Premier League Predictions API | Reliable and Accurate Predictions

Understanding Sportmonks’ Predictions for the Premier League

Welcome to a user-friendly guide on how football predictions work, specifically for the Premier 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 Premier 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: Arsenal vs. Liverpool
Let’s say Arsenal is playing against Liverpool. Here’s how we might predict the outcome:

Team Form: We’ll check how each team has performed in recent matches. For instance, if Arsenal has won its last five games and Liverpool has had mixed results, this will influence our prediction.
Player Injuries: If a star player like Bukayo Saka from Arsenal is injured, this could significantly impact the team’s chances of winning.
Head-to-Head Records: We’ll also look at how these teams have played against each other in the past. If Liverpool has a strong record against Arsenal, this could be a good indicator of their chances.
Player Contributions: Our model also considers individual player performance. For example, if Mohamed Salah has been scoring lots of goals lately, this could increase Liverpool’s predicted chance of winning. By analysing individual players’ performance and impact on the team, we can better understand how they’ll contribute to the match’s final outcome. This model takes into account various metrics, such as the player’s recent form, their position, and their contribution to the team’s overall performance.

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 Premier League. Enjoy the matches, and happy predicting!

{
  "data": {
   "id": 18841686,
   "sport_id": 1,
   "league_id": 8,
   "season_id": 21646,
   "stage_id": 77464011,
   "group_id": null,
   "aggregate_id": null,
   "round_id": 307072,
   "state_id": 5,
   "venue_id": 204,
   "name": "Arsenal vs Manchester City",
   "starting_at": "2023-10-08 15:30:00",
   "result_info": "Arsenal won after full-time.",
   "leg": "1/1",
   "details": null,
   "length": 90,
   "placeholder": false,
   "has_odds": true,
   "has_premium_odds": false,
   "starting_at_timestamp": 1696779000,
   "predictions": [
    {
     "id": 8303226,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 42.93,
      "no": 57.07
     },
     "type_id": 332,
     "type": {
      "id": 332,
      "name": "Away Over/Under 1.5 Probability",
      "code": "away-over-under-1_5_probability",
      "developer_name": "AWAY_OVER_UNDER_1_5_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303236,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 73.62,
      "no": 26.38
     },
     "type_id": 333,
     "type": {
      "id": 333,
      "name": "Away Over/Under 0.5 Probability",
      "code": "away-over-under-0_5_probability",
      "developer_name": "AWAY_OVER_UNDER_0_5_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303238,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 73.96,
      "no": 26.04
     },
     "type_id": 334,
     "type": {
      "id": 334,
      "name": "Home Over/Under 0.5 Probability",
      "code": "home-over-under-0_5_probability",
      "developer_name": "HOME_OVER_UNDER_0_5_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303265,
     "fixture_id": 18841686,
     "predictions": {
      "home_home": 22.41,
      "home_away": 2.36,
      "home_draw": 5.8,
      "away_home": 3.02,
      "away_away": 21.01,
      "away_draw": 6.49,
      "draw_draw": 13.1,
      "draw_home": 14.43,
      "draw_away": 11.37
     },
     "type_id": 232,
     "type": {
      "id": 232,
      "name": "Half Time/Full Time Probability",
      "code": "half-time-full-time-probability",
      "developer_name": "HTFT_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303244,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 7.36,
      "no": 92.64
     },
     "type_id": 326,
     "type": {
      "id": 326,
      "name": "Home Over/Under 3.5 Probability",
      "code": "home-over-under-3_5_probability",
      "developer_name": "HOME_OVER_UNDER_3_5_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303231,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 19.98,
      "no": 80.02
     },
     "type_id": 330,
     "type": {
      "id": 330,
      "name": "Home Over/Under 2.5 Probability",
      "code": "home-over-under-2_5_probability",
      "developer_name": "HOME_OVER_UNDER_2_5_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303268,
     "fixture_id": 18841686,
     "predictions": {
      "home": 39.1,
      "away": 37,
      "draw": 23.9
     },
     "type_id": 237,
     "type": {
      "id": 237,
      "name": "Fulltime Result Probability",
      "code": "fulltime-result-probability",
      "developer_name": "FULLTIME_RESULT_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303256,
     "fixture_id": 18841686,
     "predictions": {
      "home": 49.02,
      "away": 46.45,
      "draw": 4.52
     },
     "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": 8303249,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 61.97,
      "no": 38.03
     },
     "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": 8303248,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 40.27,
      "no": 59.73
     },
     "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": 8303230,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 43.92,
      "no": 56.08
     },
     "type_id": 331,
     "type": {
      "id": 331,
      "name": "Home Over/Under 1.5 Probability",
      "code": "home-over-under-1_5_probability",
      "developer_name": "HOME_OVER_UNDER_1_5_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303254,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 81.96,
      "no": 18.04
     },
     "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": 8303267,
     "fixture_id": 18841686,
     "predictions": {
      "yes": 61.85,
      "no": 38.15
     },
     "type_id": 231,
     "type": {
      "id": 231,
      "name": "Both Teams To Score Probability",
      "code": "both-teams-to-score-probability",
      "developer_name": "BTTS_PROBABILITY",
      "model_type": "prediction",
      "stat_group": null
     }
    },
    {
     "id": 8303242,
     "fixture_id": 18841686,
     "predictions": {
      "scores": {
       "0-0": 4.52,
       "0-1": 6.81,
       "0-2": 5.36,
       "0-3": 2.55,
       "1-0": 6.71,
       "1-1": 10.55,
       "1-2": 8.43,
       "1-3": 4.36,
       "2-0": 5.28,
       "2-1": 8.54,
       "2-2": 6.86,
       "2-3": 3.26,
       "3-0": 2.76,
       "3-1": 4.78,
       "3-2": 3.39,
       "3-3": 1.69,
       "Other_1": 7.12,
       "Other_2": 6.8,
       "Other_X": 0.25
      }
     },
     {
      "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
      }
    ]
  },

Practical example: USING THE ‘GET Probabilities by Fixture ID’ ENDPOINT FOR PREMIER LEAGUE PREDICTIONS.

For this how-to guide, we will use the ‘Probabilities by Fixture ID’ endpoint. We’ll look at the first match-up in the Premier League between two title contenders: Arsenal vs Manchester City, played in the 2023/2024 season of the English Premier League.

First, some general information about the English Premier League

League ID: 8
2023/2024 season ID: 21646
Arsenal team ID: 19
Manchester City team ID: 9
Fixture ID: 18841686

Based on the information, your request will be:
https://api.sportmonks.com/v3/football/predictions/probabilities/fixtures/18841686?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/18841686?api_token=YOUR_TOKEN&include=predictions

Now that you’ve learned how to request predictions, let’s discuss the response in the next chapter.

{
  "data": {
    "id": 18841686,
    "sport_id": 1,
    "league_id": 8,
    "season_id": 21646,
    "stage_id": 77464011,
    "group_id": null,
    "aggregate_id": null,
    "round_id": 307072,
    "state_id": 5,
    "venue_id": 204,
    "name": "Arsenal vs Manchester City",
    "starting_at": "2023-10-08 15:30:00",
    "result_info": "Arsenal won after full-time.",
    "leg": "1/1",
    "details": null,
    "length": 90,
    "placeholder": false,
    "has_odds": true,
    "has_premium_odds": false,
    "starting_at_timestamp": 1696779000,
    "predictions": [
      {
        "id": 8303226,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 42.93,
          "no": 57.07
        },
        "type_id": 332,
        "type": {
          "id": 332,
          "name": "Away Over/Under 1.5 Probability",
          "code": "away-over-under-1_5_probability",
          "developer_name": "AWAY_OVER_UNDER_1_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303212,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 30.48,
          "no": 59.31,
          "equal": 10.21
        },
        "type_id": 1688,
        "type": {
          "id": 1688,
          "name": "Corners Over/Under 10 Probability",
          "code": "corners-over-under-10-probability",
          "developer_name": "CORNERS_OVER_UNDER_10_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303236,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 73.62,
          "no": 26.38
        },
        "type_id": 333,
        "type": {
          "id": 333,
          "name": "Away Over/Under 0.5 Probability",
          "code": "away-over-under-0_5_probability",
          "developer_name": "AWAY_OVER_UNDER_0_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303206,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 52.08,
          "no": 36.21,
          "equal": 11.71
        },
        "type_id": 1689,
        "type": {
          "id": 1689,
          "name": "Corners Over/Under 8 Probability",
          "code": "corners-over-under-8-probability",
          "developer_name": "CORNERS_OVER_UNDER_8_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303238,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 73.96,
          "no": 26.04
        },
        "type_id": 334,
        "type": {
          "id": 334,
          "name": "Home Over/Under 0.5 Probability",
          "code": "home-over-under-0_5_probability",
          "developer_name": "HOME_OVER_UNDER_0_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303265,
        "fixture_id": 18841686,
        "predictions": {
          "home_home": 22.41,
          "home_away": 2.36,
          "home_draw": 5.8,
          "away_home": 3.02,
          "away_away": 21.01,
          "away_draw": 6.49,
          "draw_draw": 13.1,
          "draw_home": 14.43,
          "draw_away": 11.37
        },
        "type_id": 232,
        "type": {
          "id": 232,
          "name": "Half Time/Full Time Probability",
          "code": "half-time-full-time-probability",
          "developer_name": "HTFT_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303214,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 21.98,
          "no": 69.52,
          "equal": 8.5
        },
        "type_id": 1684,
        "type": {
          "id": 1684,
          "name": "Corners Over/Under 11 Probability",
          "code": "corners-over-under-11-probability",
          "developer_name": "CORNERS_OVER_UNDER_11_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303244,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 7.36,
          "no": 92.64
        },
        "type_id": 326,
        "type": {
          "id": 326,
          "name": "Home Over/Under 3.5 Probability",
          "code": "home-over-under-3_5_probability",
          "developer_name": "HOME_OVER_UNDER_3_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303231,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 19.98,
          "no": 80.02
        },
        "type_id": 330,
        "type": {
          "id": 330,
          "name": "Home Over/Under 2.5 Probability",
          "code": "home-over-under-2_5_probability",
          "developer_name": "HOME_OVER_UNDER_2_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303200,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 74.76,
          "no": 15.96,
          "equal": 9.28
        },
        "type_id": 1685,
        "type": {
          "id": 1685,
          "name": "Corners Over/Under 6 Probability",
          "code": "corners-over-under-6-probability",
          "developer_name": "CORNERS_OVER_UNDER_6_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303268,
        "fixture_id": 18841686,
        "predictions": {
          "home": 39.1,
          "away": 37,
          "draw": 23.9
        },
        "type_id": 237,
        "type": {
          "id": 237,
          "name": "Fulltime Result Probability",
          "code": "fulltime-result-probability",
          "developer_name": "FULLTIME_RESULT_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303217,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 30.48,
          "no": 69.52,
          "equal": null
        },
        "type_id": 1585,
        "type": {
          "id": 1585,
          "name": "Corners Over/Under 10.5 Probability",
          "code": "corners-over-under-10_5-probability",
          "developer_name": "CORNERS_OVER_UNDER_10_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303240,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 7.05,
          "no": 92.95
        },
        "type_id": 327,
        "type": {
          "id": 327,
          "name": "Away Over/Under 3.5 Probability",
          "code": "away-over-under-3_5_probability",
          "developer_name": "AWAY_OVER_UNDER_3_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303256,
        "fixture_id": 18841686,
        "predictions": {
          "home": 49.02,
          "away": 46.45,
          "draw": 4.52
        },
        "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": 8303249,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 61.97,
          "no": 38.03
        },
        "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": 8303248,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 40.27,
          "no": 59.73
        },
        "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": 8303260,
        "fixture_id": 18841686,
        "predictions": {
          "draw_home": 63,
          "draw_away": 60.9,
          "home_away": 76.1
        },
        "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
        }
      },
      {
        "id": 8303230,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 43.92,
          "no": 56.08
        },
        "type_id": 331,
        "type": {
          "id": 331,
          "name": "Home Over/Under 1.5 Probability",
          "code": "home-over-under-1_5_probability",
          "developer_name": "HOME_OVER_UNDER_1_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303254,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 81.96,
          "no": 18.04
        },
        "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": 8303187,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 22.5,
          "no": 77.5
        },
        "type_id": 1679,
        "type": {
          "id": 1679,
          "name": "Over/Under 4.5 Probability",
          "code": "over-under-4_5-probability",
          "developer_name": "OVER_UNDER_4_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303202,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 63.79,
          "no": 25.24,
          "equal": 10.97
        },
        "type_id": 1686,
        "type": {
          "id": 1686,
          "name": "Corners Over/Under 7 Probability",
          "code": "corners-over-under-7-probability",
          "developer_name": "CORNERS_OVER_UNDER_7_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303221,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 18.91,
          "no": 81.09
        },
        "type_id": 328,
        "type": {
          "id": 328,
          "name": "Away Over/Under 2.5 Probability",
          "code": "away-over-under-2_5_probability",
          "developer_name": "AWAY_OVER_UNDER_2_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303267,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 61.85,
          "no": 38.15
        },
        "type_id": 231,
        "type": {
          "id": 231,
          "name": "Both Teams To Score Probability",
          "code": "both-teams-to-score-probability",
          "developer_name": "BTTS_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303191,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 91.05,
          "no": 4.3,
          "equal": 4.64
        },
        "type_id": 1690,
        "type": {
          "id": 1690,
          "name": "Corners Over/Under 4 Probability",
          "code": "corners-over-under-4-probability",
          "developer_name": "CORNERS_OVER_UNDER_4_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303242,
        "fixture_id": 18841686,
        "predictions": {
          "scores": {
            "0-0": 4.52,
            "0-1": 6.81,
            "0-2": 5.36,
            "0-3": 2.55,
            "1-0": 6.71,
            "1-1": 10.55,
            "1-2": 8.43,
            "1-3": 4.36,
            "2-0": 5.28,
            "2-1": 8.54,
            "2-2": 6.86,
            "2-3": 3.26,
            "3-0": 2.76,
            "3-1": 4.78,
            "3-2": 3.39,
            "3-3": 1.69,
            "Other_1": 7.12,
            "Other_2": 6.8,
            "Other_X": 0.25
          }
        },
        "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": 8303252,
        "fixture_id": 18841686,
        "predictions": {
          "home": 29.88,
          "away": 33.55,
          "draw": 36.57
        },
        "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": 8303195,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 84.04,
          "no": 8.95,
          "equal": 7.01
        },
        "type_id": 1683,
        "type": {
          "id": 1683,
          "name": "Corners Over/Under 5 Probability",
          "code": "corners-over-under-5-probability",
          "developer_name": "CORNERS_OVER_UNDER_5_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8303208,
        "fixture_id": 18841686,
        "predictions": {
          "yes": 40.69,
          "no": 47.92,
          "equal": 11.4
        },
        "type_id": 1687,
        "type": {
          "id": 1687,
          "name": "Corners Over/Under 9 Probability",
          "code": "corners-over-under-9-probability",
          "developer_name": "CORNERS_OVER_UNDER_9_PROBABILITY",
          "model_type": "prediction",
          "stat_group": null
        }
      },
      {
        "id": 8771213,
        "fixture_id": 18841686,
        "predictions": {
          "bet": "1",
          "bookmaker": "bet365",
          "fair_odd": 3.02,
          "odd": 3.2,
          "stake": 0.58,
          "is_value": false
        },
        "type_id": 33,
        "type": {
          "id": 33,
          "name": "Valuebet",
          "code": "valuebet",
          "developer_name": "VALUEBET",
          "model_type": "prediction",
          "stat_group": null
        }
      }
    ]
  },

Explanation of the data:

Both Teams To Score Probability (type id: 231) = 61,85% probability that both teams will score.

Over/Under 1.5 Probability (type id: 234) = 81,96% probability that there will be more than 1.5 goals.

Over/Under 2.5 Probability (type id: 235) = 61,97% probability that there will be more than 2.5 goals.

Over/Under 3.5 Probability (type id: 236) = 40,27 % probability that there will be more than 3.5 goals.

Fulltime Result Probability (type id: 237) = probability of 39,10% that the home team (Arsenal) will win. 37,00% probability that the away team (Manchester City) will win and a 23,90% probability that the match will end in a draw.

Correct Score Probability (type id: 240) = 10,5% probability of a final score of 1-1.

Away Over/Under 2.5 Probability (type id: 328) = 18,91% probability that the away team will score more than 2.5 goals, 81,09% probability that the away team will score less than 2.5 goals.

Home Over/Under 1.5 Probability (type id: 331) =43,92% probability that the home team will score more than 1.5 goals, 56,08% probability that the home team will score less than 1,5 goals.

Away Over/Under 0.5 Probability (type id: 333) = 73,62% probability that the away team will score more than 0.5 goals (Manchester City), 26,04% probability that the away team will score less than 0.5 goals.

Home Over/Under 0.5 Probability (type id: 334) = 73,96% probability that the away Home will score more than 0.5 goals (Arsenal), 27,90% probability that the home team will score less than 0.5 goals.

PRACTICAL EXAMPLE: USING THE ‘GET PROBABILITIES’ ENDPOINT TO RETRIEVE ALL PREDICTIONS.

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": 1,
         "league_id": 8,
         "type_id": 241,
         "data": {
             "fulltime_result": -1.0822,
             "away_over_under_0_5": -0.5582,
             "away_over_under_1_5": -0.6948,
             "both_teams_to_score": -0.6675,
             "team_to_score_first": -0.8204,
             "home_over_under_0_5": -0.4598,
             "home_over_under_1_5": -0.6919,
             "over_under_1_5": -0.372,
             "over_under_2_5": -0.6853,
             "over_under_3_5": -0.7046,
             "correct_score": -3.2324,
             "ht_ft": -1.9927,
             "fulltime_result_1st_half": -1.1156
         },
         "type": {
             "id": 241,
             "name": "Historical Log Loss",
             "code": "historical-log-loss",
             "developer_name": "HISTORICAL_LOG_LOSS",
             "model_type": "prediction",
             "stat_group": null
         }
       },
       {
         "id": 2,
         "league_id": 8,
         "type_id": 242,
         "data": {
             "fulltime_result": 0.59,
             "away_over_under_0_5": 0.67,
             "away_over_under_1_5": 0.7,
             "both_teams_to_score": 0.59,
             "team_to_score_first": 0.66,
             "home_over_under_0_5": 0.85,
             "home_over_under_1_5": 0.66,
             "over_under_1_5": 0.88,
             "over_under_2_5": 0.64,
             "over_under_3_5": 0.56,
             "correct_score": 0.22,
             "ht_ft": 0.39,
             "fulltime_result_1st_half": 0.46
         },
         "type": {
             "id": 242,
             "name": "Model Hit Ratio",
             "code": "model-hit-ratio",
             "developer_name": "MODEL_HIT_RATIO",
             "model_type": "prediction",
             "stat_group": null
         }
       },
       {
         "id": 3,
         "league_id": 8,
         "type_id": 243,
         "data": {
             "fulltime_result": "high",
             "away_over_under_0_5": "poor",
             "away_over_under_1_5": "high",
             "both_teams_to_score": "poor",
             "team_to_score_first": "high",
             "home_over_under_0_5": "medium",
             "home_over_under_1_5": "high",
             "over_under_1_5": "poor",
             "over_under_2_5": "good",
             "over_under_3_5": "medium",
             "correct_score": "high",
             "ht_ft": "high",
             "fulltime_result_1st_half": "high"
         },
         "type": {
             "id": 243,
             "name": "Model Predictability",
             "code": "model-predictability",
             "developer_name": "MODEL_PREDICTABILITY",
             "model_type": "prediction",
             "stat_group": null
         }
       },
       {
         "id": 4,
         "league_id": 8,
         "type_id": 244,
         "data": {
             "fulltime_result": "up",
             "away_over_under_0_5": "down",
             "away_over_under_1_5": "unchanged",
             "both_teams_to_score": "down",
             "team_to_score_first": "up",
             "home_over_under_0_5": "up",
             "home_over_under_1_5": "up",
             "over_under_1_5": "unchanged",
             "over_under_2_5": "unchanged",
             "over_under_3_5": "unchanged",
             "correct_score": "unchanged",
             "ht_ft": "up",
             "fulltime_result_1st_half": "up"
          },
          "type": {
              "id": 244,
              "name": "Model Predictive Power",
              "code": "model-predictive-power",
              "developer_name": "MODEL_PREDICTIVE_POWER",
              "model_type": "prediction",
              "stat_group": null
          }
        },
        {
          "id": 5,
          "league_id": 8,
          "type_id": 245,
          "data": {
              "fulltime_result": -0.9109,
              "away_over_under_0_5": -0.5779,
              "away_over_under_1_5": -0.5809,
              "both_teams_to_score": -0.6702,
              "team_to_score_first": -0.7528,
              "home_over_under_0_5": -0.4369,
              "home_over_under_1_5": -0.6364,
              "over_under_1_5": -0.386,
              "over_under_2_5": -0.6498,
              "over_under_3_5": -0.6862,
              "correct_score": -2.5799,
              "ht_ft": -1.7559,
              "fulltime_result_1st_half": -1.0088
          },
          "type": {
              "id": 245,
              "name": "Models Log Loss",
              "code": "models-log-loss",
              "developer_name": "MODELS_LOG_LOSS",
              "model_type": "prediction",
              "stat_group": null
            }
          }
       ],

PRACTICAL EXAMPLE: USING THE ‘GET PERFORMANCE BY LEAGUE ID’ ENDPOINT FOR THE PREMIER LEAGUE.

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 Premier League, league ID 8.
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.

Explained: the Value Bet model

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 options for Premier League matches. Whether betting on Manchester United vs Liverpool or the latest North London derby, our model will help you find the best Value Bets, enhancing your Premier League betting strategy.

Predictive lineups

Another cool feature related to predictions is predictive lineups for upcoming Premier 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.

Give your Football App a winning edge with our Predictions API

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!

Start your free trial now

Are you interested in more in-depth knowledge about mathematical models? Learn more.

Learn how to use our Prediction API and find out how to become smarter than the bookmakers. Check it out here and benefit now.

How to get the best probabilities out there?

Our Predictions API offers predictions on various markets like the winner, correct scores, over/under and both teams to score (BTTS) are all available here, produced with our machine learning techniques and models. An overview of all the options to request predictions:

GET Probabilities: Returns all probabilities available within your subscription.
GET Performance by League ID: Returns the performances of our Predictions Model for your requested league ID.
GET Probabilities by Fixture ID: Returns all the predictions available for your requested fixture ID.

Check our docs for more info.

What is the quality of the predictions?

At Sportmonks, we believe that transparency on the predictions and models used results in a better understanding, more sympathy and a greater product. That’s why we've introduced the league predictability. We provide access to our prediction history and performance metrics, allowing you to see the accuracy of our model over time. We are committed to continually monitoring and refining our model to remain accurate and up-to-date. We understand the importance of trust when making informed betting decisions. With our transparent approach, you can be confident in our predictions' accuracy and easily make informed decisions.

What models do you use?

Our prediction API has two key models:

1. Prediction model:
Using advanced machine learning techniques and historical data, our model accurately predicts the outcomes of future football matches, considering various factors such as team form, player injuries, head-to-head records, and more. In addition to that, we also incorporate the player contribution model, which further enhances the accuracy of the predictions. By analysing the performance of individual players and their impact on the team, we can better understand how they'll contribute to the match's final outcome. This model takes into account various metrics such as the player's recent form, their position, and their contribution to the team's overall performance

2. Value bet model:
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.

The models and algorithms are based on five key principles:

1. Timely and substantive:
The prediction API is updated daily with the latest data from the Sportmonks Football Database.

2. Data controlled:
No human intervention is needed. The prediction API runs on statistical analysis results based on the entire historical Sportmonks Football Database.

3. Precise probabilities:
The prediction API offers the most precise probabilities possible, thanks to our mathematical probability distribution models.

4. Predictability performance:
We monitor our prediction API's success rate and quality, but you can also track our predictions’ performance.

5. Machine Learning:
We use cross-validation machine learning models.

Why should I use the Predictions?

Football prediction sites have become increasingly popular in recent years as more and more people turn to online resources for insights into their favourite football teams and matches. Using advanced machine learning techniques and historical data, our Prediction API accurately predicts the outcomes of future football matches, considering various factors such as team form, player injuries, head-to-head records, and more. We also incorporate the player contribution model, which further enhances the accuracy of the predictions. With our Predictions API, you’ll have access to a vast range of markets, including match-winner, double chance, total goals, and beyond. We’re constantly improving our model, adding new features to ensure you have access to the most accurate and up-to-date predictions possible. The Prediction API is a valuable asset to any football prediction website, providing accurate predictions for future matches based on advanced machine-learning techniques and historical data.

Where can I create an account?

You can create an account via our dedicated My Sportmonks platform. After you signed up, you will automatically be assigned to our free plan.

How can I create an API-token?

Once you’ve created your account, you can create your API token via the settings page on My Sportmonks. For security reasons, the API token will only be shown to you once when you create it. Please make sure to write down your API token somewhere safe.

SCORE THE BEST PREMIER LEAGUE PREDICTIONS API

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