Experience top-notch Champions League predictions with our Football Prediction API. Developed and continually improving since 2017, our state-of-the-art model offers accurate predictions for the most popular leagues and prediction markets. Available supplementary to our Football API.
Welcome to a user-friendly guide on how football predictions work, specifically for the Champions League! If you’re new to this, don’t worry – we’ll explain everything in simple terms and use some recent examples to make it clear.
How Do We Make Predictions?
Our Football Predictions API uses machine learning techniques and models that include loads of historical data to predict the outcomes of Champions League matches. We look at a lot of factors, like how well the teams have been playing recently, any injuries to key players, and how the teams have performed against each other in the past. We also incorporate the player contribution model, which further enhances the accuracy of the predictions.
Example: Real Madrid vs. Manchester City
Let’s say Real Madrid is playing against Manchester City. Here’s how we might predict the outcome:
Our predictions are available 21 days before the match and are updated daily to reflect the latest information.
Why Use Our Predictions?
Our predictions give you a comprehensive view of the game, helping you make informed decisions whether you’re placing bets, creating fantasy football teams, or want to know what might happen in the next big match. We’re constantly improving our model, adding new features to ensure you have access to the most accurate and up-to-date predictions possible. With our Football Predictions API, you’ll have access to a vast range of markets, including match-winner, double chance, total goals, and more.
Want To Know More?
Interested in a deeper dive into how our predictions work? Read further! It covers all the technical aspects and explains how we keep refining our model to give you the best possible insights.
We hope this guide helps you understand how football predictions work for the Champions League. Enjoy the matches, and happy predicting!
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"code": "over-under-1_5-probability",
"developer_name": "OVER_UNDER_1_5_PROBABILITY",
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},
{
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"type_id": 233,
"type": {
"id": 233,
"name": "First Half Winner Probability",
"code": "first-half-winner",
"developer_name": "FIRST_HALF_WINNER_PROBABILITY",
"model_type": "prediction",
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}
},
{
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"type_id": 238,
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"name": "Team To Score First Probability",
"code": "team_to_score_first-probability",
"developer_name": "TEAM_TO_SCORE_FIRST_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
},
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},
"type_id": 239,
"type": {
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"name": "Double Chance Probability",
"code": "double_chance-probability",
"developer_name": "DOUBLE_CHANCE_PROBABILITY",
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],
For this how-to guide, we will use the ‘Probabilities by Fixture ID’ endpoint. We’ll look at the final of the previous Champions League edition between Borussia Dortmund and Real Madrid, played in the 2023/2024 season of the Champions League.
First, some general information about the Champions League
Based on the information, your request will be:
https://api.sportmonks.com/v3/football/predictions/probabilities/fixtures/19101794?api_token=YOUR_TOKEN
As mentioned before, you have multiple options for retrieving predictions, including the alternative option to use the fixtures between dates or fixture by ID endpoint with predictions as include.
For example:
https://api.sportmonks.com/v3/football/fixtures/19101794?api_token=YOUR_TOKEN&include=predictions
Now that you’ve learned how to request predictions, let’s discuss the response in the next chapter.
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"1-3": 5.47,
"2-0": 4.37,
"2-1": 6.47,
"2-2": 6.58,
"2-3": 4.02,
"3-0": 1.99,
"3-1": 3.5,
"3-2": 2.82,
"3-3": 2.2,
"Other_1": 6.08,
"Other_2": 10.81,
"Other_X": 0.28
}
},
"type_id": 240,
"type": {
"id": 240,
"name": "Correct Score Probability",
"code": "correct-score-probability",
"developer_name": "CORRECT_SCORE_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
},
{
"id": 13537693,
"fixture_id": 19101794,
"predictions": {
"yes": 42.95,
"no": 57.02
},
"type_id": 236,
"type": {
"id": 236,
"name": "Over/Under 3.5 Probability",
"code": "over-under-3_5_probability",
"developer_name": "OVER_UNDER_3_5_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
},
{
"id": 13537698,
"fixture_id": 19101794,
"predictions": {
"yes": 63.54,
"no": 36.46
},
"type_id": 235,
"type": {
"id": 235,
"name": "Over/Under 2.5 Probability",
"code": "over-under-2_5-probability",
"developer_name": "OVER_UNDER_2_5_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
},
{
"id": 13537699,
"fixture_id": 19101794,
"predictions": {
"yes": 83.59,
"no": 16.41
},
"type_id": 234,
"type": {
"id": 234,
"name": "Over/Under 1.5 Probability",
"code": "over-under-1_5-probability",
"developer_name": "OVER_UNDER_1_5_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
},
{
"id": 13537707,
"fixture_id": 19101794,
"predictions": {
"home": 29.87,
"away": 32.56,
"draw": 37.58
},
"type_id": 233,
"type": {
"id": 233,
"name": "First Half Winner Probability",
"code": "first-half-winner",
"developer_name": "FIRST_HALF_WINNER_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
},
{
"id": 13537708,
"fixture_id": 19101794,
"predictions": {
"home": 43.06,
"away": 53.14,
"draw": 3.8
},
"type_id": 238,
"type": {
"id": 238,
"name": "Team To Score First Probability",
"code": "team_to_score_first-probability",
"developer_name": "TEAM_TO_SCORE_FIRST_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
},
{
"id": 13537709,
"fixture_id": 19101794,
"predictions": {
"draw_home": 53.230000000000004,
"draw_away": 69.72,
"home_away": 76.99000000000001
},
"type_id": 239,
"type": {
"id": 239,
"name": "Double Chance Probability",
"code": "double_chance-probability",
"developer_name": "DOUBLE_CHANCE_PROBABILITY",
"model_type": "prediction",
"stat_group": null
}
}
],Fulltime Result Probability (type id: 237) = probability of 30,25% that the home team (Borussia Dortmund) will win. 46,74% probability that the away team (Real Madrid) will win and a 22,98% probability that the match will end in a draw.
Team To Score First Probability (type id: 238) = 43,06% probability Dortmund scores the first goal and 53,14% probability Real Madrid scores the first goal.
Over/Under 1.5 Probability (type id: 234) = 83,59% probability that there will be more than 1.5 goals.
Over/Under 2.5 Probability (type id: 235) = 63,54% probability that there will be more than 2.5 goals.
Over/Under 3.5 Probability (type id: 236) = 42,95 % probability of more than 3.5 goals, 57,02% probability of less than 3.5 goals.
Correct Score Probability (type id: 240) = 10,31% probability of a final score of 1-1.
Away Over/Under 2.5 Probability (type id: 328) = 26,77% probability that the away team will score more than 2.5 goals, 73,23% probability that the away team will score less than 2.5 goals.
Home Over/Under 1.5 Probability (type id: 331) =38,33% probability that the home team will score more than 1.5 goals, 61,67% probability that the home team will score less than 1,5 goals.
Away Over/Under 0.5 Probability (type id: 333) = 77,92% probability that the away team will score more than 0.5 goals (Real Madrid), 22,08% probability that the away team will score less than 0.5 goals.
Home Over/Under 0.5 Probability (type id: 334) = 68,26% probability that the home team will score more than 0.5 goals (Borussia Dortmund), 31,74% probability that the home team will score less than 0.5 goals.
Use this predictions endpoint to retrieve all probabilities available within your subscription. Whether you’re exploring match outcomes or assessing potential betting options, this endpoint provides valuable insights into the likelihood of various scenarios.
All probabilities are available 21 days before the match starts.
You can make the following API request to retrieve all probabilities available within your subscription: https://api.sportmonks.com/v3/football/predictions/probabilities?api_token=YOUR_TOKEN&include=type.
{
"data": [
{
"id": 1251,
"league_id": 2,
"type_id": 241,
"data": {
"fulltime_result": -1.0493,
"away_over_under_0_5": -0.6277,
"away_over_under_1_5": -0.655,
"both_teams_to_score": -0.6786,
"team_to_score_first": -0.829,
"home_over_under_0_5": -0.3736,
"home_over_under_1_5": -0.6989,
"over_under_1_5": -0.5464,
"over_under_2_5": -0.6887,
"over_under_3_5": -0.6642,
"correct_score": -3.2201,
"ht_ft": -1.9891,
"fulltime_result_1st_half": -1.0724
}
},
{
"id": 1252,
"league_id": 2,
"type_id": 242,
"data": {
"fulltime_result": 0.57,
"away_over_under_0_5": 0.68,
"away_over_under_1_5": 0.63,
"both_teams_to_score": 0.64,
"team_to_score_first": 0.58,
"home_over_under_0_5": 0.88,
"home_over_under_1_5": 0.55,
"over_under_1_5": 0.77,
"over_under_2_5": 0.57,
"over_under_3_5": 0.6,
"correct_score": 0.03,
"ht_ft": 0.32,
"fulltime_result_1st_half": 0.46
}
},
{
"id": 1253,
"league_id": 2,
"type_id": 243,
"data": {
"fulltime_result": "good",
"away_over_under_0_5": "medium",
"away_over_under_1_5": "good",
"both_teams_to_score": "poor",
"team_to_score_first": "medium",
"home_over_under_0_5": "poor",
"home_over_under_1_5": "poor",
"over_under_1_5": "poor",
"over_under_2_5": "poor",
"over_under_3_5": "poor",
"correct_score": "high",
"ht_ft": "poor",
"fulltime_result_1st_half": "poor"
}
},
{
"id": 1254,
"league_id": 2,
"type_id": 244,
"data": {
"fulltime_result": "unchanged",
"away_over_under_0_5": "unchanged",
"away_over_under_1_5": "unchanged",
"both_teams_to_score": "unchanged",
"team_to_score_first": "unchanged",
"home_over_under_0_5": "unchanged",
"home_over_under_1_5": "unchanged",
"over_under_1_5": "down",
"over_under_2_5": "down",
"over_under_3_5": "unchanged",
"correct_score": "unchanged",
"ht_ft": "unchanged",
"fulltime_result_1st_half": "unchanged"
}
},
{
"id": 1255,
"league_id": 2,
"type_id": 245,
"data": {
"fulltime_result": -0.9867,
"away_over_under_0_5": -0.606,
"away_over_under_1_5": -0.6215,
"both_teams_to_score": -0.6694,
"team_to_score_first": -0.7963,
"home_over_under_0_5": -0.4237,
"home_over_under_1_5": -0.6831,
"over_under_1_5": -0.5408,
"over_under_2_5": -0.7045,
"over_under_3_5": -0.667,
"correct_score": -2.7618,
"ht_ft": -1.9601,
"fulltime_result_1st_half": -1.077
}
}
],
Do you want to investigate the performance of our Predictions Model for a specific league? Utilise this prediction endpoint to obtain detailed performance metrics tailored to your requested league ID. We’ve got you covered.
You can make the following API request to retrieve the performance of our Predictions Model for a specific league:
https://api.sportmonks.com/v3/football/predictions/predictability/leagues/{ID}?api_token=YOUR_TOKEN.
For this practical example, we will look at the performance of the Champions League, league ID 2.
First, you will find an overview of the historical log loss, type ID 241. The historical log loss is expressed as a numerical value.
When you scroll down below, you will find data about the model hit ratio, type ID 242, which is also expressed as a numerical value.
Next, you will see type ID 243, which contains all the information you need about the model’s predictability expressed as poor, medium, good or high.
Furthermore, you will also see the model predictive power, type ID 244, which is expressed as unchanged, up or down.
Last but not least, you can also find everything you need to know about the model’s log loss, type ID 245, expressed as a numerical value.
The Value Bet model processes thousands of historical odds data and market trends to find the best value opportunities. In other words, it compares bookmakers’ odds with each other and then gives you the best value bookmaker.
Using our Value Bet model, you can access valuable insights into the best bets, which can help you make better decisions. We continuously improve and test our model by analysing past odds and Value Bets to ensure it works correctly.
Our Value Bet models use bookmakers’ odds to find the best betting option. Our Value Bet models use bookmakers’ odds to find the best betting options for Champions League matches. Whether you’re betting on Real Madrid vs Bayern Munich or Manchester City vs Paris Saint Germain, our model will help you find the best Value Bets, enhancing your Champions League betting strategy.
Another cool feature related to predictions is predictive lineups for upcoming Champions League fixtures. In API 3.0, getting a lineup before the definitive lineup is released is now possible. When you use the “Fixture By ID” endpoint with the lineup included, you will see a lineup way before the actual lineup is released.
By using the include metadata, you can find the lineup_confirmed type. The type will be false if the club has yet to release the lineup. This is when we show a predicted lineup based on previous lineups in games, as well as suspended and injured players.
Once the team/club officially releases their lineup, it will be set to true. Then, the lineup we offer should reflect the final lineup as it is during the game.
Stay ahead of the competition with our constantly improving technology, delivering the latest and most accurate predictions possible. You’ll have plenty of options with an extensive list of markets available. Start winning today!
Learn how to use our Prediction API and find out how to become smarter than the bookmakers. Check it out here and benefit now.
