u0442 \u043e\u0442 \u0432\u0430\u0448\u0435\u0439 \u043c\u043e\u0434\u0435\u043b\u0438\n  const xGOutside = 0.04;  \/\/ \u043f\u0440\u0438\u043c\u0435\u0440 \u0443\u0441\u0440\u0435\u0434\u043d\u0451\u043d\u043d\u043e\u0433\u043e \u0432\u0435\u0441\u0430, \u0437\u0430\u0432\u0438\u0441\u0438\u0442 \u043e\u0442 \u0432\u0430\u0448\u0435\u0439 \u043c\u043e\u0434\u0435\u043b\u0438\n  return shotsInsideBox * xGInside + shotsOutsideBox * xGOutside;\n}\nfetch('https:\/\/api.api-sport.ru\/v2\/football\/matches\/14570728', {\n  headers: { 'Authorization': '\u0412\u0410\u0428_API_\u041a\u041b\u042e\u0427' }\n})\n  .then(r =&gt; r.json())\n  .then(match =&gt; {\n    const stats = match.matchStatistics.find(s =&gt; s.period === 'ALL');\n    const shotsGroup = stats.groups.find(g =&gt; g.groupName === 'Shots');\n    const insideItem = shotsGroup.statisticsItems.find(i =&gt; i.key === 'totalShotsInsideBox');\n    const outsideItem = shotsGroup.statisticsItems.find(i =&gt; i.key === 'totalShotsOutsideBox');\n    const homeXG = estimateTeamXG(insideItem.homeValue, outsideItem.homeValue);\n    const awayXG = estimateTeamXG(insideItem.awayValue, outsideItem.awayValue);\n    console.log('\u041e\u0446\u0435\u043d\u043e\u0447\u043d\u044b\u0439 xG \u0445\u043e\u0437\u044f\u0435\u0432:', homeXG.toFixed(2));\n    console.log('\u041e\u0446\u0435\u043d\u043e\u0447\u043d\u044b\u0439 xG \u0433\u043e\u0441\u0442\u0435\u0439:', awayXG.toFixed(2));\n  });\n<\/pre>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-4\">How to analyze a forward\u2019s shots, positions, and actions on the field using the API<\/h2>\n<p>A deep analysis of a forward\u2019s game begins with analyzing his actions during the match: shots, movements, participation in possession phases, and pressing. Through the endpoint \/v2\/football\/matches\/{matchId}\/events, you get a chronology of key events: goals, penalties, substitutions, cards, added time fixation, and other moments. By linking the player ID (player.id) from the events with player objects in the lineup (lineup.players), you can reconstruct the forward\u2019s participation map in important episodes and assess his impact on the team\u2019s game.<\/p>\n<p>Aggregated match statistics help to complement the picture with qualitative indicators. In the matchStatistics object, there are groups Shots and Attack, which contain data on total shots, shots on target, shots from the penalty area and outside it, \u00abbig chances,\u00bb touches in the penalty area (touchesInOppBox), offsides, duels won, and successful dribbles. By comparing them with the minutes the forward spent on the field, you calculate shots and touches in the penalty area per 90 minutes, loss and offside frequency, as well as dribbling efficiency. These data can then be easily visualized in the form of graphs, heat maps, and dashboards for the coaching staff or analytical department.<\/p>\n<p>In the future, the emergence of WebSocket connection in <a href=\"http:\/\/api-sport.pro\/es\/\">API de Eventos Deportivos<\/a> will allow translating such analysis in real time: receiving events immediately during the match, automatically updating the shot map, as well as embedding proprietary AI models on top of the data to recognize attacking movement patterns and predict moments. Already, REST endpoints provide everything necessary for building complex tracking systems and post-match analytics that summarize data across dozens of matches and seasons for each attacker.<\/p>\n<pre class=\"brush: jscript; title: ; notranslate\" title=\"\" data-no-translation=\"\">\n\/\/ \u041f\u0440\u0438\u043c\u0435\u0440: \u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c \u0441\u043e\u0431\u044b\u0442\u0438\u044f \u043c\u0430\u0442\u0447\u0430 \u0438 \u043e\u0442\u0431\u0438\u0440\u0430\u0435\u043c \u0433\u043e\u043b\u044b \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u0433\u043e \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0435\u0433\u043e\nconst MATCH_ID = 14570728;\nconst FORWARD_ID = 123456; \/\/ ID \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0435\u0433\u043e \u0438\u0437 \u0441\u043f\u0438\u0441\u043a\u0430 \u0438\u0433\u0440\u043e\u043a\u043e\u0432\nfetch(`https:\/\/api.api-sport.ru\/v2\/football\/matches\/${MATCH_ID}\/events`, {\n  headers: { 'Authorization': '\u0412\u0410\u0428_API_\u041a\u041b\u042e\u0427' }\n})\n  .then(r =&gt; r.json())\n  .then(data =&gt; {\n    const goals = data.events.filter(e =&gt; e.type === 'goal' &amp;&amp; e.player &amp;&amp; e.player.id === FORWARD_ID);\n    goals.forEach(goal =&gt; {\n      console.log(`\u0413\u043e\u043b \u043d\u0430 ${goal.time}-\u0439 \u043c\u0438\u043d\u0443\u0442\u0435, \u0441\u0447\u0451\u0442 ${goal.homeScore}:${goal.awayScore}`);\n      console.log('\u041a\u043e\u043c\u0430\u043d\u0434\u0430:', goal.team === 'home' ? '\u0425\u043e\u0437\u044f\u0435\u0432\u0430' : '\u0413\u043e\u0441\u0442\u0438');\n    });\n  });\n<\/pre>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-5\">What advanced metrics for forwards are available in popular sports event APIs<\/h2>\n<p>In addition to basic statistics of goals and shots, professional sports event APIs offer a wide range of advanced metrics that are particularly important for evaluating attackers. These include touches in the penalty area, the share of shots from dangerous zones, participation in the final and penultimate phases of attack, successful dribbles in the final third, the share of duels won, as well as the engagement rate in progressive attacks. Within the Sport Events API, some of these metrics are already selected directly from matchStatistics: touchesInOppBox, finalThirdEntries, accurateCross, dribblesPercentage, duelWonPercent, and other indicators reflecting the quality and volume of actions in the attacking phase.<\/p>\n<p>Based on these, it is easy to build composite indices. For example, the attack participation index can take into account touches in the penalty area, dribbles, entries into the final third (finalThirdEntries), and passing accuracy in the attacking zone. The penalty area assertiveness index combines shots from the penalty area, the percentage of won aerial and physical duels, and the number of fouls earned in the final third (fouledFinalThird). Such composite metrics allow identifying players who may not score much yet but systematically get into sharp positions and create threats \u2014 this is an important signal for scouting and betting on long-term markets.<\/p>\n<p>Usando <a href=\"http:\/\/api-sport.pro\/es\/\">API de Eventos Deportivos<\/a>, you can aggregate advanced metrics by seasons, tournaments, and specific segments (for example, the last 10 rounds), build cohorts of attackers with similar profiles, and compare their impact on the game with market expectations through oddsBase data. Support for different sports (football, hockey, basketball, tennis, esports, and others) allows replicating the approach to advanced metrics in other disciplines where the performance of attacking players or lines is important. All of this forms a powerful foundation for club analytics, betting, fantasy platforms, and media projects.<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\" data-no-translation=\"\">\n# \u041f\u0440\u0438\u043c\u0435\u0440: \u0430\u0433\u0440\u0435\u0433\u0438\u0440\u0443\u0435\u043c \u0447\u0430\u0441\u0442\u044c \u043f\u0440\u043e\u0434\u0432\u0438\u043d\u0443\u0442\u044b\u0445 \u043c\u0435\u0442\u0440\u0438\u043a \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u043f\u043e \u0441\u043f\u0438\u0441\u043a\u0443 \u043c\u0430\u0442\u0447\u0435\u0439\nimport requests\nAPI_KEY = '\u0412\u0410\u0428_API_\u041a\u041b\u042e\u0427'\nSPORT = 'football'\nmatch_ids = &#x5B;14570728, 14586240]\nheaders = {'Authorization': API_KEY}\ntouches_total = 0\nfinal_third_entries = 0\nmatches_count = 0\nfor mid in match_ids:\n    r = requests.get(f'https:\/\/api.api-sport.ru\/v2\/{SPORT}\/matches\/{mid}', headers=headers)\n    match = r.json()\n    stats_all = next(s for s in match&#x5B;'matchStatistics'] if s&#x5B;'period'] == 'ALL')\n    attack_group = next(g for g in stats_all&#x5B;'groups'] if g&#x5B;'groupName'] == 'Attack')\n    passes_group = next(g for g in stats_all&#x5B;'groups'] if g&#x5B;'groupName'] == 'Passes')\n    touches_item = next(i for i in attack_group&#x5B;'statisticsItems'] if i&#x5B;'key'] == 'touchesInOppBox')\n    final_third_item = next(i for i in passes_group&#x5B;'statisticsItems'] if i&#x5B;'key'] == 'finalThirdEntries')\n    # \u0414\u043b\u044f \u043f\u0440\u0438\u043c\u0435\u0440\u0430 \u0441\u0447\u0438\u0442\u0430\u0435\u043c \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u0445\u043e\u0437\u044f\u0435\u0432\n    touches_total += touches_item&#x5B;'homeValue']\n    final_third_entries += final_third_item&#x5B;'homeValue']\n    matches_count += 1\nprint('\u0421\u0440\u0435\u0434\u043d\u0438\u0435 \u043a\u0430\u0441\u0430\u043d\u0438\u044f \u0432 \u0448\u0442\u0440\u0430\u0444\u043d\u043e\u0439 \u0437\u0430 \u043c\u0430\u0442\u0447:', touches_total \/ matches_count)\nprint('\u0421\u0440\u0435\u0434\u043d\u0438\u0435 \u0432\u0445\u043e\u0434\u044b \u0432 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u044e\u044e \u0442\u0440\u0435\u0442\u044c \u0437\u0430 \u043c\u0430\u0442\u0447:', final_third_entries \/ matches_count)\n<\/pre>\n<\/div>\n<div class=\"universal_article\">\n<h2 id=\"contents-6\">How to automate the calculation of forward metrics based on API data<\/h2>\n<p>For attacking metrics to provide real benefits, they need to be calculated regularly and without manual labor. A typical pipeline based on the Sport Events API looks like this: on a schedule, the service polls the \/v2\/football\/matches endpoints with filters by tournament, season, or team, retrieves a fresh list of matches, then requests details for each match (\/v2\/football\/matches\/{matchId}) and events (\/v2\/football\/matches\/{matchId}\/events). The data is then saved to an internal database, where derived metrics are calculated \u2014 goals and shots per 90 minutes, xG\/xA according to your models, attack and pressing participation indices, forward ratings by club or league.<\/p>\n<p>Such architecture is easily scalable: you can parallelize requests by competitions, store different slices (by seasons, tournaments, player roles), and update data warehouses for different teams of analysts, coaches, and traders. In the near future, the introduction of a WebSocket stream in the API will simplify working with live data: instead of frequent polling, the server will push updates on match events, and you will be able to build online models of attacking effectiveness, track the dynamics of their xG and engagement right during the game. Additional AI tools will allow automating the search for anomalies and new patterns in forwards\u2019 play.<\/p>\n<p>To get started, it is enough to obtain an API key at <a href=\"https:\/\/app.api-sport.ru\">tu cuenta personal en api-sport.ru<\/a>, set up authorization via the Authorization header, and integrate calls into your service or analytics scripts. Below is a minimal example in Python that pulls matches for a team once per run, calculates simple metrics for shots, and can be expanded into a full ETL pipeline with the calculation of complex attacking metrics, storage, and dashboard creation in BI systems.<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\" data-no-translation=\"\">\n# \u041f\u0440\u0438\u043c\u0435\u0440: \u0443\u043f\u0440\u043e\u0449\u0451\u043d\u043d\u044b\u0439 \u0441\u043a\u0440\u0438\u043f\u0442 \u0434\u043b\u044f \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f \u043c\u0435\u0442\u0440\u0438\u043a \u043f\u043e \u043c\u0430\u0442\u0447\u0430\u043c \u043a\u043e\u043c\u0430\u043d\u0434\u044b\nimport requests\nAPI_KEY = '\u0412\u0410\u0428_API_\u041a\u041b\u042e\u0427'\nTEAM_ID = 195801\nSPORT = 'football'\nheaders = {'Authorization': API_KEY}\n# 1. \u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043c\u0430\u0442\u0447\u0438 \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u043d\u0430 \u0441\u0435\u0433\u043e\u0434\u043d\u044f\nparams = {'team_id': TEAM_ID}\nresp = requests.get(f'https:\/\/api.api-sport.ru\/v2\/{SPORT}\/matches', headers=headers, params=params)\nmatches = resp.json()&#x5B;'matches']\nfor m in matches:\n    match_id = m&#x5B;'id']\n    detail = requests.get(f'https:\/\/api.api-sport.ru\/v2\/{SPORT}\/matches\/{match_id}', headers=headers).json()\n    # \u043f\u0440\u0438\u043c\u0435\u0440: \u0434\u043e\u0441\u0442\u0430\u0451\u043c \u043e\u0431\u0449\u0435\u0435 \u0447\u0438\u0441\u043b\u043e \u0443\u0434\u0430\u0440\u043e\u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u0445\u043e\u0437\u044f\u0435\u0432\n    stats_all = next(s for s in detail&#x5B;'matchStatistics'] if s&#x5B;'period'] == 'ALL')\n    shots_group = next(g for g in stats_all&#x5B;'groups'] if g&#x5B;'groupName'] == 'Shots')\n    total_shots = next(i for i in shots_group&#x5B;'statisticsItems'] if i&#x5B;'key'] == 'totalShotsOnGoal')\n    print(f&quot;\u041c\u0430\u0442\u0447 {match_id}: \u0443\u0434\u0430\u0440\u044b \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u0445\u043e\u0437\u044f\u0435\u0432 = {total_shots&#x5B;'homeValue']}&quot;)\n    # \u0437\u0434\u0435\u0441\u044c \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0438\u0442\u044c: \u043f\u0440\u0438\u0432\u044f\u0437\u0430\u0442\u044c \u043a \u0438\u0433\u0440\u043e\u043a\u0430\u043c, \u043f\u0435\u0440\u0435\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u0437\u0430 90 \u043c\u0438\u043d\u0443\u0442 \u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0432 \u0411\u0414\n<\/pre>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Content What metrics are important for evaluating a forward&#8217;s performance in football What data about forwards can be obtained through sports statistics API How to calculate xG, xA and other goal metrics of a forward through API How to analyze shots, positions, and actions of a forward on the field using API What advanced metrics for forwards are available in popular sports event APIs How to automate [\u2026]<\/p>","protected":false},"author":1,"featured_media":1343,"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\":\"\u041a\u0430\u043a\u0438\u0435 \u0431\u0430\u0437\u043e\u0432\u044b\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0438 \u0432\u0430\u0436\u043d\u044b \u0434\u043b\u044f \u043e\u0446\u0435\u043d\u043a\u0438 \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0435\u0433\u043e?\",\"answer\":\"\u0412 \u043f\u0435\u0440\u0432\u0443\u044e \u043e\u0447\u0435\u0440\u0435\u0434\u044c \u0441\u043c\u043e\u0442\u0440\u044f\u0442 \u043d\u0430 \u0433\u043e\u043b\u044b, \u0433\u043e\u043b\u0435\u0432\u044b\u0435 \u043f\u0435\u0440\u0435\u0434\u0430\u0447\u0438, \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0438 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0443\u0434\u0430\u0440\u043e\u0432, \u0434\u043e\u043b\u044e \u0433\u043e\u043b\u043e\u0432 \u0431\u0435\u0437 \u043f\u0435\u043d\u0430\u043b\u044c\u0442\u0438, \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u043c\u0435\u043d\u0442\u043e\u0432. \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u044e\u0442 \u043a\u0430\u0441\u0430\u043d\u0438\u044f \u0432 \u0448\u0442\u0440\u0430\u0444\u043d\u043e\u0439, \u0443\u0447\u0430\u0441\u0442\u0438\u0435 \u0432 \u0430\u0442\u0430\u043a\u0430\u0445, \u043e\u0431\u0432\u043e\u0434\u043a\u0438 \u0438 \u0432\u044b\u0438\u0433\u0440\u0430\u043d\u043d\u044b\u0435 \u0435\u0434\u0438\u043d\u043e\u0431\u043e\u0440\u0441\u0442\u0432\u0430, \u0447\u0442\u043e\u0431\u044b \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442, \u043d\u043e \u0438 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u0438\u0433\u0440\u044b \u0444\u043e\u0440\u0432\u0430\u0440\u0434\u0430.\"},{\"question\":\"\u041c\u043e\u0436\u043d\u043e \u043b\u0438 \u043f\u043e\u043b\u0443\u0447\u0430\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0451\u0442\u0430 xG \u0438 xA \u0447\u0435\u0440\u0435\u0437 Sport Events API?\",\"answer\":\"\u0413\u043e\u0442\u043e\u0432\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f xG \u0438 xA API \u043d\u0435 \u043d\u0430\u0432\u044f\u0437\u044b\u0432\u0430\u0435\u0442, \u043d\u043e \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435: \u0441\u043e\u0431\u044b\u0442\u0438\u044f \u043c\u0430\u0442\u0447\u0430 (\u0433\u043e\u043b\u044b, \u0443\u0434\u0430\u0440\u044b, \u043f\u0435\u0440\u0435\u0434\u0430\u0447\u0438) \u0438 \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u0443\u044e \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0443 \u043f\u043e \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0435 \u0430\u0442\u0430\u043a. \u041d\u0430 \u044d\u0442\u043e\u0439 \u0431\u0430\u0437\u0435 \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u043e\u0431\u0443\u0447\u0438\u0442\u044c \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u0443\u044e \u043c\u043e\u0434\u0435\u043b\u044c xG\/xA \u0438\u043b\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0443\u043f\u0440\u043e\u0449\u0451\u043d\u043d\u044b\u0435 \u044d\u043c\u043f\u0438\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u043f\u043e\u0434\u0445\u043e\u0434\u044b, \u0441\u043e\u043f\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044f \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u043c\u043e\u043c\u0435\u043d\u0442\u043e\u0432 \u0441 \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u043c\u0438 \u0433\u043e\u043b\u0430\u043c\u0438 \u0438 \u0430\u0441\u0441\u0438\u0441\u0442\u0430\u043c\u0438.\"},{\"question\":\"\u0415\u0441\u0442\u044c \u043b\u0438 \u0432 API \u0434\u0430\u043d\u043d\u044b\u0435 \u0438\u043c\u0435\u043d\u043d\u043e \u043f\u043e \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0438\u043c, \u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043f\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u0430\u043c?\",\"answer\":\"\u0414\u0430, \u044d\u043d\u0434\u043f\u043e\u0438\u043d\u0442\u044b \/players \u0438 \/teams \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u044e\u0442 \u043f\u0440\u043e\u0444\u0438\u043b\u044c\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e\u0431 \u0438\u0433\u0440\u043e\u043a\u0430\u0445, \u0438\u0445 \u043f\u043e\u0437\u0438\u0446\u0438\u044f\u0445 \u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u0435 \u043a\u043e\u043c\u0430\u043d\u0434. \u0412 \u043e\u0431\u044a\u0435\u043a\u0442\u0430\u0445 \u0441\u043e\u0441\u0442\u0430\u0432\u0430 (lineup.players.statistics) \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442\u0441\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430 \u043f\u043e \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u043c\u0443 \u043c\u0430\u0442\u0447\u0443, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u043c\u043e\u0436\u043d\u043e \u0430\u0433\u0440\u0435\u0433\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u043e \u0441\u0435\u0437\u043e\u043d\u0430\u043c \u0438 \u0442\u0443\u0440\u043d\u0438\u0440\u0430\u043c \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0435\u0433\u043e.\"},{\"question\":\"\u041a\u0430\u043a \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u043c\u0435\u0442\u0440\u0438\u043a \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0438\u0445?\",\"answer\":\"\u041e\u0431\u044b\u0447\u043d\u043e \u043d\u0430\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u044e\u0442 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u044b\u0439 \u0441\u043a\u0440\u0438\u043f\u0442 \u0438\u043b\u0438 \u0441\u0435\u0440\u0432\u0438\u0441, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e \u0440\u0430\u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044e \u043e\u0431\u0440\u0430\u0449\u0430\u0435\u0442\u0441\u044f \u043a \u044d\u043d\u0434\u043f\u043e\u0438\u043d\u0442\u0430\u043c \/matches, \/matches\/{matchId} \u0438 \/matches\/{matchId}\/events, \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442 \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 \u0445\u0440\u0430\u043d\u0438\u043b\u0438\u0449\u0435 \u0438 \u043f\u0435\u0440\u0435\u0441\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u0442 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u043d\u044b\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0438 (\u0433\u043e\u043b\u044b \u0437\u0430 90 \u043c\u0438\u043d\u0443\u0442, xG\/xA, \u0438\u043d\u0434\u0435\u043a\u0441\u044b \u0443\u0447\u0430\u0441\u0442\u0438\u044f \u0432 \u0430\u0442\u0430\u043a\u0430\u0445). \u0414\u043b\u044f \u0430\u0432\u0442\u043e\u0440\u0438\u0437\u0430\u0446\u0438\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f API-\u043a\u043b\u044e\u0447 \u0438\u0437 \u043b\u0438\u0447\u043d\u043e\u0433\u043e \u043a\u0430\u0431\u0438\u043d\u0435\u0442\u0430 api-sport.ru.\"},{\"question\":\"\u041c\u043e\u0436\u043d\u043e \u043b\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 Sport Events API \u0434\u043b\u044f \u0431\u0435\u0442\u0442\u0438\u043d\u0433\u043e\u0432\u043e\u0439 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0438 \u043f\u043e \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0438\u043c?\",\"answer\":\"\u0414\u0430, \u0432 \u043e\u0442\u0432\u0435\u0442\u0430\u0445 \u043f\u043e \u043c\u0430\u0442\u0447\u0430\u043c \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b \u043a\u0430\u043a \u0441\u043f\u043e\u0440\u0442\u0438\u0432\u043d\u044b\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0438, \u0442\u0430\u043a \u0438 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u044b \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u043e\u0432 \u0432 \u0431\u043b\u043e\u043a\u0435 oddsBase. \u042d\u0442\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0441\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0442\u044c \u0440\u0435\u0430\u043b\u044c\u043d\u0443\u044e \u0438 \u043e\u0436\u0438\u0434\u0430\u0435\u043c\u0443\u044e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c \u043d\u0430\u043f\u0430\u0434\u0430\u044e\u0449\u0438\u0445 \u0441 \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u043c\u0438 \u043e\u0436\u0438\u0434\u0430\u043d\u0438\u044f\u043c\u0438, \u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u0440\u044b\u043d\u043a\u043e\u0432 \u0433\u043e\u043b\u043e\u0432, \u0443\u0434\u0430\u0440\u043e\u0432 \u0438 \u0438\u043d\u0434\u0438\u0432\u0438\u0434\u0443\u0430\u043b\u044c\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439 \u0438\u0433\u0440\u043e\u043a\u043e\u0432.\"}]","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1344","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"<title>Metrics of forwards in football through API \u2014 analytics with api-sport.ru<\/title>\n<meta name=\"description\" content=\"Analysis of key metrics for attackers and examples of their calculation based on data from the Sport Events API. 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