This project is targeting on how to predict European league football match results based on the history data of teams,matches,bet-odds by using machine learning algorithms
We've been really involved in this project, starting from understanding the basis of "stream" application, to those techniques to mining data.
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Downlaod the data.zip file from (https://drive.google.com/open?id=0BwlNuYlbCa_6V1k5NGVEUUwzZWs)
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The zip file contains a main directory called "data"
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Export it in the directory ./ScorePredictionRep/
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Final folder structure:
├── data │ ├── db │ │ └── database.sqlite │ │ └── src
Code for crawling the data , pre-process and algorithms
Crawl:
One of the most important part for this project is collecting data from different websites and formalize
Code for crawling the website information,Collect data for teams, players,leageues,matches and bet-odds
enetscores
crawling from http://football-data.mx-api.enetscores.com
football_data
crawling from http://www.odds.football-data.co.uk
sofifa
crawling from http://sofifa.com
Domain:
Code for Dedicated methods for different types of data
Exception:
Code for Customizing the exceptions for this application
MachineLearning:
Another most important part for this project is :
Code for using the different algorithm for calculating the predictions
experiments
input_train
my_possion
my_sklearn
prediction_accuracy
Code for User interactive interface, implements easy instruction for customized the input parameters
Test code for without GUI part ,calling /src/application/MachineLearning/experiments to test different combinations of inputs and algorithms
Code for all the common functions
Entry for the whole application.
./ScorePredictionRep/src/main.py --no-crawl --no-index -v
--no-crawl : for not crawling the website and update the SQLLite
--no-index : for indexing the Italy league
-v : for the debug
python ./ScorePredictionRep/src/main.py
> Initialization DB
> Indexing...
...finished in 181.01 seconds
> Init crawling: started
****************************************************************************************************
*********************************** ScorePrediction application ***********************************
****************************************************************************************************
Browse the application to discover different data
- 1 : Players
- 2 : Matches
- 3 : Leagues
- 4 : Countries
- 5 : Teams
- 6 : Crawling
- 7 : Prediction
- 8 : Bet odds
Browse the application to discover different data
- 1 : Players
- 2 : Matches
- 3 : Leagues
- 4 : Countries
- 5 : Teams
- 6 : Crawling
- 7 : Prediction
- 8 : Bet odds
Select an item: 1
[INFO: Opening --> Players]
Players menu:
- 1 : Find by Name
- 2 : Find by Team
- gb : Go back
Select an item: 1
[INFO: Machine Learning Framework --> 1]
[INFO: Machine Learning Algorithm --> 2]
[INFO: Machine Learning Input --> 1]
[INFO: Machine Learning Input representation --> 2]
[INFO: Machine Learning Training Size --> 20]
[INFO: Setting --> predictor]
[ANSWER of List --> Frameworks (String)]
1) Sklearn
2) my_poisson
[ANSWER of List --> Algorithms (String)]
1) SVM
2) KNN
3) RandomForest
[ANSWER of List --> Machine learning input (Id)]
1) team form
Representations: [1, 2, 3, 4]
2) team home away form
Representations: [1, 2, 3, 4]
3) match statistics
Representations: []
4) Kekko input
Representations: []
5) Poisson inpunt
Representations: []
[INSTRUCTION: list of parameter --> framework(str) algorithm(str) input(int) representation(int) training(int)]
[INSTRUCTION: Use . for --> default]
Type your representation: Sklearn KNN 1 2 20
****************************************************************************************************
Predictions Menu:
- 1 : Set Current Predictor
- 2 : Show Current Predictor
- 3 : Check setting current predictor
- 4 : Predict matches by date
- gb : Go back
Select an item: 2
[INFO: Show --> Current Predictor]
[INFO: Machine Learning Framework --> Sklearn]
[INFO: Machine Learning Algorithm --> KNN]
[INFO: Machine Learning Input --> 1]
[INFO: Machine Learning Input representation --> 2]
[INFO: Machine Learning Training Size --> 20]
****************************************************************************************************
Predictions Menu:
- 1 : Set Current Predictor
- 2 : Show Current Predictor
- 3 : Check setting current predictor
- 4 : Predict matches by date
- gb : Go back
Select an item: 4
[INFO: Predict matches by --> date]
Insert a date (YYYY-MM-DD) or an integer (the day passed from today --> 0 is today): -4
[ANSWER of Prediction by date --> 2017-03-18]
1) SD Eibar vs RCD Espanyol
1 (57.14%)
2) Aberdeen vs Heart of Midlothian|Hearts
1 (100.0%)
3) West Bromwich Albion vs Arsenal
1 (58.06%)
4) VfL Wolfsburg vs SV Darmstadt 98
1 (44.44%)
5) 1. FC Köln vs Hertha BSC Berlin
1 (66.67%)
6) FC Augsburg vs SC Freiburg
1 (66.67%)
7) SV Werder Bremen vs RB Leipzig
2 (55.56%)
8) TSG 1899 Hoffenheim vs Bayer 04 Leverkusen
1 (66.67%)
9) Piast Gliwice vs Arka Gdynia
2 (66.67%)
10) Crystal Palace vs Watford
2 (45.16%)
11) Everton vs Hull City
1 (58.06%)
12) Stoke City vs Chelsea
1 (38.71%)
13) Sunderland vs Burnley
2 (45.16%)
14) West Ham United vs Leicester City
1 (58.06%)
15) Inverness Caledonian Thistle vs Ross County FC
0 (40.0%)
16) Kilmarnock vs Partick Thistle F.C.
1 (80.0%)
17) Motherwell vs St. Johnstone FC|St Johnstone
1 (60.0%)
18) Rangers vs Hamilton Academical FC
1 (60.0%)
19) Athletic Club de Bilbao|Athletic Bilbao vs Real Madrid CF
2 (47.62%)
20) FC Nantes vs OGC Nice
2 (71.43%)
21) CF Os Belenenses vs SC Braga|Sporting de Braga
1 (60.0%)
22) Moreirense FC vs Tondela
0 (33.33%)
23) FC Luzern vs FC Sion
1 (46.15%)
24) Torino vs Inter|Internazionale
2 (41.94%)
25) Zagłębie Lubin|Zaglebie Lubin vs Ruch Chorzów|Ruch Chorzow
1 (58.06%)
26) Alaves|Deportivo Alaves vs Real Sociedad
2 (47.62%)
27) Bournemouth|AFC Bournemouth vs Swansea City
2 (45.16%)
28) Eintracht Frankfurt vs Hamburger SV
1 (66.67%)
29) FC Groningen vs Willem II
1 (48.39%)
30) Sporting CP vs CD Nacional|Nacional da Madeira
1 (66.67%)
31) N.E.C.|NEC vs FC Utrecht
1 (48.39%)
32) PSV|PSV Eindhoven vs Vitesse
1 (74.19%)
33) AS Nancy-Lorraine vs FC Lorient
1 (66.67%)
34) Girondins de Bordeaux vs Montpellier Hérault SC|Montpellier HSC
0 (38.1%)
35) Toulouse FC vs Stade Rennais FC
1 (38.1%)
36) Angers SCO vs En Avant de Guingamp|En Avant Guingamp
0 (38.1%)
37) FC Basel vs Grasshopper Club Zürich
1 (84.62%)
38) Pogoń Szczecin|Pogon Szczecin vs Jagiellonia Białystok|Jagiellonia Bialystok
1 (58.06%)
39) Real Betis Balompié vs CA Osasuna
1 (52.38%)
40) Milan|AC Milan vs Genoa
1 (61.29%)
41) Sparta Rotterdam vs Heracles Almelo
1 (41.94%)
42) FC Paços de Ferreira vs SL Benfica
2 (60.0%)
****************************************************************************************************
###Test for algorithm
####Input
Team Form: combination of points gathered by the teams
Team Home Away Form: combination of points gathered by the teams, considering matches played at home and away.
Match Statistics: combination of previous match statistics performed by teams.
Kekko input: features an human uses to gather information before placing a bet.
Poisson input: home strength and away strength (average goal a team will score)
The representations of the Team Forms are:
- Representation 1 (r1): This represents the numeric values of the team forms, normalized to interval [0,3].
- Representation 2 (r2): This represents the discretized value of the team forms.
- Representation 3 (r3): This represents the subtracted value between the home team form and away team form. This subtracted value is normalized to the interval [-3,3]; a negative value means away team superiority and a positive value means home team superiority while zero means an equal advantage.
- Representation 4 (r4): This represents the discretized values of r3. This representation will be discretized by equal frequency into three bins.
Number of past stages to consider in training: depending on the league, every stage has different number of matches (e.g., Italy has 10 matches in a stage, while Germany just 9 matches).
Windows tested: [ 9, 11, 19, 35, 71, 105, 141]
- Flat Bet: for all predictions, bet 1€
- Smart Bet: for all prediction bet if and only if 𝑝>1/𝑥 (probability > 1/bet-odd)
- Most accurate teams bet: bet only on that teams that seems to be most accurate in predictions
- Combination of 2 and 3
Simone Caldaro caldaro.1324152@studenti.uniroma1.it
Leonardo Martini martini.1722989@studenti.uniroma1.it
Na Zhu zhu.1706409@studenti.uniroma1.it
Aris Anagnostopoulos
Ioannis Chatzigiannakis
A Comparison of Methods for Predicting Football Matches, David B. Ekefre
Predicting Soccer Match Results in the English Premier League, Ben Ulmer, Matthew Fernandez
├── data
│ ├── db
│ │ └── database.sqlite
│ ├── experiments
│ ├── log
│ │ ├── crawl_log.txt
│ │ └── logging.txt
│ │
└── src
├── __init__.py
├── __pycache__
├── application
│ ├── Crawl
│ │ ├── Crawl.py
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ ├── enetscores
│ │ │ ├── CrawlMatch.py
│ │ │ ├── Crawler.py
│ │ │ ├── CrawlerIncidents.py
│ │ │ ├── CrawlerLeague.py
│ │ │ ├── CrawlerLineup.py
│ │ │ ├── CrawlerTeam.py
│ │ │ ├── __init__.py
│ │ │ └── __pycache__
│ │ ├── football_data
│ │ │ ├── Crawler.py
│ │ │ ├── CrawlerEvent.py
│ │ │ ├── CrawlerLeague.py
│ │ │ ├── CrawlerMatch.py
│ │ │ ├── __init__.py
│ │ │ └── __pycache__
│ │ └── sofifa
│ │ ├── Crawler.py
│ │ ├── CrawlerLeague.py
│ │ ├── CrawlerPlayer.py
│ │ ├── CrawlerTeam.py
│ │ ├── __init__.py
│ │ └── __pycache__
│ ├── Domain
│ │ ├── Bet_Event.py
│ │ ├── Country.py
│ │ ├── Event.py
│ │ ├── League.py
│ │ ├── Match.py
│ │ ├── MatchEvent.py
│ │ ├── Player.py
│ │ ├── Player_Attributes.py
│ │ ├── Shot.py
│ │ ├── Team.py
│ │ ├── Team_Attributes.py
│ │ ├── __init__.py
│ │ └── __pycache__
│ ├── Exception
│ │ ├── CrawlException.py
│ │ ├── MLException.py
│ │ ├── TeamException.py
│ │ ├── __init__.py
│ │ └── __pycache__
│ ├── MachineLearning
│ │ ├── MachineLearningAlgorithm.py
│ │ ├── MachineLearningInput.py
│ │ ├── Plot_graph.py
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ ├── experiment
│ │ │ ├── __init__.py
│ │ │ ├── __pycache__
│ │ │ ├── experiment.py
│ │ │ ├── experiment_1.py
│ │ │ ├── experiment_2.py
│ │ │ ├── experiment_3.py
│ │ │ ├── experiment_4.py
│ │ │ └── experiment_plot.py
│ │ ├── input_train
│ │ │ ├── __init__.py
│ │ │ ├── __pycache__
│ │ │ ├── kekko_input.py
│ │ │ ├── match_statistics.py
│ │ │ ├── poisson.py
│ │ │ ├── team_form.py
│ │ │ └── team_home_away_form.py
│ │ ├── my_poisson
│ │ │ ├── __init__.py
│ │ │ ├── __pycache__
│ │ │ └── poisson.py
│ │ ├── my_sklearn
│ │ │ ├── Sklearn.py
│ │ │ ├── __init__.py
│ │ │ └── __pycache__
│ │ └── prediction_accuracy
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ └── prediction_accuracy.py
│ ├── __init__.py
│ └── __pycache__
├── gui
│ ├── BetOddsGui.py
│ ├── CountryGui.py
│ ├── CrawlGui.py
│ ├── LeaguesGui.py
│ ├── MainGui.py
│ ├── MatchGui.py
│ ├── PlayerGui.py
│ ├── PredictionGui.py
│ ├── TeamGui.py
│ ├── __init__.py
│ └── __pycache__
├── main.py
├── test
│ ├── Test.py
│ └── __init__.py
└── util
├── Cache.py
├── GuiUtil.py
├── MLUtil.py
├── SQLLite.ini
├── SQLLite.py
├── __init__.py
├── __pycache__
└── util.py
User interactive interface
Crawl the website information
Collect data for teams, players,leageues,matches and bet-odds
Use the different algorithm for calculating the predictions
Show what the library does as concisely as possible, developers should be able to figure out how your project solves their problem by looking at the code example. Make sure the API you are showing off is obvious, and that your code is short and concise.