This repository contains the code for the API built for the Big Data FC project.
To run the project it is sufficient to:
- Install requirements with
pip3 install -r requirements.txt. - Run the API with
flask run.
The API will listen on port 5000.
Alternatively, the API can also be run by executing ./app.py.
The API allows getting team features, player features, league tables and predictions, hence the needed data is:
- Player features, in a single CSV file in
./data/players.csv. - Team features, in a single CSV file in
./data/teams.csv. - Predictions dataframe, in a pickle directory in
./data/df.pkl. - PySpark Learning model, exported in
./data/model.
Said CSV files can be exported from the dataframes in the main project. A sample of them is already provided in this repository.
Dataframe pickle and learning model can also be exported from the main repository. As a sample, this project currently uses the Linear Regression model.
The following use-cases can informally be defined:
GetPlayer(playerName, year)→{}playerFeaturesGetTeam(teamName, year)→{ {}teamFeatures, {}players }GetTable(league, year)→{}tablePredict(league, year)→{}table
Gets the features of the given player for a specific season.
Example body:
{
"playerName": "L. Messi",
"season": 19
}Example response:
[
{
"attacking_crossing": 88,
"attacking_finishing": 95,
"attacking_heading_accuracy": 70,
"attacking_short_passing": 92,
"club_name": "FC Barcelona",
"defending": 39,
"defending_standing_tackle": 37,
"dribbling": 96,
"league_name": "Spain Primera Division",
"mentality_aggression": 48,
"mentality_penalties": 75,
"movement_acceleration": 91,
"movement_reactions": 95,
"movement_sprint_speed": 84,
"pace": 87,
"passing": 92,
"physic": 66,
"player_positions": "RW, CF, ST",
"power_long_shots": 94,
"power_shot_power": 86,
"power_stamina": 75,
"power_strength": 68,
"season": 19,
"shooting": 92,
"short_name": "L. Messi",
"skill_ball_control": 96,
"skill_dribbling": 97,
"skill_fk_accuracy": 94,
"skill_long_passing": 92
}
]Gets the team information for a specific season, along with its players.
If "compact": true, then a list of player names will be returned instead of the complete players features.
Example body:
{
"teamName": "Roma",
"season": 15,
"compact": true
}Example response:
{
"players": [
"Gervinho",
"K. Manolas",
"L. Digne",
"A. Florenzi",
"M. Salah",
"N. Gyömbér",
"S. Anočič",
"F. Totti",
"Maicon",
"R. Nainggolan",
"V. Torosidis",
"K. Strootman",
"E. Capradossi",
"S. Uçan",
"A. Cole",
"M. Pjanić",
"A. Rüdiger",
"José Machín",
"D. De Rossi",
"S. Keita",
"Leandro Castán",
"E. Džeko",
"Iago Falqué",
"J. Iturbe"
],
"team_features": [
{
"avg(attacking_crossing)": 61.583333333333336,
"avg(attacking_finishing)": 51.833333333333336,
"avg(attacking_heading_accuracy)": 64.16666666666667,
"avg(attacking_short_passing)": 71.91666666666667,
"avg(defending)": 62.04166666666666,
"avg(defending_standing_tackle)": 64.41666666666667,
"avg(dribbling)": 70.04166666666667,
"avg(mentality_aggression)": 71.54166666666667,
"avg(mentality_penalties)": 56.125,
"avg(movement_acceleration)": 73.20833333333333,
"avg(movement_reactions)": 74.5,
"avg(movement_sprint_speed)": 72.875,
"avg(pace)": 73.0,
"avg(passing)": 65.45833333333333,
"avg(physic)": 70.375,
"avg(power_long_shots)": 60.41666666666666,
"avg(power_shot_power)": 69.75,
"avg(power_stamina)": 73.25,
"avg(power_strength)": 68.625,
"avg(shooting)": 57.958333333333336,
"avg(skill_ball_control)": 74.20833333333333,
"avg(skill_dribbling)": 67.33333333333333,
"avg(skill_fk_accuracy)": 52.625,
"avg(skill_long_passing)": 67.375,
"club_name": "Roma",
"club_name_ext": "Roma",
"league": "Italian Serie A",
"place": 3,
"points": 80.0,
"season": 15,
"year": 15
}
]
}Get the final table for a specific league in a specific season.
If "compact": true, then team's features are not returned.
Example body:
{
"league": "Italian Serie A",
"season": 19,
"compact": true
}Example response:
[
{
"club_name_ext": "Juventus",
"place": 1,
"points": 83.0
},
{
"club_name_ext": "Inter",
"place": 2,
"points": 82.0
},
{
"club_name_ext": "Atalanta",
"place": 3,
"points": 78.0
},
{
"club_name_ext": "Lazio",
"place": 4,
"points": 78.0
},
{
"club_name_ext": "Roma",
"place": 5,
"points": 70.0
},
{
"club_name_ext": "Milan",
"place": 6,
"points": 66.0
},
{
"club_name_ext": "Napoli",
"place": 7,
"points": 62.0
},
{
"club_name_ext": "Sassuolo",
"place": 8,
"points": 51.0
},
{
"club_name_ext": "Hellas Verona",
"place": 9,
"points": 49.0
},
{
"club_name_ext": "Fiorentina",
"place": 10,
"points": 49.0
},
{
"club_name_ext": "Parma",
"place": 11,
"points": 49.0
},
{
"club_name_ext": "Bologna",
"place": 12,
"points": 47.0
},
{
"club_name_ext": "Udinese",
"place": 13,
"points": 45.0
},
{
"club_name_ext": "Cagliari",
"place": 14,
"points": 45.0
},
{
"club_name_ext": "Sampdoria",
"place": 15,
"points": 42.0
},
{
"club_name_ext": "Torino",
"place": 16,
"points": 40.0
},
{
"club_name_ext": "Genoa",
"place": 17,
"points": 39.0
},
{
"club_name_ext": "Lecce",
"place": 18,
"points": 35.0
},
{
"club_name_ext": "Brescia",
"place": 19,
"points": 25.0
},
{
"club_name_ext": "SPAL",
"place": 20,
"points": 20.0
}
]Get predictions for a specific league and season.
Example body:
{
"league": "Italian Serie A",
"season": 20
}Example response:
[
{
"club_name": "Juventus",
"league": "Italian Serie A",
"points": 78.0,
"prediction": 73.41170178534836,
"season": "20"
},
{
"club_name": "Inter",
"league": "Italian Serie A",
"points": 91.0,
"prediction": 72.40765187271296,
"season": "20"
},
{
"club_name": "Napoli",
"league": "Italian Serie A",
"points": 77.0,
"prediction": 69.16208198417701,
"season": "20"
},
{
"club_name": "Lazio",
"league": "Italian Serie A",
"points": 68.0,
"prediction": 64.21875497063937,
"season": "20"
},
{
"club_name": "Roma",
"league": "Italian Serie A",
"points": 62.0,
"prediction": 63.53082085023385,
"season": "20"
},
{
"club_name": "Milan",
"league": "Italian Serie A",
"points": 79.0,
"prediction": 62.814598579220586,
"season": "20"
},
{
"club_name": "Atalanta",
"league": "Italian Serie A",
"points": 78.0,
"prediction": 57.78263510039619,
"season": "20"
},
{
"club_name": "Fiorentina",
"league": "Italian Serie A",
"points": 40.0,
"prediction": 53.58237913622716,
"season": "20"
},
{
"club_name": "Sassuolo",
"league": "Italian Serie A",
"points": 62.0,
"prediction": 50.1505880245785,
"season": "20"
},
{
"club_name": "Torino",
"league": "Italian Serie A",
"points": 37.0,
"prediction": 50.07513215235619,
"season": "20"
},
{
"club_name": "Udinese",
"league": "Italian Serie A",
"points": 40.0,
"prediction": 49.337986323723015,
"season": "20"
},
{
"club_name": "Parma",
"league": "Italian Serie A",
"points": 20.0,
"prediction": 48.53700882154524,
"season": "20"
},
{
"club_name": "Genoa",
"league": "Italian Serie A",
"points": 42.0,
"prediction": 48.210985155274585,
"season": "20"
},
{
"club_name": "Sampdoria",
"league": "Italian Serie A",
"points": 52.0,
"prediction": 48.13371543997002,
"season": "20"
},
{
"club_name": "Bologna",
"league": "Italian Serie A",
"points": 41.0,
"prediction": 45.497475904203526,
"season": "20"
},
{
"club_name": "Hellas Verona",
"league": "Italian Serie A",
"points": 45.0,
"prediction": 44.94413284124002,
"season": "20"
},
{
"club_name": "Cagliari",
"league": "Italian Serie A",
"points": 37.0,
"prediction": 44.81365706218898,
"season": "20"
},
{
"club_name": "Benevento",
"league": "Italian Serie A",
"points": 33.0,
"prediction": 34.14765512409974,
"season": "20"
},
{
"club_name": "Crotone",
"league": "Italian Serie A",
"points": 23.0,
"prediction": 30.767860847476044,
"season": "20"
},
{
"club_name": "Spezia",
"league": "Italian Serie A",
"points": 39.0,
"prediction": 28.95803552515497,
"season": "20"
}
]The Postman collection can be used to quickly import sample requests to test the API.